ASAM CLASS OF 2014 ANNUAL REPORT

ASAM CLASS OF 2014 ANNUAL REPORT

ANDERON STUDENT ASSET MANAGEMENT

TABLE OF CONTENTS

II. ANNUAL STAKEHOLDER LETTER 4

II. FUND MANAGEMENT OVERVIEW – ASAM CLASS OF 2013 6

PURPOSE OF FUND 6

INVESTMENT PHILOSOPHY 6

III. ASAM INVESTMENT STRATEGIES 7

OVERALL PERFORMANCE REVIEW 7

THE F-SCORE STRATEGY 10

THE TACTICAL ASSET ALLOCATION (TAA) STRATEGY 20

THE EARNINGS ANNOUNCEMENT RETURN (EAR) STRATEGY 37

FUNDAMENTAL INDEX STRATEGY ...... 44

IV. ASAM FELLOWSHIP HIGHLIGHTS 60

DISTINGUISHED SPEAKER SERIES 60

FIRM VISITS 61

BUFFET TRIP 62

ACCOMPLISHMENTS AND HONORS 64

RECRUITING ...... 64

UCLA ASAM CLASS OF 2014 65

UCLA Anderson Student Asset Management Annual Report | 3

I. ANNUAL STAKEHOLDER LETTER

ThisThis was was without without a doubta doubt a bannera banner year year for for the the Anderson Anderson Student Student Asset Asset Management Management Fellows Fellows (“ASAM”) (“ASAM”).. The The ASAM ASAM Class Classof 2014 of 2014 has hasseen seen strong strong performance performance in inall all three three traded traded strategies, strategies, hostedhosted a numbernumber ofof preeminent preeminent guest guest speakers, speakers, and andvisited visited many many of ofthe the largest largest and and most most respected respected assetasset managementmanagement firms in thethe world.world. We We travelled travelled across across the the country to countryNew York, to New San York, Francisco San Francisco and even and made even a trekmade to a visit trek theto visit Oracle the ofOracle Omaha, of Omaha, Warren Warren Buffett. Buffett Moreover,. Moreover, we launched we a new launchedinvestment a new strategy, investment the Fundamental strategy, the FundamentalIndex, based Index,on an basedacademic on an paper academic by UCLA paper A bydjunct UCLA Professors, Adjunct Professors, long-time ASAM supporters and co-founders of Research Affiliates, Dr. Jason Hsu and Mr. Robert Arnott. long-time ASAM supporters and co-founders of Research Affiliates, .Dr Jason Hsu and Mr. Robert Arnott. This year ASAM Fellows stood on the shoulders of the 2013 class. After a robust knowledge transfer in the spring of 2013, This year ASAM Fellows stood on the shoulders of the 2013 class. After a robust knowledge transfer in the spring of ASAM was able to maintain strong momentum and ongoing management of its investments in three strategies. This 2013,resulted ASAM in a was strong able continuation to maintain strongof performance momentum, which and weongoing hope managementwill be maintained of its investmentsin future classes in three to develop strategies a consistent. Thistrack resulted record in of a yearstrong-over continuation-year returns of performance, for our Tactical which Asset we hopeAllocation will be (TAA), maintained Fundamental in future Indexclasses and to developF-Score astrategies. consistentThis year, track ASAM record boasts of year-over-year one year combined returns returns for our from Tactical May Asset 1, 201 Allocation3 to April 30, (TAA), 2014 Fundamental of 14.94%, increasing Index and theF-Score size of the strategiesassets under. This managementyear, ASAM boasts by $87K one from year $582K combined to $669K returns. Of from further May note, 1, 2013 all tothree April actively 30, 2014 traded of 14 strategies.94%, increasing outperformed thetheir size ben of thechmarks assets for under this managementperiod. Additional by $87K analysis from $582Kof the tomarkets $669K and. Of eachfurther strategy’s note, all threereturns actively are detailed traded in the pages that follow. strategies outperformed their benchmarks for this period. Additional analysis of the markets and each strategy’s returns areIn detailedlight of thein the strong pages market that follow returns. during the year, and the continued low interest rate environment, we made a strategic mid-year decision to re-allocate funds that were set aside for a fourth strategy, Earnings Announcement Return (“EAR”), to Inbe light evenly of the allocated strong market among returns the three during traded the year, strategies and the and continued out of alow fixed interest income rate indexed environment, ETF. We we made athis decision to strategicimprove mid-year the overall decision returns to ofre-allocate ASAM while funds the that EAR were strategy set aside was for continually a fourth strategy, modified Earnings and back Announcement-tested over Returnthe year. We (“EAR”),also made to be this evenly change allocated with amongthe explicit the three promise traded that strategies once aand fourth out ofstrategy a fixed isincome fully indexedback-tested ETF. Weand made can bethis properly decisionimplemented, to improve the theoverall overall fund returns will be of rebalanced ASAM while again the evenlyEAR strategy across wasthe fourcontinually strategies. modified This strategicand back-tested decision over resulted in ASAM funds being fully invested and taking advantage of strong equities markets of 2013. the year. We also made this change with the explicit promise that once a fourth strategy is fully back-tested and can be properlyIn addition implemented, to ASAM ’sthe success overall fundin implementing will be rebalanced and trading again evenlyupon theacross strategies, the four westrategies made .gre Thisat strategicstrides in decision increasing our resultedaccess inand ASAM exposure funds tobeing the fullyinvestment invested management and taking advantage industry, of enhanced strong equities the reputation markets ofof 2013ASAM. within the school, and served as an ambassador of UCLA Anderson, and the broader UCLA Community on a number of occasions. This year we Inc additiono-sponsored to ASAM’s a number success of high in implementing profile speaking and events trading with upon investment the strategies, legends we made Larry greatFink, stridesDavid Booth in increasing and Mary our Erdoes. accessThese and events exposure are further to the investmentdescribed inmanagement a new section industry, of this enhanced year’s annual the reputation report regarding of ASAM ourwithin speakers the school, and andfirm visits. servedASAM as Fellowsan ambassador also garnered of UCLA a Anderson,number of and accolades the broader within UCLA the Community university onand a numberin inter -ofcollegiate occasions competitions.. This year These weawards co-sponsored and honors a number include of a high range profile of accomplishments speaking events fromwith investmentwinning finance legends competitions, Larry Fink, toDavid delivering Booth andTED MaryTalks. These events are also described in a new section of the annual report on Fellows’ achievements. Erdoes. These events are further described in a new section of this year’s annual report regarding our speakers and firm visits2014. ASAM ASAM Fellows Fellows also also garnered received a anumber number of of accolades coveted withininternships the university and full timeand in job inter-collegiate offers in the financialcompetitions sector. including Theseoffers awards at Western and honors Asset include Management a range ofCompany, accomplishments American from International winning finance Group, competitions, and the financial to delivering consulting TED division of TalksErnst. These & Young, events among are also others. described Details in a newon thesesection post of the ASAM annual happenings, report on Fellows’ along achievementswith updates. on other recent alumni achievements are further detailed herein. 2014 ASAM Fellows also received a number of coveted internships and full time job offers in the financial sector It was also an incredible year for recruiting the next generation of ASAM fellows. We would like to take this opportunity to including offers at Western Asset Management Company, American International Group, and the financial consulting welcome and introduce the 2015 ASAM Fellows, whose ranks include multiple PhDs, CFAs, a CPA and a JD. We believe divisionthat our of strong Ernst & recruitment Young, among efforts others will. furtherDetails addon these to the post longevity ASAM and happenings, prosperity along of this with Fellowship. updates on other recent alumni achievements are further detailed herein. We would like to express our gratitude to all who helped make ASAM such a rewarding experience. In particular, we would It likewas toalso thank an incredible our faculty year advisor for recruiting Professor the nextRobert generation Geske for of ASAMhis guidance fellows and. We expertise; would like theto take UCLA this Anderson opportunity School of toManagement welcome and for introduce providing the us 2015 with ASAM this unique Fellows, opportunity; whose ranks the include Fink Center multiple for PhDs, Finance CFAs, and a Investments CPA and a JD for. We its sponsorship believeof ASAM that ourand strong collaboration recruitment with efforts respect will to further recruiting add andto the branding longevity efforts; and prosperity and our offellow this FellowshipAnderson. students for their support. We would also like to thank the guest speakers and company visit hosts who contributed their time this year.

3 4 | UCLA Anderson Student Asset Management Annual Report We would like to express our gratitude to all who helped make ASAM such a rewarding experience. In particular, we would like to thank our faculty advisor Professor Robert Geske for his guidance and expertise; the UCLA Anderson School of Management for providing us with this unique opportunity; the Fink Center for Finance and Investments for its sponsorship of ASAM and collaboration with respect to recruiting and branding efforts; and our fellow Anderson students for their support. We would also like to thank the guest speakers and company visit hosts who contributed their time this year.

In closing, I can personally say that it has been an honor to work alongside this class of ASAM Fellows. They are the most talented, diligent and creative group of individuals I have ever had the pleasure of working with. I feel strongly about the future of this organization based on all that was accomplished this year and the strong class we have recruited to serve as stewards of ASAM. We encourage future ASAM Fellows to not only build upon the prior years’ strong performance to grow the program, but also to build new relationships and seek funds to grow the size and reputation of the program, and to support the Fink Center’s mission to foster academic research in finance.

It is our sincerest hope that you enjoy this report as much as we have enjoyed this life changing experience.

Very Truly Yours,

Joseph F. Duronio, Esq. 2014 ASAM President, On behalf of all members of the ASAM Class of 2014

UCLA Anderson Student Asset Management Annual Report | 5

II. FUND MANAGEMENT OVERVIEW – ASAM CLASS OF 2014 PURPOSE OF FUND

Anderson Student Asset Management (ASAM) is a student run investment fund that aims to:

1. Enhance the educational and professional development-­‐ of the student fund managers through experiential learning in strategy development and fund management 2. Preserve capital for future ASAM fellows while providing favorable risk-­‐ adjusted returns

When possible it is intended that a portion of the Fund’s long term profits will be-­‐ donated to the UCLA Anderson School for student scholarships and research in finance. -­‐ INVESTMENT PHILOSOPHY

ASAM’s objective is to preserve capital for future students while pursuing favorable risk adjusted returns. The student managers adhere to stated investment policies established by the UCLA Anderson School and the ASAM faculty advisor. -­‐ -­‐ The Fund seeks to achieve its objectives through a diversified portfolio of securities that meet the fundamental and technical specifications adopted and developed by the managers. The managers study academic and professional papers which demonstrate that security prices sometimes violate sensible risk/return boundaries. Each of the four current portfolios seeks a practical method for student implementation to exploit these opportunities through large sample quantitative techniques. Student fund managers leverage research and analytical capabilities within the ASAM class, the Anderson finance faculty, other academic resources, and investment management professionals. -­‐

The student managers, along with the faculty advisor, back-test the strategies in and out of sample to determine an optimal mix of equity, fixed income, commodity, real estate, and cash investments. Subject to minimum liquidity requirements, the Fund may hold the stock or ETF of securities traded on U.S. exchanges. To minimize idiosyncratic risk from holding large positions in individual securities, the managers establish maximum position limits. Furthermore, the fund is also diversified to mitigate risks associated with sector, industry or asset class concentration. Each year, the student managers, in consultation with the advisor, select benchmarks for the Funds that reflect the asset allocation decision. -­‐ -­‐

New York Stock Exchange visit, September 2013

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III. ASAM INVESTMENT STRATEGIES OVERALL PERFORMANCE REVIEW

For the one year period ending April 30, 2014, the Total ASAM Portfolio returned 14.9%. During the same time period, the S&P 500 Index returned 20.4%.

ASAM benchmarked the F-Score strategy to the Russell 2000 Value Index. Since the F-Score strategy is a small-cap value strategy, we believed the Russell 2000 Value Index, which measures the small-cap segment of the US equity universe with higher book-to-market ratios, would be an appropriate benchmark. TAA’s benchmark remained as an equally weighted portfolio comprised of the strategy’s underlying indices. Since the strategy attempts to maximize its objective function by finding optimal portfolio weights, we believe an equally weighted portfolio reflects no value added from portfolio management. ASAM chose to benchmark the Fundamental Index strategy to the Russell Megacap Index, which is a market- value weighted index comprised of the 50 largest US stocks. This was the logical choice considering ASAM’s Fundamental Index strategy was constructed using the top 50 fundamentally weighted US stocks.

When the 2014 ASAM fellows assumed portfolio management duties on May 1, 2013, only two strategies, F-Score and Tactical Asset Allocation, were actively trading. The funds allocated to the Earnings Announcement Release and Fundamental Index strategies were invested in the Barclays 1-3 Year Treasury Bond Fund while those strategies were in the process of being back-tested. At the beginning of the 2014 ASAM fiscal year, 42.3% of ASAM’s assets were invested in short-duration bonds. On October 29, 2013, ASAM successfully launched a new Fundamental Index strategy, allocating approximately $100k of EAR’s assets to the Fundamental Index strategy and the remaining $46k towards the Tactical Asset Allocation strategy. As of October 29, 2013, ASAM’s assets were 100% invested in actively trading strategies.

The ASAM 2014 fellows are pleased to announce that ASAM’s assets have appreciated from $582.5k to $669.5k from May 1, 2013 to April 30, 2014, representing a gain of $87.0k. Additionally, each one of ASAM’s actively traded strategies have outperformed their respective benchmarks with competitive risk metrics. Performances and risk metrics per strategy alongside their corresponding benchmark returns are summarized in the following tables below:

ASAM Asset Values

Fundamental Total Date TAA EAR F-Score Index Portfolio May 1, 2013 $160,291 $145,932 $175,902 $100,402 $582,527 April 30, 2014 $217,035 $0 $236,113 $216,399 $669,547

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1 1 Performance for the Total Portfolio, TAA Portfolio, and the F-Score Portoflio reported from May 1, 2013 to April 30, 2014. Since inception date for the Fundamental Performance Index for the Portfolio Total Portfolio, is as of October TAA Portfolio, 29, 2013. and the F-Score Portoflio reported from May 1, 2013 to April 30, 2014. Since inception date for the 2Fundamental Index Portfolio is as of October 29, 2013. 2 The custom benchmark for the TAA strategy is comprised of 20% MSCI US REIT Index, 20% CRSP US Total Market Index, 20% FTSE All World ex- US The Index, custom 20% ben Barclayschmark US for Agg the FloatTAA strategy Adjusted is Index, comprised and 20%of 20% DB MSCI Commodity US REIT Index. Index, Prior 20% to CRSPDecember US Total 24, 2013, Market the Index, benchmark 20% FTSE was comprise All Worldd ofex - 20%US Index, MSCI 20% US REITBarclays Index, US 20% Agg CRSPFloat AdjustedUS Total Index,Market and Index, 20% 20% DB CommodityFTSE Emerging Index. Markets Prior toIndex, December 20% Barclays 24, 2013, US the Agg benchmark Float Adjusted was comprise Index, dand of 20% DBMSCI Commodity US REIT Index,Index. 20% CRSP US Total Market Index, 20% FTSE Emerging Markets Index, 20% Barclays US Agg Float Adjusted Index, and 20% DB Commodity Index.

ASAM Performance, May 1, 2013 to April 30, 2014

Value Portfolio (April 30, Total Return1 2014) Total Portfolio $669,547 14.94% S&P 500 Index 20.44% TAA $217,035 5.66% TAA Custom Benchmark2 4.17% F-Score $236,113 34.49% Russell 2000V Index 19.61% Fundamental Index $216,399 7.60% Russell Megacap Index 7.28%

The above figures are inclusive of non-equity (i.e. cash and short duration bonds) held over the course of the year. Net, these non-equity balances reduced returns for ASAM on the whole. From May 1, 2013 through October 28, 2013, the S&P 500 Index returned 11.5%. During the same time period, the Earnings Announcement Release and Fundamental Index strategies were fully invested in short duration bonds and experienced low returns compared to the S&P 500 Index.

ASAM Monthly Returns, May 1, 2013 to April 30, 2014 5.0% 4.3% 5.0% TAATAA TAA TAA Custom Custom Benchmark BM 4.3%3.9% 3.2% 3.7%3.3% 3.9% 3.2% 3.0% 3.0% 3.0%3.0% 2.2% 1.8% 2.2% 1.8% 1.8% 1.3% 1.3% 1.8% 1.3% 1.3% 0.5% 0.5% 0.4% 1.0%1.0% 0.2% 0.5% 0.5% 0.1% 0.4% 0.2% 0.1%

(1.0%)(1.0%) (1.0%) (1.0%) (1.4%) (1.4%) (1.4%) (1.8%) (1.4%) (1.9%)(1.9%) (1.8%) (3.0%)(3.0%) (2.2%)(2.2%) (2.8%)(2.8%) (2.8%)(2.8%) (2.9%) (2.9%)

(5.0%)(5.0%) 1 PerformanceApr-13May-­‐13 for May-13the Total Jun-­‐13 Portfolio,Jun-13 Jul-­‐13 TAA Portfolio, Jul-13Aug-­‐13 and Aug-13the Sep-­‐13 F-Score Portoflio Sep-13Oct-­‐13 reported Nov-­‐13 Oct-13 from MayDec-­‐13 Nov-13 1, 2013 Jan-­‐14 toDec-13 April 30, Feb-­‐14 2014.Jan-14 Since Mar-­‐14 inceptiFeb-14on Apr-­‐14 date forMar-14 the Fundamental Index Portfolio is as of October 29, 2013. 2 The custom benchmark for the TAA strategy is comprised of 20% MSCI US REIT Index, 20% CRSP US Total Market Index, 20% FTSE All World ex- US Index, 20% Barclays US Agg Float Adjusted Index, and 20% DB Commodity Index. Prior to December 24, 2013, the benchmark was comprised of 20% MSCI US REIT Index, 20% CRSP US Total Market Index, 20% FTSE Emerging Markets Index, 20% Barclays US Agg Float Adjusted Index, and 20% DB Commodity Index.

1 Performance for Total the Portfolio, TAA Portfolio, and the F-­‐Score Portoflio reported from May 1, 2013 to April 30, 2014. Since inception date for the Fundamental Index P ortfolio is as of October 29, 2013. 2 The custom benchmark for the TAA strategy is comprised of 20% MSCI US REIT Index, 20% CRSP US Total Market Index, All 20% FTSE World ex-­‐US Index, 20% Barclays US Agg Float Adjusted Index, and 20% DB Commodity Index. Prior to December 24, 2013, the benchmark 20% was comprised of MSCI US REIT Index, 20% CRSP US Total Market Index, 20% FTSE Emerging Markets Index, 20% Barclays US Agg Float Adjusted Index, and 20% DB Commodity Index.

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9.0% FSCORE Russell 2000V 9.0% 7.0% 6.4% 6.4% 6.4%FSCORE Russell 6.6% 2000V 6.6% 6.4% 6.4% 5.8%6.4% 6.6% 6.6% 5.1%7.0% 5.8% 5.0% 5.1% 4.3% 4.6% 3.9% 3.8% 4.6% 3.3%4.3% 5.0% 3.0% 2.9% 3.9% 3.8% 3.0% 3.0% 2.9% 3.3% 1.9% 3.0% 1.9% 1.2% 1.0% 1.2% 1.0% (1.0%) (0.4%) (0.7%) (1.0%) (1.2%) (3.0%) (0.4%) (0.7%) (2.6%) (3.1%) (1.2%) (3.0%) (3.9%) (5.0%) (4.4%) (2.6%) (3.1%) (5.0%) (3.9%) (7.0%) (4.4%) (6.1%) Apr-13 May-13 Jun-13 Jul-13 Aug-13 Sep-13 Oct-13 Nov-13 Dec-13 Jan-14 Feb-14 Mar-14 (7.0%) (6.1%) May-­‐13 Jun-­‐13 Jul-­‐13 Aug-­‐13 Sep-­‐13 Oct-­‐13 Nov-­‐13 Dec-­‐13 Jan-­‐14 Feb-­‐14 Mar-­‐14 Apr-­‐14

5.0% Fundamental Index Russell Megacap Index 5.0% Fundamental Index Russell Megacap3.5% 3.6% Index 3.5% 4.0% 3.2% 4.0% 3.5% 3.6% 2.8% 3.5% 3.0% 2.4% 3.2% 1.9% 2.8% 3.0% 2.4% 1.5% 1.4% 1.3% 2.0% 1.9% 2.0% 1.5% 1.0% 1.4% 1.3% 1.0% 0.0% 0.0% (1.0%) (1.0%) (0.7%) (0.7%) (2.0%) (0.7%) (0.7%) (2.0%) (3.0%) (3.0%) (4.0%) (4.0%) (5.0%) (4.4%) (4.4%)(4.4%) (4.4%) (5.0%) May-­‐13 Jun-­‐13 Jul-­‐13 Aug-­‐13 Sep-­‐13 Oct-­‐13 Nov-­‐13 Dec-­‐13 Jan-­‐14 Feb-­‐14 Mar-­‐14 Apr-­‐14 Apr-13 May-13 Jun-13 Jul-13 Aug-13 Sep-13 Oct-13 Nov-13 Dec-13 Jan-14 Feb-14 Mar-14

ASAM Risk and Performance Summary, March 1, 2013 to April 30, 2014

Total Portfolio TAA FSCORE Fundamental Index3 Benchmark Statistics TAA Benchmark S&P 500 Benchmark Russell 2000 Value Russell Megacap Return 20.44% 4.17% 19.61% 7.28% Standard Deviation (daily) 0.70% 0.43% 0.89% 0.66% Standard Deviation (annualized) 11.12% 6.81% 14.10% 10.43% Sharpe Ratio 1.83 0.60 1.38 0.69 Treynor Ratio 0.20 0.04 0.20 0.07 Average Daily Return 0.07% 0.02% 0.07% 0.06% Number of Up Days 146 137 147 67 Number of Down Days 106 123 105 58 Percentage of Down Days 40.61% 47.13% 40.23% 43.94% Average Daily Gain 0.55% 0.32% 0.68% 0.54% Average Daily Loss -0.57% -0.32% -0.77% -0.50%

Portfolio Statistics Return 14.94% 5.66% 34.49% 7.60%

(continued on next page) 3 Since inception date for the Fundamental Index P ortfolio is as of October 29, 2013.

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1 Since inception date for the Fundamental Index Portfolio is as of October 29, 2013. ASAM Risk and Performance Summary, March 1, 2013 to April 30, 2014 (continued)

Excess Return (bps) (550) 149 1488 32 Standard Deviation (daily) 0.63% 0.68% 1.09% 0.61% Standard Deviation (annualized) 9.91% 10.70% 17.24% 9.63% Beta 0.81 1.29 1.08 0.90 Correlation 0.90 0.82 0.88 0.97 Sharpe Ratio 1.49 0.52 2.00 0.78 Treynor Ratio 0.18 0.04 0.32 0.08 Average Daily Return 0.06% 0.02% 0.12% 0.06% Number of Up Days 150 138 147 67 Number of Down Days 100 112 103 59 Percentage of Down Days 38.31% 42.91% 39.46% 44.70% Average Daily Gain 0.46% 0.50% 0.82% 0.51% Average Daily Loss -0.54% -0.56% -0.87% -0.45% Number of Outperforming Days 123 131 135 61

Other Portfolio Ratios Capture ratio -0.01% 0.01% 0.05% 0.00% Up capture indicator 83.18% 155.57% 121.68% 94.61% Down capture indicator 94.08% 173.96% 113.76% 90.35% Up number ratio 93.15% 79.56% 85.03% 88.06% Down number ratio 84.91% 73.98% 77.14% 87.93% Up percentage ratio 36.99% 56.93% 52.38% 37.31% Down percentage ratio 65.09% 43.09% 55.24% 62.34% Percent gain ratio 102.74% 100.73% 100.00% 100.00%

Notes: Capture ratio is the average of the captured performance (difference between fund’s returns and benchmark’s returns) Up capture indicator is fund’s average return divided by benchmark average return, considering only periods when benchmark was up Down capture indicator is fund average/benchmark average considering only periods when benchmark was down Up number ratio is number of periods fund and benchmark were up, divided by number of periods benchmark were up Down number ratio is number of periods fund and benchmark were down, divided by number of periods benchmark was down Up percentage ratio is the percentage of periods the fund outperformed when the benchmark was up Down percentage ratio is the percentage of periods the fund outperformed when benchmark was down Percent gain ratio is number of fund up periods over number of benchmark up periods

THE F -SCORE STRATEGY

Overvi1ew Since inception date for the Fundamental Index Portfolio is as of October 29, 2013. We had a great year in the F-Score strategy with an overall return of 34.5% over the holding period of May 1, 2013 to April 30, 2014. F-score was the leading strategy for this year’s ASAM class. The strategy’s return of 34.5%, beat its benchmark, Russell 2000 Value, by 14.9%. Given the prior success of F-Score, the 2014 F-Score team chose to build upon the work of the previous year’s class rather than institute drastic changes.

Based on the theory established in 1993 by Fama and French, small-cap value stocks tend to outperform large cap stocks due to market efficiencies and financial risk.4 Fama and French introduced the 3-factor model and the High minus Low (HML) risk factor with respect to book-to-market, which is a proxy for value stocks. Through this three factor model, Fama and French made the case that investors can improve returns of their portfolios by investing in small market-cap value stocks.

4 "Common risk factors in the returns on stocks and bonds". Fama and French, Journal of Financial Economics, 1993

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Furthermore, study of Value vs. Growth by Josef Lakonishkov, Robert Vishney and Andrei Shlieffer shows that low Price to Book (P/B) stocks outperform high P/B stocks 73% of the time for one-year holding periods, 90% of the time for three-year holding periods and 100% for holding periods of five years5. However, despite the strong performance of a low P/B value portfolio, a majority of stocks, almost 57%, underperform the market over one and two year stretches.

Building on the work of academics before him, Joseph Piotroski, set out to find a way for investors to distinguish between financially healthy value stocks versus stocks undervalued due to financial distress.6 Piotroski noted that while the mean market adjusted return of value stocks is significantly positive; the median return is in fact negative. Additionally, few value stocks show significant outperformance. He concluded that any strategy that could eliminate the left tail of the return distribution, the stocks that underperform the market, could greatly improve the portfolio’s performance.

Piotroski examined whether an investor could improve his or her investment returns by using a simple accounting-based fundamental analysis. Assuming that the average high book-to-market value stock is 1) neglected by the analyst community, 2) typically does not communicate outside of “formal” channels, and 3) tends to be financially distressed, Piotroski determined that this class of stocks typically does not incorporate financial information into prices in a timely manner, thus allowing for abnormal excess portfolio returns. Piotroski chose a total of nine financial “signals” that he concluded would best indicate the underlying financial health of a stock. He classified each stock’s fundamental signal as either “good”, assigned a value of 1, or “bad”, assigned a value of 0. The sum of these nine signals makes up the total F- score.

The nine financial measures are grouped into three key areas7:

1- Profitability 2- Financial Leverage or Liquidity 3- Operating efficiency

Financial performance: (profitability)

ROA>0: (net income before extraordinary items)/beginning of the year Total Asset

CFO>0: (cash flow from operation)/beginning of the year Total Asset

∆ROA>0: (ROA-this year) - (ROA-last year)

Accrual: (net income before extraordinary items – cash flow from operation)/ beginning of the year Total Asset (Expectation is to find that CFO is higher than Net income)

Financial performance: (Leverage, Liquidity & source of funds)

∆LEVER<0: changes in (Total Long Term debt/Average Total Asset)

∆LIQUID>0: positive changes in current ratio (current Asset/current Liability) over a year

EQ_OFFER: 1, if the firm did not issue common equity compared to the prior year)

5 “Contrarian Investment, Extrapolation and Risk”, Lakonishkov,Vishney and Shlieffer, Journal of Finance, 1994 6 “Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers”, Joseph Piotroski, Journal of Accounting Research, 2000 7 “Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers”, Joseph Piotroski, Journal of Accounting Research, 2000

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Financial performance: (operating efficiency)

∆Margin>0: firms current gross margin ratio (gross margin/total sales) – last year’s gross margin ratio

∆Turn>0: improvement in asset turn over (total sales/beg. Of year total asset)

Piotroski found that by identifying financially strong value stocks and differentiating between these assets against distressed stocks, he could improve the return of a low P/B value portfolio by 7.5% per year and beat the market by 13.4% per year (average low P/B portfolio outperformed the market by 5.9% from 1976 to 1996). Furthermore he found that an investment strategy that buys expected cheap/value stocks with a high F-score and shorts cheap/distressed stocks with a low F-score generated a 23% annual return between 1976 and 1996.

F-Score Shortcomings:

One criticism of the F-score is that the best-performing stocks tend to be smaller illiquid firms with low share prices and reduced trading volumes. Although the majority of firms do fall into at least one of these two categories, the margin is slim, with over 45% of top performing stocks classified as having medium or large share prices and trading volumes. Piotroski further examined the returns in higher market capitalizations by dividing the universe of stocks into three portfolios based on market capitalizations. Small market capitalization stocks make up nearly 62% of total high scoring stocks (F-score of 8 or 9). Meanwhile, medium-sized portfolio stocks made up 27% of the total and the top market capitalization stocks accounted for only 11% of high scores.

Piotroski also found the strongest benefit from financial statement analysis to be in the small stock portfolios. The results followed his assumption, since large capitalization stocks are more widely followed by analysts and therefore are less likely to suffer from mispricing and undervaluation that can be easily identified.

Piotroski found some benefit from the application of the F-score in the medium sized portfolio with high scoring stocks earning approximately 7% more than the average medium sized company and 17.3% more than the lowest scoring firms.

Additionally, Piotroski states that he “does not purport to find the optimal set of financial ratios for evaluating the performance prospects of individual “value” firms.” Because the F-score is just one way for investors to eliminate stocks with poor future prospects, our team used a modification to both the ratios utilized and the generated score to identify a potentially improved screening process. Finally, Piotroski does not recommend that investors only use the F-score as a stand-alone screen. He encourages investors to make a more macro analysis before applying the strategy.

In conclusion, using F-score as a measure of financial statement analysis holds up well and is robust across all levels of share price, trading volume, and analyst following. There is, however, a concentration of smaller, thinly traded firms that provide more predictable future stock returns. These small firms can present a limitation for investors looking to allocate large assets or to take a large position. For the purposes of ASAM, with modest assets, the F-score strategy offers few shortcomings.

Continuation of the strategy:

Given the strong performance of the F-Score strategy from 2012-2013, we continued the same metrics for this year. We applied the F-score filter for Small/Mid cap stocks within Russell 2000 index.

The 2012 – 2013 ASAM class had used the following metrics: Small/Mid-cap stocks with a market capitalization of less than $2.7B and P/B ratio of less than 1.8 with minimum daily value traded of $100K per stock; with the goal of including more liquid stocks, relatively cheap and with higher capitalization. In addition last years’ class also included an Altman-Z

11

12 | UCLA Anderson Student Asset Management Annual Report

score so as to differentiate the healthy stocks from distressed stocks based on the probability of default and bankruptcy of the respective firm. score so as to differentiate the healthy stocks from distressed stocks based on the probability of default and bankruptcy of In rebalancing this year we applied the same filters. However, given market appreciation we used a decile approach where the respective firm. necessary in place of “hard coded” numbers. We started with the Russell 2000 Index and filtered for market caps greater Inthan rebalancing $50M. For this the year remainder we applied of the the filters, same we filters. built- However,in formulas given to pull market the criteria appreciation and applied we used the filtersa decile in aexcelpproach for ease where of necessaryuse. The raw in placedata was of “hard cleaned coded” up, sorted numbers. and Wefiltered started as follows: with the initially Russell all 2000stocks Index with anand F -filteredScore 6 for or lowermarket were caps dropped. greater thanThen $50M. the stocks For the were remainder sorted based of the on filters, P/BV we deciles built- inand formulas the top to half pull – the more criteria expensive and applied stocks the – were filters discarded. in excel fo Thisr ease is ofin use.line withThe rawour goaldata ofwas focusing cleaned on up, value sorted stocks and andfiltered it is as a substitutefollows: initially for the all hard stocks code with of anP/BV F-Score of 1.8 6 used or lower last wereyear. dropped.We then Thenapplied the the stocks Altman were Z- sortedscore filter based to onfactor P/BV for deciles risk of and distress the top and half chose – more stocks expensive with a scorestocks of – 2.99were or discarded. higher. Finally, This is we in linechecked with forour stocks goal of with focusing a daily on trading value stocksvalue over and $100kit is a substituteto ensure liquidity.for the hard Once code this of final P/BV set ofwas 1.8 sorted used last into year. a group We of the 63n appliedstocks, wethe sorted Altman again Z-score for F filter-Score. to Chosingfactor for the risk highest of distress F-Score and as chose well the stocks lowest with P/BV a score stocks of 2.99led to or a higher.portfolio Finally, that was we compocheckedsed for of stocks 21 stocks. with Thereforea daily trading the final value criteria over $100kwere: to ensure liquidity. Once this final set was sorted into a group of 63 stocks, we sorted again for F-Score. Chosing the highest F-Score as well the lowest P/BV stocks led to a portfolio that was - F-Score 7 or higher composed of 21 stocks. Therefore the final criteria were: - P/BV in the bottom half of Russell 2000 Index Altman Z Score > 2.99 - F-Score 7 or higher - Average Daily Trading Value > $100k - P/BV in the bottom half of Russell 2000 Index - Altman Z Score > 2.99 Russell S&P ASAM F-Score 2000 - Average Daily Trading Value > $100k 500 Value Total return: 5/1/13 - 4/30/14 34.5% 19.6% 20.4%

Average Monthly Return 2.59% 1.56% 1.60% Monthly Standard Deviation 4.32% 3.66% 2.89% Monthly Sharpe Ratio 0.60 0.43 0.55

Average Daily Return 0.12% 0.07% 0.07% Daily Standard Deviation 1.09% 0.89% 0.70% Daily Sharpe Ratio 0.11 0.08 0.10

Information Ratio 0.44

Factor Contributions CAPM Market - Risk Free % Alpha 34.5% 34.47% 0.01%

Figure 1F: aSamplema Frenc resultsh from Mscreeingarket - Ri smetricsk Free % forSM BA %SAMHM FL- Score% Alph a 34.5% 16.60% 15.64% 1.97% #### For all scoring metrics we used CapIQ to pull financial data. In the screen, we included both values and logic gate screens withinFigure Capital 1: Sample IQ to results screen andfrom attribute screeing a score metrics of 1 for for each ASAM F-Score. F-Score For all scoring metrics we used CapIQ to pull financial data. In the screen, we included both values and logic gate screens After running the screen and extracting the financial information for the majority of the Russell 2000 index, we sorted the within Capital IQ to screen and attribute a score of 1 for each F-Score. data based on the F-score calculated above (ASAM F-Score), and compared it to the CapIQ built-in Piotroski Score filter. AfterThe Scores running were the comparable screen and evenextracting though the not fin identical.ancial information The differences for the between majority the of theASAM Russell F-Score 2000 and index, the build we sorted-in CapIQ the dataPiotroski based score on thecan Fbe-score highlighted calculated as follows: above (ASAM F-Score), and compared it to the CapIQ built-in Piotroski Score filter. The Scores were comparable even though not identical. The differences between the ASAM F-Score and the build-in CapIQ For the comparison of CFO and NI, criteria 4, we used LTM whereas the built-in function uses latest K. We used the long Piotroski score can be highlighted as follows: term-debt to capital ratio for criteria 5 based on historical screens used by last year’s class instead of the bottom up formula For the comparison of CFO and NI, criteria 4, we used LTM whereas the built-in function uses latest K. We used the long term -debt to capital ratio for criteria 5 based on historical screens used by last year’s class instead of the bottom up formula12

UCLA Anderson Student Asset Management Annual Report | 13 12

used by Capital IQ’s built-in function. The shares outstanding formula for criteria 7, for the built-in formula only factors equality of shares outstanding whereas a key criteria is whether the shares have increased or not over the past year, hence the importance of net share buybacks. On Criteria 8 again we used LTM versus the latest K built-in CapIQ. Finally for criteria 9 we used the latest quarter in place of the latest K.

Distress Risk & Probability of Default:

In looking for value, how do we avoid a value-trap? Price-to-Book and distress have often been used synonymously in finance literature. Fama and French8 repeatedly use the word distress to describe the Book-to-Price factor. 20 Years later financiers are still debating and publishing papers on the subject.

One well-written discussion of the distress debate is a paper by Campbell et. al.9 Campbell suggests that all else equal, distressed stocks under-perform non-distressed stocks. Therefore, distress is an unlikely explanation for value returns. Or, put another way, value stocks continue to perform well, even after you hedge out your exposure to distress, momentum, Beta, and size.

We continued using the Altman Z-score as a measure to protect against choosing distressed stocks. Altman Z predicts the probability that a firm will go into bankruptcy within two years. The Z-score uses accounting metrics to measure the financial health of a company. The definition of the Altman Z-score is available on various sources including the original paper10, Wikipedia and Investopedia and the definition is as follows:

Altman Z-score calculation

T1 = Working Capital / Total Assets

T2 = Retained Earnings / Total Assets

T3 = Earnings Before Interest and Taxes / Total Assets

T4 = Market Value of Equity / Total Liabilities

T5 = Sales/ Total Assets

Z score bankruptcy model:

Z = 1.2T1 + 1.4T2 + 3.3T3 + 0.6T4 + .999T5

Zones of Discrimination:

Z > 2.99 “Safe” Zones

1.81 < Z < 2.99 “Grey” Zones

Z < 1.81 -“Distress” Zones

Tests of the Altman Z-Score over a 30 year period have shown it to be up to 90% accurate in predicting bankruptcy one year in advance.11

8 “The Cross-Section of Expected Stock Returns”, Fama, French, Journal of Finance, 1992 9 “In Search of Distressed Risk”, Campbell, Hilscher, Szilagyi, Harvard Institute of Economic Research, 2005 10 “Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy”. Edward Altman, Journal of Finance, 1968 11 “Predicting Financial Distress of Companies”, Edward Altman, 2000, http://pages.stern.nyu.edu/~ealtman/Zscores.pdf

13 14 | UCLA Anderson Student Asset Management Annual Report

Performance of F-Score portfolio from May 1, 2013 through April 30, 2014:

Given the metrics and approach above, we can report the following performance versus benchmarks: the chart below depicts how $100,000 investment in F-Score has performed versus the Russell 2000 value benchmark and the broader market of S&P 500 over the holding period of 05/01/2013 to 04/30/2014: Figure 2: Performance of ASAM F-Score versus benchmarks

150,000 F-Score Russell 2000 Value 140,000 S&P 500

130,000

120,000

110,000

100,000

90,000 4/6/2013 6/15/2013 8/24/2013 11/2/2013 1/11/2014 3/22/2014 5/31/2014

14 UCLA Anderson Student Asset Management Annual Report | 15

Delving in deeper the charts below highlight portfolio attributions over the holding period of 05/01/2013 to 04/07/2014. Keep inDelving mind thatin deeper given thethe chartsquantitative below approach highlight portfolioto our strategy, attributions we refrain over thefrom holding delving period in too of deep 05/01/2013 into specific to 04/07/2014. stocks as that mayKeep bias in mindour selection that given of the portfolioquantitative and approach turn the process to our strategy, subjective we to refrain human from judgment delving. in too deep into specific stocks as that may bias our selection of the portfolio and turn the process subjective to human judgment.

Figure 3: Portfolio Attribution Report

Figure 3S: uPortfoliommary Attribution Report Summary Portfolio FSCORE Portfolio FSCORE Benchmark ISHARES RUSSELLBenchmark 2000 VALUE INDEX FUND (IWN US)ISHARES RUSSELL 2000 VALUE INDEX FUND (IWN US) Start Date(Earliest Available) 4/30/2013 Start Date(Earliest Available) 4/30/2013 End Date 4/30/2014 End Date 4/30/2014 Attribution Summary (Grid) Attribution Summary (Grid) Avg % Wgt CTR Tot Rtn Tot Attr Alloc Selec Inter Port Bench +/- Port Bench +/-Avg %P oWrt gt Bench +/- CTR FSCORE 100.00 100.00 0.00 35.18 20.08Port 15.10 Benc35.18h 20.08+/- 15.10Port 15.10 3.54 11.56 Industrials 30.47 12.84 17.63 12.45 3.33 9.12 42.95 27.38 15.58 5.49 1.20 4.29 Consumer DiscFSCOREretionary 16.76 10.36 6.39 5.77 2.44100.003.33 100.0031.35 22.800.00 8.54 35.181.76 0.16 1.60 Energy Industrials 13.51 7.12 6.39 4.90 2.1130.472.79 12.8434.03 30.6517.63 3.38 12.451.43 0.72 0.71 Information Technology Consumer Discretionary 12.25 10.71 1.54 1.82 2.7416.76-0.92 10.3611.07 25.976.39 -14.90 5.77-1.16 -0.28 -0.88 Materials 11.33 4.61 6.72 4.04 0.81 3.23 33.44 18.34 15.10 1.69 0.12 1.57 Health Care Energy 5.73 4.49 1.23 6.16 1.17 13.514.99 171.347.12 28.296.39143.06 4.905.20 -0.03 5.23 Cash Information Technology 5.59 0.13 5.46 0.00 0.00 12.250.00 10.710.00 0.001.54 0.00 1.82-1.29 -1.29 0.00 Consumer Staples Materials 4.07 2.38 1.69 0.42 0.55 11.33-0.13 4.616.32 24.236.72 -17.92 4.04-0.73 -0.02 -0.71 Telecommunication Services 0.28 0.53 -0.25 -0.39 -0.05 -0.34 -7.20 -8.15 0.94 -0.22 0.03 -0.25 Financials Health Care 0.00 38.39 -38.39 5.11 5.73-5.11 4.49 12.961.23 -12.96 6.162.92 2.92 0.00 Stocks Cash 0.00 2.28 -2.28 1.22 5.59-1.22 0.13 36.565.46 -36.56 0.00-0.68 -0.68 0.00 Utilities Consumer Staples 0.00 6.15 -6.15 0.64 4.07-0.64 2.38 9.681.69 -9.68 0.420.68 0.68 0.00 Telecommunication Services 0.28 0.53 -0.25 -0.39 Financials 0.00 38.39 -38.39 Stocks 0.00 2.28 -2.28 Utilities 0.00 6.15 -6.15

Top 20 Contribution to Return Avg % Wgt CTR Port Bench +/- Port FSCORE 100.00 100.00 0.00 35.18 ANIKA THERAPEUTICS INC 5.45 0.02 5.43 6.52 RED ROBIN GOURMET BURGERS8.96 0.02 8.94 4.15 KIMBALL INTERNATIONAL-B 3.97 0.06 3.91 3.50 CHASE CORP 5.83 0.02 5.81 3.19 ENERSYS 6.79 0.26 6.53 3.09 LYDALL INC 4.84 0.04 4.80 2.78 ADAMS RESOURCES & ENERGY7.70 INC 0.02 7.68 1.70 PC CONNECTION INC 4.56 0.02 4.54 1.61 GRANITE CONSTRUCTION INC 4.06 0.17 3.90 1.61 ZYGO CORP 0.35 0.03 0.32 1.39 BOLT TECHNOLOGY CORP 4.91 0.02 4.89 1.22 REX AMERICAN RESOURCES CORP0.32 0.02 0.29 0.94 BOB EVANS FARMS 5.00 0.16 4.84 0.89 GLATFELTER 5.51 0.04 5.47 0.85 VAALCO ENERGY INC 0.30 0.02 0.28 0.83 SKECHERS USA INC-CL A 0.30 0.15 0.15 0.76 FREIGHTCAR AMERICA INC 4.18 0.03 4.14 0.61 ARCBEST CORP 0.31 0.09 0.21 0.58 UNIFIRST CORP/MA 5.48 0.10 5.37 0.45 FRESH DEL MONTE PRODUCE INC4.07 0.14 3.94 0.42

15 16 | UCLA Anderson Student Asset Management Annual Report 15

Finally, the following tables list the portfolio holdings throughout the year before and after the rebalancing:

F-Score Portfolio 05/01/13 - 04/07/14: F-Score Portfolio 04/07/14 - present: Ticker Company weight Ticker Company weight AE ADAMS RESOURCES & ENERGY NEW 1% DTSI DTS INC 5% ANIK ANIKA THERAPEUTICS INC 7% EGY VAALCO ENERGY 5% BOBE BOB EVANS FARMS INC 1% ETH ETHAN ALLEN INTERIORS INC 5% BOLT BOLT TECHNOLOGY CORP 7% FIX COMFORT SYSTEMS USA INC 5% BSET BASSETT FURNITURE INDS INC 1% FLWS 1-800 FLOWERS.COM INC CL A 5% CCF CHASE CORP 7% LDL LYDALL INC 5% ENS ENERSYS 7% MDCI MEDICAL ACTION INDS INC 5% FDP FRESH DEL MONTE PRODUCE INC 1% MIND MITCHAM INDS INC 5% GLT GLATFELTER 7% PCTI PC TEL INC 5% GVA GRANITE CONSTR INC 7% PSEM PERICOM SEMICONDUCTOR CORP 5% KBALB KIMBALL INTL INC CL B 7% QLGC QLOGIC CORP 5% LDL LYDALL INC 7% RECN RESOURCES CONNECTION INC 5% PCCC PC CONNECTION INC 7% RELL RICHARDSON ELECTRONICS LTD 5% RAIL FREIGHTCAR AMERICA INC 7% REX REX AMERICAN RESOURCES CORP 5% RRGB RED ROBIN GOURMET BURGERS INC 7% SKX SKECHERS USA INC CLASS A 5% UNF UNIFIRST CORP 7% SSD SIMPSON MANUF CO INC 5% XOXO XO GROUP INC 7% USMO USA MOBILITY INC 5% Cash 7% WMAR WEST MARINE INC 5% ZIGO ZYGO CORP 5% Cash 6%

Back-testing:

The goal of the back testing the F-score strategy is to: first and foremost, test the hypothesis that selecting ‘cheap stocks’ based on the ranked lower 30% of the population based on the EV/EBITDA ratio, outperform the non-sorted F-score benchmark of the same respective F-score tranche bands and secondly and perhaps more importantly to create a robust back-testing approach and platform using the data available to ASAM and non-proprietary technology the incoming ASAM 2015 Fellows. Keep in mind that given the educational lens that is the mission of ASAM our goal was to lay a foundation that can be built upon each year. For this reason we invested time in exploring a coding language that would be conducive to a perpetual handoff and one that would allow for modules to be added incrementally.

The back test will compare two groups, the F-Score strategy for score bands seven, eight and nine as outlined by Joseph Piotroski, to a sorted ‘cheap stock’ segment of the same respective bands over a ten year period from 2002 to 2012 by F- score score band by year. To define a ‘cheap stock’, the enterprise value to EBITDA ratio will be used:

𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬 𝑽𝑽𝑽𝑽𝑽𝑽𝑽𝑽𝒆𝒆 = 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣 𝑜𝑜𝑜𝑜 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 + 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣 𝑜𝑜𝑜𝑜 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 + 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖

+ 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 − 𝑐𝑐𝑐𝑐𝑐𝑐ℎ

𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉 𝐸𝐸𝐸𝐸!"#$% = 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸

16 UCLA Anderson Student Asset Management Annual Report | 17

To test the hypothesis, we will analyze the z-statistics based on the following test by year:

Hypothesis:

H0:

H1: 𝜇𝜇!!!"# !"#$% !"#$%& ! 𝜇𝜇 !"#$%& ! 0

!!!"# !"#$% !"#$%& ! !"#$%& ! Test: 𝜇𝜇 𝜇𝜇 0

!!!!"# !"#$% !"#$%& ! !!"#$%& 𝑧𝑧 − 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑐𝑐(!): !!!!"# !"#$% !"#$%& Where t equals the year from 2002 to 2012

As for the outcome, we will have a z-statistic per year, which will enable us to state the percentage of times that the ‘cheap stock’, F-score statistically significantly, outperformed the traditional F-score as well as whether the performance occurred during an ‘up’ or ‘down’ market.

The second goal of the back test is to provide a robust back testing methodology using CRSP and Compustat data from Wharton Research Data Services (WRDS) and technology, such as “R”, to analyze the results. To date, the back test “R” code calculates the F-score as defined by Joseph Piotroski using the Merged CRSP and Compustat databases and provides a comprehensive starting point for the ASAM Class of 2015.

Next Steps and Recommendation for the Class Of 2015:

Using EV/EBIT Multiple vs. Book to Market

University of Chicago’s Wesley R. Gray and Tobia Carlisle back-tested the impact of different value multiples from 1964 to 2012. Their study shows a comparison of 5 different value multiples: Earning Yield, EBITDA multiple, EBIT Multiple, Free Cash Flow Yield and Book to market. The value decile of the EBIT multiple has the highest CAGR, lower downside deviation, the highest Sharpe ratio and the highest Sortino ratio in comparison with the value deciles of the other multiples.

Earning EBITDA Free Cash Book to S&P 500 Yield Multiple EBIT Multiple Flow Yield Market CAGR 12.44% 13.72% 14.55% 11.68% 13.11% 9.52%

17.62% 17.25% 17.20% 16.42% 17.39% 15.19% Standard Deviation

12.17% 11.49% 11.34% 11.00% 11.12% 10.66% Downside Deviation Sharpe Ratio 0.46 0.53 0.58 0.44 0.50 0.33 Sortino Ratio 0.68 0.82 0.89 0.68 0.80 0.50 (MAR=5%)

-­‐49.01% -­‐43.45% -­‐37.25% -­‐44.54% -­‐49.20% -­‐50.21% Worst Drawdown

Figure 4: Risk measure for value decile of all price ratios12

12 Quantitative Value, A Practitioner's Guide to Automating Intelligent Investment and Eliminating Behavioral Errors,

17 18 | UCLA Anderson Student Asset Management Annual Report

Our Recommendation for the next class is to replace the Book to Market ratio in F-score with the EV/EBIT multiple and back test the result for the last 20 years.

Weighing ratios

We have used equal weight ratios in our construct but perhaps a more optimized portfolio based on Sharpe ratios would emulate a Markowitz optimization based on Sharpe ratios. There are papers that suggest equal weight could outperform other “optimized” portfolios. Specifically for the case of F-Score this can be tested to establish the best approach going forward.

Long/short

We continue to believe there is a significant risk reduction and upside with incorporating a short strategy along the long portfolio. Numerous papers and practitioners advise to the benefits of such an approach especially for the case of the F- score.

Other factors to include: Momentum

There is literature to suggest that momentum combined with value can produce disproportionate returns. However, the F- score strategy has a time horizon of one year whereas momentum tends to have a shorter time horizon, typically 6 – 9 months. Therefore it is worth investigating to see if incorporating for momentum and optimizing for turnover, a superior strategy can be developed.

F-score attribution regression model

To advance the theory of F-score, one idea we did not implement by highly encourage for the next class is attribution regression. This is perhaps the most difficult recommendation here to implement given the complexity of tracking. This is the idea of tracking performance of F-scored portfolios and matching this performance with the metrics for each stock in each individual F-score category. Therefore the final output of this exercise would highlight the importance of each of the nine metrics. Once this relationship is established the weighing of the F-score factors can be optimized to select for more optimal portfolios.

Rebalancing optimization

Although the original F-score looks at the one-year time horizon for optimization, with multiple changes and addition of factors such as momentum it is plausible to entertain shorter rebalancing periods. An interesting exercise would be to see if shorter time horizons can outperform when back-tested over longer periods and after inclusion of some of the new metrics mentioned.

Wesley R. Gray, Tobias E. Carlisle, Wiley, December 2012

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THE TACTICAL ASSET ALLOCATION (TAA) STRATEGY

1. Overview

The 2014 TAA class took over responsibility for ASAM’s Tactical Asset Allocation model in May 2013. We inherited a successfully back-tested and fully invested model from the 2013 class. Upon assuming control of the model, we first familiarized ourselves with the mechanics of the optimization function and data updating requirements. We then devised ways to confirm the back-testing results of 2013 and build tests for our own hypotheses.

During the time period when we were acquainting ourselves with the model and performing our back-tests, we continued to invest using the existing 2013 model. The 2013 model consisted of 5 asset classes: US Stocks, US REITS, US Total Bond Market, Emerging Markets Stocks, and Commodities. Each asset class is invested in a low cost, diversified index ETF. The benchmark is simply an equal-weighted portfolio of these five ETFs.

We have achieved several of the goals that we set throughout the year and have made progress in improving the model. There are also a few weaknesses in the model that the current class has not yet been able to address with a satisfactory improvement solution. Below are highlights of major accomplishments made as well as some opportunities for improvements discovered by the current class:

Accomplishments:

• Back-tested 54 different permutations of parameters for two different sets of asset classes using two different sets of volatility measurements. • Tested the significance of the various parameter categories in the optimization. • After successfully back-testing a new set of asset classes, replaced the Vanguard FTSE Emerging Markets ETF in our model with the Vanguard FTSE All-World Ex-US ETF to capture a broader array of global stock returns.

Discoveries:

• Recognized that the existing model did not account for dividend payments, but were unable to devise an agreed-upon solution to this issue, though several were attempted. • Discovered that, all else equal, a higher frequency of rebalancing increased risk-adjusted returns, but were unable to fully complete a back-test using monthly intervals to extrapolate our findings to shorter rebalancing time periods. • Attempted to expand the optimization model to include more than 5 asset classes, but could not achieve this goal due to technical and programming limitations.

As of April 30, 2014, the TAA portfolio had holdings that totaled $ 217,035.27 invested across five ETFs and cash. Based on analysis and model outputs, our team rebalanced our asset allocations quarterly for a total of four re-allocations over the 2013-2014 year. To date, we have invested and re-allocated the portfolio on four occasions, according to model specifications: June 21, 2013; September 24, 2013; December 23, 2013 and March 25, 2014. We also invested a cash inflow of roughly $46,000 on October 29, 2013 according to our model’s specifications. The current team is working with the incoming class and will assist with the portfolio reallocation scheduled for June 25, 2014.

The 2014 TAA team provided a rich combination of skills that includes hands on asset-management (DoubleLine, PIMCO), quantitative skills (Boeing) and alternative investments (Luminous Capital, Toma Capital):

• Alex Revy – Strategy Lead • Jacob Gore – Risk Manager • Nedal Alqam • Kevin Zhang

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2. Strategy Overview

The TAA Strategy is designed to add value to an investment portfolio by capturing expected relative market outperformance within a chosen set of asset classes during a pre-determined periodic time frame. The strategy generates alpha in comparison to a static benchmark by over or under weighting the asset classes that comprise its benchmark. ASAM has developed a quantitative asset allocation model that uses a risk-weighted statistical framework to predict excess returns for each asset class in order to determine which asset classes to invest in and with what relative weights. The TAA Strategy team has also defined different trading rules to control and scale the amount of over/under weighting of each security in our portfolio.

This strategy is based on the academic paper “Strategic Asset Allocation” (1997) by Michael Brennan. The paper’s thesis teaches that investors can optimize their mean – variance trade-off when there is time variation in expected returns on a portfolio of stocks, bonds, and alternative investments. Although Brennan’s paper sets the foundation of the optimal asset allocation mix, it provided limited guidance over how practitioners should build a quantitative model that tracks the market risk factors and allocates to certain asset classes based on optimal risk-return trade off. To address this problem, the TAA team used the quantitative model derived from William Sharpe’s paper “What Practitioners Need to Know about Optimization?” (1987), and first implemented by the 2013 class. The enhanced model accounted for the correlation among the asset classes and tolerated the no shorting constraint that every ASAM strategy must adhere to.

3. Model Overview

The TAA model is one that seeks to optimize the percentage of a portfolio allocated to each of that portfolio’s assets by maximizing an objective function. The objective function (shown below) is one that reflects the tradeoff between an expected portfolio return and the overall portfolio risk. It is a function of the expected asset returns, implied asset volatilities, a risk aversion coefficient, and the weighting of each asset in the portfolio, as shown below:

Maximize Φ Φ=E[RP]- λ σP

⋅ Where: RP = Portfolio Return λ = Risk Aversion Coefficient σP = Portfolio Implied Standard Deviation

The risk aversion coefficient measures our willingness to sacrifice one unit of expected return in order to reduce risk (implied standard deviation) by λ units. Thus, ceteris paribus, the higher lambda is, the lower Φ will be. By maximizing this objective function, we maximize expected returns minus a quantity representing our risk aversion times portfolio implied standard deviation. The resulting utility, Φ, measures the degree of satisfaction that we derive from a particular combination of expected return and risk, given our attitude towards risk. In order to calculate the expected portfolio return, a matrix of portfolio weights was multiplied by a matrix of the expected excess return for each asset in the portfolio. The expected excess returns were calculated as the average, annualized excess return for a period of time in the immediate past. In order to calculate the portfolio standard deviation, the matrix of portfolio weights was used along with an implied covariance matrix. This covariance matrix was calculated by using implied volatilities from one month options on each of the assets in the portfolio and the historical correlations of every asset to each other over the same time period used to calculate expected excess return. Once the matrices were calculated, the maximization of the objective function was calculated and the optimal asset allocation calculated. This process was repeated and the portfolio rebalanced quarterly.

Implementation / Improvements

The goal for the TAA team during the calendar year was to fully analyze the model, the back-testing, and all the variables employed with the objective of validating the current model and finding areas to improve. In order to do so, we re-ran the back-testing to confirm the results using both implied and realized volatility data and existing ETFs. Through this process,

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we validated a number of hypothesis including the superiority of implied volatility versus historical, the max and min allocation constraints, and the lambda (risk coefficient of the model). We also uncovered a number of areas to focus on, including finding an alternative ETF to represent the Global ex-U.S. market, which was not currently represented appropriately. We also recently discovered that the current model was using price movements for ETFs without accounting for the dividends earned, and therefore, not fully accounting for total returns.

4.1 Index/Investment Vehicles

The model we inherited employed five ETFs which represented diverse set of asset classes. The criteria used for selecting the investment options were cost, effectiveness, length of trading history, liquidity, and option availability (to obtain implied volatility data).

The model that we inherited included a number of different asset classes: U.S. equities, real estate, emerging market equities, core bonds, and commodities. Our team did not feel that non-U.S. equities were represented adequately, and as such, we began to research for alternatives. Ultimately, we decided on a broader ETF to represent foreign developed and emerging market equities in lieu of the existing ETF. As depicted in the regional allocation grid below, Vanguard FTSE All- World ex-US (“VEU“) is more geographically diverse, encompassing markets such as Japan, Australia, and the countries of Europe. The back-testing section includes further details on the analysis and results.

Previously Previou Used sly Use ETFs d ETFs Asset Class Asset CTickerlass NameTicker Expense N a RatiomeInception Net Assets US US Equities Equities VTI Vanguard VTI Total Stock Vanguard Market Total0.05% Stock MarketJun-­‐01 324B Emerging Emerging Markets Markets VWO Vanguard VWO MSCI EM Vanguard MSCI0.15% EM Mar-­‐08 58B BondsBonds BND Vanuard BND Total Bond MarketVanuard Total0.08% Bond MarketApr-­‐07 112B Real Real Estate Estate VNQ Vanuard VNQ Real Estate Vanuard Real0.10% Estate Aug-­‐04 39B CommoditiesCommodities DBC PowerShares DBC DB Commodity PowerShares Index 0.85% DB CommodityFeb-­‐06 Index 5.7B Cash Cash Cash Cash Cash Cash n/a n/a n/a

New ETF Line-­‐up New ETF Line-up Asset Class Ticker Name Expense Ratio Inception Net Assets US Equities AsseVTIt Class Vanguard Total Ticke Stock r Market 0.05%Name Jun-­‐01 324B Non-­‐USUS Equities VEU Vanguard VTI FTSE All World Vanguard ex-­‐US Total0.15% Stock MarketMar-­‐07 21B BondsNon-US BND Vanuard VEU Total Bond MarketVanguard FTSE0.08% All WorldApr-­‐07 ex-US 112B Real Bonds Estate VNQ Vanuard BND Real Estate Vanuard Total0.10% Bond MarketAug-­‐04 39B CommoditiesReal Estate DBC PowerShares VNQ DB Commodity Vanuard Index Real0.85% Estate Feb-­‐06 5.7B Cash Commodities Cash Cash DBC PowerShares DBn/a Commodityn/a Index n/a Cash Cash Cash VEU Composition/Regional Allocation Region MV% Index Composition Emerging Markets V17.50%EU Composition/Regional Allocation Weight Index Name Europe R47.90%egion MV% 20% MSCI US REIT Index Pacific Emerging27.70% Markets 17.50% 20% CRSP US TOTAL MARKET INDEXMiddle East Europe0.50% 47.90% 20% FTSE ALL WORLD EX-US INDEX North America Pacific6.40% 27.70% 20% BARCLAYS US AGG FLOAT INDEX Middle East 0.50% 20% DB COMMODITY INDEX North America 6.40%

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4.3 Volatility Inputs

The TAA model attempts to maximize a utility function by measuring an investor’s willingness to sacrifice one unit of expected return in order to reduce risk (variance) by one unit. When estimating future variance, the model called for the use of historical standard deviation figures. However, implied volatility was thought to be a better predictor of future volatility and asset class performance. We re-tested the hypothesis by running a regression analysis on the five asset classes, using the new ETF (VEU) and excluding cash. We concluded that implied volatility is a better predictor of future realized volatility levels versus historical volatility data.

4.3 Defining Risk Management Procedures

Risk management, as applicable for TAA, can be perceived as diversification among asset classes, rebalancing frequency, asset class constraints, and the use of volatility to optimize allocations. A diverse set of asset classes was used not only to ensure exposure to different asset types, but also to achieve low correlation across each investment, with the objective of reducing overall volatility of the portfolio. Furthermore, the use of ETFs offers diversification among different companies, allowing us to avoid significant individual security or sector risk.

As we back-tested the model, we evaluated different asset allocation constraints, different Lambda levels, and rebalancing periods. We maintained the current asset class constraints to be no more than 50% or less than 5% of the portfolio. We then considered different Lambda levels, varying from one to three and ultimately decided to use two as this was the optimal point based on back-testing results.

4.4 Employing Excess Returns

Realizing that the risk-free rate fluctuates over time and to standardize our return evaluations, we used excess return measures, which allowed us to better estimate the true and more representative return for each asset class over time.

4.5 TAA’s Benchmark

In the years prior, the S&P500 Index was used as the primary benchmark. As we transitioned to this strategy last year, our team along with the prior year class decided that the S&P was not an appropriate benchmark, simply because the TAA portfolio is more broadly invested portfolio with a different risk and return profile. The decision was made to use an equal weight portfolio as the benchmark. Since the TAA strategy has the ability to allocate and rebalance between different asset classes, the best way to measure its effectiveness is to compare it to an equally weighted and static portfolio.

The custom benchmark currently used for the TAA strategy is an equally weighted portfolio comprised of the following indices. Given the change in one of the ETFs used in the TAA portfolio to represent a broader foreign equity universe, we also adjusted the benchmark accordingly. As of December 24, 2013, we replaced the FTSE Emerging Market Index with the FTSE All World ex-US Index. May 2013 - December 2013 Weight Index Name Weight20% MSCI USIndex REIT Index Name 20%20%CRSP USMSCI TOTAL MARKET US REIT INDEX Index 20%20%FTSE EMERGINGCRSP US MARKETS TOTAL INDEX MARKET INDEX 20% BARCLAYS US AGG FLOAT INDEX 20%20%DB COMMODITYFTSE ALL INDEX WORLD EX-­‐US INDEX 20% BARCLAYS US AGG FLOAT INDEX December 2013 - Present Weig20%ht Index NDB ame COMMODITY INDEX 20% MSCI US REIT Index 20% CRSP US TOTAL MARKET INDEX 20% FTSE ALL WORLD EX-US INDEX 20% BARCLAYS US AGG FLOAT INDEX 22 20% DB COMMODITY INDEX

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5. Back-Testing Summary

Methodology • Back-tested 54 different permutations of 4 portfolio parameters • Used 2 different measures of volatility for each back-test permutation • Ran this back-test on the existing model and a new model devised in Fall 2013 • Measured the effectiveness of each portfolio parameter

Results • Confirmed that portfolios created using implied volatility vastly outperformed portfolios created with historical volatility data • Found that the new model that used all non-US stocks for its foreign equity allocation outperformed the old model that allocated only to emerging market stocks. • Discovered that the single most important factor in the model’s performance was using a shorter look-back period in estimating returns and covariances, followed by which measure of volatility was used. The minimum and maximum constraints were the next most important. • Lambda, the risk coefficient, was the least important parameter in determining future returns.

Our ideal parameters based on the backtest were: • Quarterly look-back to measure returns and the corresponding covariance matrices • Implied volatility • 5% Minimum Constraint • 50% Maximum Constraint • 2 Lambda

5.1 Purpose

• To determine if the model is able to consistently outperform the benchmark • To validate the results of the 2013 class • To determine whether a new asset class would outperform the current asset mix • To select the best parameters for the model • To determine which parameters are the biggest drivers of performance

The Tactical Asset Allocation back-testing started as an exercise to validate the 2013 team’s back-test results and grew to test additional hypotheses the 2014 team had developed regarding the model. The initial purpose of the back-testing was two-fold: first, to determine the effectiveness of the model in producing allocations that exceed the risk-adjusted returns of the benchmark and second, to select the optimal parameters to use as model inputs.

From the data produced through this back-test, we measured the predictive power of each individual variable to determine which factors have the greatest effect on the model and which factors are most critical in producing the highest risk- adjusted returns as measured by the Sharpe ratio.

Finally, a parallel model was constructed to measure the effect of changing one asset class in the allocation. Our team believed that an allocation to global stocks from both developed and emerging nations would more accurately reflect a holistic global asset allocation than an allocation to emerging market stocks alone. We back-tested the new model to determine if the new global asset allocation would outperform the old foreign stock allocation to just emerging market countries.

The two different models will hereafter be referred to as the VWO model and the VEU model. VWO is the ticker for the Vanguard MSCI Emerging Markets Stock ETF, while VEU is the ticker for the Vanguard FTSE All-World ex-US ETF.

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5.2 Data Selection

Two main data points were downloaded from Bloomberg with additional data points calculated within the model. From Bloomberg, we obtained daily closing stock price levels and the daily risk-free rate, as measured by the annualized return of the three-month T-Bill. We also downloaded daily closing implied volatility levels on the 30-day options for each ETF. With this data, many additional measures are calculated within the model including historical standard deviations of returns, correlations, covariance, and excess returns for various time periods.

30 day option implied volatilities were chosen due to the higher liquidity of shorter maturity options markets. This increased liquidity is believed to result in a more dynamic and accurate measure of current volatility expectations in markets and thus to have higher predictive relevance. Continuous data is also more readily available in the shorter maturity option markets.

The full data sets begin for both models in May 2007, resulting in over six years of data available for back-testing. The starting date of the model data was dictated by the first day that implied volatility data was available for all five asset classes. Although the data time period is relatively short, it encompasses nearly an entire business cycle. Ultimately our back-test data set spanned from May 10, 2007 to September 20, 2013.

5.3 Model Inputs

Lower Upper Backtesting Length Volatility Lambda Bound Bound (years in past) Type 1 5% 30% 0.25 Historical 2 10% 40% 0.5 Implied 3 50% 1

The team was able to vary four categories of parameters to create 54 permutations of the TAA model to back-test. These four categories are the lambda, back-testing look-back length, and minimum and maximum asset allocation limits. Daily measurements of returns of volatilities were used in the back-tests.

The lambda, or risk coefficient, values tested were 1, 2, and 3. This variable is multiplied by standard deviation and subtracted from expected return in the optimization model. Roughly speaking, the optimization equation will seek to reduce standard deviation at the expense of expected return as lambda increases.

The minimum and maximum asset allocation limits were imposed to stay true to the multi-asset nature of Tactical Asset Allocation. The team felt that it was important to never have a zero weight to an asset class nor to let a single asset class represent more than half of the portfolio, as those conditions would reduce the diversification benefit of the TAA portfolio and increase idiosyncratic risk. The lower bounds tested were 5% and 10%. The upper bounds tested were 30%, 40%, and 50%.

The back test length is toggled to run different amounts of daily past performance and standard deviation data through the mean-variance optimization algorithm to recommend a portfolio whose performance is then measured over the following three month period. This variable explores the effects of varying the timeframe the model used to calculate the asset excess returns and the covariance matrix, which itself is constructed from correlations and volatilities of each asset over the time period. For example, a back test length of one year means that the algorithm would look back over 4 previous quarters of return, volatility, correlation and covariance data to produce a portfolio to invest in for the upcoming quarter. In all situations, the back test produces a portfolio from historical data that then invests for 1 quarter. For this variable, we compared back test lengths of .25, .5, and 1 year.

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Backtests on these 54 permutations were run concurrently using historical volatility data and implied volatility data. For every period and combination of parameters, there is a unique result obtained from using each of the two different volatility data sets. Finally, this overall process was run twice – once for the VWO model and once for the VEU model.

5.4 Model Outputs

Both the VWO and VEU backtests produced 1,278 individual return records. Each record contains the portfolio weights recommended using the historical volatility data and the weights recommended using implied volatility. The resulting portfolio return, standard deviation, and Sharpe ratio derived from each volatility type are automatically calculated from historical data and included in the record. Finally, the portfolio return, standard deviation, and Sharpe ratio of the equal- weighted benchmarks during the time period are also included with each record. The benchmarks for the VWO and VEU models just differed in which foreign stock ETF was used.

In order to interpret this large amount of data, our team broke down the back test into each of the separate permutations and calculated the average return and the average Sharpe ratio for each combination of parameters. With this data we calculated total average returns and total average Sharpe ratios for both types of volatility data and for both the VEU and VWO models.

5.5 Data Interpretation

Some interesting information can be gleaned from and back-tested data above:

• Optimized portfolios outperformed the equal-weighted benchmarks both in terms of average returns and average Sharpe ratios. This is true for both asset models and both volatility measures. • The average returns from implied volatility models were about twice as large as the average returns from historical volatility models and the benchmark returns. • The average Sharpe ratios did not differ much when comparing which type of volatility was used in the optimization back-test. • The average Sharpe ratios from both the VEU and the VWO models outperformed their respective benchmarks by about 20%. • The average Sharpe ratios from the VEU model were about 10% higher than the ratios from the VWO model. • No single permutation simultaneously achieved the highest average return and highest average Sharpe ratio. Parameter selection is discussed in more detail below.

Implied Volatility Results vs. Historical Volatility Results

VWO - Implied Volatility vs. Historical Volatility Returns Sharpe Ratio Average Returns Average Sharpe Implied Volatility 57% 47% 93% 43% Historical Volatility 43% 53% 7% 57%

VEU - Implied Volatility vs. Historical Volatility Returns Sharpe Ratio Average Returns Average Sharpe Implied Volatility 61% 50% 91% 59% Historical Volatility 39% 50% 9% 41%

In addition to the large tables of average returns and average Sharpe ratios that we constructed for each individual permutation, our team quantified how many times allocations from historical volatility models outperformed allocations from implied volatility models and vice versa.

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Overall, implied volatility models clearly produced higher returns than historical volatility models. However, the results were split between the VEU and VWO models for better Sharpe ratios. Using average measures for each permutation helped to highlight the differences between the volatility measures. In conclusion, implied Volatility looks better, but inconclusive overall.

Model Backtest Returns vs. Benchmarks

VWO - % of Periods of Outperformance vs. Benchmark Returns Sharpe Average Returns Average Sharpe Implied Volatility 58% 50% 100% 91% Historical Volatility 44% 16% 52% 69%

VEU - % of Periods of Outperformance vs. Benchmark Returns Sharpe Average Returns Average Sharpe Implied Volatility 60% 51% 100% 100% Historical Volatility 44% 49% 57% 70%

Next, we compared the results from the two different volatility models to their respective benchmarks. From the observations above, we concluded that optimized portfolios constructed using implied volatility were more consistently able to outperform their respective benchmarks on a risk-adjusted basis than portfolios optimized through historical volatility. In conclusion, implied volatility outperforms the benchmark more often on an absolute and risk-adjusted basis.

VEU vs. VWO

Below is a table comparing which model, VEU or VWO, outperformed the other for a greater percentage of periods. It looks at both the individual records (all 1,278 records) and the average calculations for each of the 54 permutations. Each “average” measure is the arithmetic average of returns and Sharpe ratios, respectively, for each individual permutation.

When looking at the individual measures, neither model stands out versus the other. However, once returns and Sharpe ratios are averaged for each of the back-test permutations, it becomes clear that the VEU portfolios are superior. We believe the average returns present a more accurate picture. The average returns are compiled over 25 quarters of data and are more representative of longer term performance. They also take into account the magnitude of difference in outperformance, while the single comparisons do not.

% of Periods Outperformance - VEU vs. VWO Average Average Volatility Data Model Return Return Sharpe Sharpe Implied Volatilty VEU 52% 65% 50% 100% Implied Volatility VWO 48% 35% 50% 0% VEU - Implied Volatility vs. Historical Volatility Returns Sharpe Ratio Average Returns Average Sharpe Implied Volatility 61% 50% 91% 59% Compared to the VWO implied volatility model, the VEU implied volatility model had higher average absolute performance, Historical Volatility 39% 50% 9% 41% 1.04% vs. 1.00%, and outperformed its index across all permutations by a higher margin – 58 basis points vs. 54 basis points. The average Sharpe ratio for the VEU model outpaced VWO’s average Sharpe ratio .124 to .109 and outpaced its benchmark’s average Sharpe ratio by a higher margin: .024 to .02.

In terms of individual records, neither the VWO nor VEU implied volatility models stood out versus each other in terms of number of periods of return or Sharpe ratio outperformance. Those results were pretty even, with VEU holding a slight edge

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in terms of return. However, when the returns for each permutation are averaged up, the VEU implied volatility model has the higher return 65% of the time and the higher Sharpe ratio 100% of the time.

Due to the outperformance in total return and risk-adjusted return that the VEU model exhibited in back-testing versus the VWO model, our team made the decision to swap out the Emerging Markets ETF for the All-World Ex-US ETF.

It should be noted that there are individual permutations of the VWO implied volatility model that exhibit higher average returns than their matching VEU permutation. However, none of those higher average returns are accompanied by higher average Sharpe ratios. We felt that choosing a model based on the best performing permutation would be cherry-picking results. Instead we focused on the performance of each model over all permutations as expressed by total average returns and average Sharpe ratios, as we believed it would give a more complete picture of how the model will perform in the future. In conclusion, VEU model outperforms VWO model.

Permutations and Parameters

In performing these back tests, our team contemplated which permutation of the model to choose to invest with. The VEU model was superior to the VWO model and results from implied volatility models clearly outperformed results from historical volatility models. However, there was no clear “winner” in terms of which combination of parameters to use.

The highest average Sharpe ratio, .157, came from the 1/10%/50%/0.25 combination, but its average return was only 1.42%. The highest average return was 1.88% from the 2/5%/50%/0.25 combination and its Sharpe ratio was just .002 lower. Instead of looking at the average return and average Sharpe data for each permutation to make a decision, our team decided to perform a second experiment on our data to determine which individual parameters accounted for highest average returns and Sharpe ratios.

In our next experiment, we held each individual parameter constant while varying all the other parameters and measured average returns and Sharpe ratios. Those measurements were then used to compare the parameters against other choices in their category and to other categories. A standard deviation for each Sharpe ratio was calculated to gauge how much variance existing within each parameter category. The results surprised us.

Los Angeles company visits, August 2013

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Backtest Time (in years) St. Deviation 0.25 0.5 1 Average Return 1.30% 0.63% 0.44% Average Sharpe 0.151 0.12 0.098 2.6627%

Type of Volatility Implied Vol Historical Vol Benchmark Average Return 1.04% 0.54% 0.46% Average Sharpe 0.124 0.122 0.1 1.3317%

Minimum Constraint 5% 10% Average Return 0.84% 0.74% Average Sharpe 0.126 0.119 0.4950%

Max Constraint 50% 40% 30% Average Return 0.83% 0.82% 0.72% Average Sharpe 0.126 0.123 0.119 0.3512%

Lambda 2 3 1 Average Return 0.82% 0.77% 0.78% Average Sharpe 0.125 0.122 0.121 0.2082%

The most important variable in our back-tests was the length that the model looked back when constructing its allocations. Permutations with quarterly look-back outperformed all other portfolios in terms of both absolute and risk-adjusted returns. They also outperformed other choices in their category by the widest margin.

We used the standard deviations of the average Sharpe ratios to rank which categories exhibited the greatest variability among their constituents. Back-test length and volatility type stood out as variables that produced the most variability in returns and Sharpe ratios. The minimum and maximum constraints produced modest variation. Finally, differences in the lambda value proved to produce almost no difference in returns, risk-adjusted or otherwise.

Armed with this data on which individual parameter in each category resulted in the highest average return and Sharpe ratio, we made the decision to use best performing value for each independent variable as our model’s parameters. This resulted in optimized parameters of 2/5%/50%/quarterly/ implied volatility. That is the combination of parameters that also happened to have the highest return and second highest Sharpe ratio in our analysis of the different permutations. In conclusion, optimal model parameters are a 2 lambda, quarterly look-back at data, 5% minimum constraint, 50% maximum constraint using the implied volatility version of the VEU model.

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5.5 Return

For the back-testing period of May 9, 2007 to September 20, 2013, the specified model generated a cumulative return of 44.94%, while the benchmark achieved a cumulative return of just 6.42%, an outperformance of 38.52%. The chart below shows the theoretical performance of the TAA portfolio versus the benchmark.

Model Model C Cumula+ve umulative Retur n Return vs. Bench m vs. ark Benchmark 60% 50%60% 40%50% 30% 40% 20% 30% 10% 20% 0% -10% 10% -20% -30%0% -40%-­‐10%

-­‐20% 2/1/2011 5/1/2011 8/1/2011 11/1/2011 2/1/2013 2/1/2012 5/1/2013 5/1/2012 8/1/2013 8/1/2012 2/1/2010 5/1/2010 8/1/2010 11/1/2012 11/1/2010 5/1/2007 8/1/2007 2/1/2009 2/1/2008 5/1/2008 5/1/2009 8/1/2008 8/1/2009 11/1/2007 11/1/2009 -­‐30% 11/1/2008 Model Cumulative Return Benchmark Cumulative Return -­‐40%

Model CumulaSve Return Benchmark CumulaSve Return 5/1/2007 8/1/2007 2/1/2008 5/1/2008 8/1/2008 2/1/2009 5/1/2009 8/1/2009 2/1/2011 5/1/2011 8/1/2011 2/1/2010 5/1/2010 8/1/2010 2/1/2012 5/1/2012 8/1/2012 2/1/2013 5/1/2013 8/1/2013 11/1/2007 11/1/2008 11/1/2009 11/1/2010 11/1/2011 11/1/2012

Location name visit, November 2013

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13 6. Risk Analysis

Below is a graph of annualized standard deviations for TAA, the benchmark, and the S&P 500 index. During the summer months, the portfolio had a lower annualized standard deviation. This makes logical sense as there was a large allocation to BND over the summer. During the academic school year (as opposed to the summer), we can see that the standard deviation of TAA deviates from that of the benchmark and more closely follows that of the overall stock market. Please note that the vertical lines only approximate the rebalance dates. Lastly, it is evident that the standard deviation of the equities market is lower than in an average year. The year saw little volatility and near linear growth of the stock market.

Annualized Standard Devia+on 20.0% 15.0% 10.0% 5.0% 0.0%

TAA Benchmark Total Stock Market

NIBC Competition Graduate Division Winner, January 2014 Need name of competition, etc., May 2014

13 The charts in the Risk Analysis section use the original benchmark that includes the VWO ETF instead of the VEU ETF and are through 4/11/14.

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6.1 Risk Adjusted Returns

In order to accurately compare the risk of each portfolio, the Sharpe ratios for each month are shown below. The values are calculated based on monthly return and standard deviation data (as opposed to annualized). The risk adjusted return characteristics of both the model portfolio and the benchmark are very similar. The total stock market had higher Sharpe Ratios. However, it is important to keep in mind that the intention of TAA is to allow for diversification and as such, is not intended to outperform the equities markets on a total return basis. It is also important to note that this past year has seen tremendous performance in the equities markets. Thus, in a down year, presumably the Sharpe Ratio of TAA could potentially be higher than that of the equities markets.

Monthly Sharpe Ra+o 100% 80% 60% 40% 20% 0% -­‐20% -­‐40%

TAA Benchmark Total Stock Market

7. Timeline

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8. Trading

8.1 Current Holdings

As of April 30, 2014 the TAA portfolio’s market value was $217,035.27. The current holdings are 5 ETFs: VTI (Vanguard Total Stock Market ETF), BND (Vanguard Total Bond Market), VNQ (Vanguard REIT Index ETF), DBC (Powershares DB Commodity Index Tracking), VEU (Vanguard FTSE All-World ex-US ETF), and cash.

Symbol Name Quantity Price Market Value % of Portfolio DBC PWRSH CMDTY INDEX ETF 406 26.4 10,722.46 4.94% VEU VNGRD FTSE ALL-WLD EX-US ETF 214 51.1 10,924.70 5.03% VNQ VNGRD REIT INDX ETF 1,503 72.9 109,628.82 50.51% BND VNGRD TTL BD MKT ETF 129 81.7 10,532.85 4.85% VTI VNGRD TTL STK MKT ETF 759 97.5 74,025.27 34.11% Cash 1,201.17 1 1,201.17 0.55% Total 217,035.27

8.2 Trading Schedule

Our team assumed control of a fully invested portfolio and utilized the TAA model to rebalance the portfolio 4 times over the 2013-2014 year. We also received a cash transfer in October, 2013, which was invested in the same allocation from our September allocation. The trade details are below.

On each quarterly rebalancing date, the team ran the optimization model with the most recent 3 months of data to generate a new portfolio allocation recommendation. Our goal was to execute all the trades as close together as possible to minimize the effect of market movements during the rebalance. We used market orders for their execution certainty. In some cases, large orders were broken down into smaller share amounts. All trades were executed electronically via our broker, Wedbush.

The portfolio’s next rebalance is scheduled for June 25, 2014.

Model Target Allocations 2013 2014 Original June - September - December - March - Portfolio September December March June Symbol Name Allocation Allocation Allocation Allocation Allocation DBC PWRSH CMDTY INDEX ETF 5% 5% 5% 5% 5% VWO VNGRD FTSE EMG MKTS ETF 5% 5% 50% - - VNQ VNGRD REIT INDX ETF 35% 5% 5% 5% 50% BND VNGRD TTL BD MKT ETF 5% 35% 5% 5% 5% VTI VNGRD TTL STK MKT ETF 50% 50% 35% 50% 35% VEU VNGRD FTSE ALL-WLD EX-US ETF - - - 35% 5%

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9. Performance14

Overall, the TAA model outperformed its blended benchmark by 108 basis points over the 2013-2014 class year. However, performance was uneven. TAA outperformed for 6 months, underperformed for 5 months, and essentially tied the index for one month.

ETF Performance For Each Rebalance Period June - September - December - ETF September December March March - June DBC 1.30% -0.39% 1.01% 1.89% VWO 11.36% -1.87% -1.60% 3.67% VNQ 1.72% -1.28% 8.93% 4.26% BND 1.03% 0.31% 1.43% 0.99% VTI 8.16% 8.11% 2.77% 0.44% VEU 12.34% 2.54% -0.29% 3.70% Total Portfolio 5.16% 1.83% 1.85% 2.61% Benchmark 4.71% 0.98% 2.77% 2.26% Outperformance 0.45% 0.86% -0.92% 0.36%

Our team rebalanced four times over the year. Three out of those four portfolios outperformed its benchmark.

June Rebalance

• 45 basis points outperformance. • Outperformance due to 50% allocation to US stocks, up 8.16% during the period. However, an underweight allocation of 5% to emerging markets stocks detracted from performance as that asset class surged up 11.36% over the summer

September Rebalance

• 86 basis points outperformance. • A 35% allocation to US Stocks helped drive performance as VTI gained 8.11% over the period. However, a 50% allocation to the worst performing asset class, emerging market stocks, erased most of those gains. • On balance, the benchmark was allocated 80% to asset classes that either fell or only gained 30%, while the TAA model was only 65% allocated to those asset classes, accounting for this period’s outperformance.

December Rebalance

• 92 basis points underperformance. • The model and benchmark moved from VWO to VEU. VEU outperforms VWO all periods going forward. • During this period, the model missed out on the best performing asset class, REITS, by assigning a weight of just 5%.

14 The Benchmark in the Performance section is a blend of the original benchmark, containing VWO, and the new benchmark, containing VEU. The benchmarks change on 12/23/13, the day that the TAA portfolio invested in VEU.

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• The 50% weight to US stocks was moderately positive as stocks were the second best performing asset class. The 35% allocation to global stocks was a loser as it was the worst performing asset class from the new selection.

March Rebalance

• Outperformed by 36 basis points through April 30. • Has a 50% allocation to the top performing asset class, REITS. • Its 35% allocation to US stocks and underweight allocations to all other sectors are detracting from performance.

10. Recommendations for the 2014 TAA Team

To continue the development effort on the TAA model, we suggest the following additional steps to continue to refine the model. These include improving the data, additional back-testing, and expanding the model into more asset classes.

10.1 Improve data in the model

The data in the model has a number of shortcomings. First, the full set of data only goes back to 2007, which is a relatively short time period for financial series data. Second, the return data does not include dividends. This causes the model to systematically underweight asset classes such as bonds and REITS that derive more of their return from dividends than from price appreciation.

To address these two issues, our team tried to build a model using the indexes the ETFs in the portfolio track. We ran into problems when we discovered that a few of the indices have even shorter histories than the ETFs that purport to track them However, longer term indices do exist. It is our opinion that indices that are highly correlated to the individual ETFs can be tested and utilized to expand the data set.

10.2 Improve the Back-Testing Methodology

The current back-test is actually very dependent on the date ranges that are set. For instance, when the back-test is run, it calculates portfolios every three months. Optimally, it would generate three month portfolios at a higher frequency. For example, a back-test that generated a portfolio every Monday over the course of the historical data would be more reliable than the current back-test.

There are also many additional parameters that can be tested. One candidate would be monthly re-balancing, as rebalancing frequency was found to have such a large effect. Another is testing half-point increments of lambda.

10.3 Additional Asset Classes

Although the current five ETFs encompass many of the world’s asset classes, we believe that there is room to both add additional asset classes and to divide existing asset classes into their components. Examples of new asset classes not represented in the portfolio are international real estate, international fixed income, and cash. For the equity positions, US stocks could be broken down into capitalization ranges and foreign stocks could be separated into developed and emerging buckets. The BND ETF could be broken down into different treasury maturities, TIPs, high yield, world debt, etc. The commodities allocation could be divided into agriculture, precious metals, industrial metals, and energy. Adding more asset class would further diversify the portfolio and most likely also mitigate the current tendency of the model to concentrate in a single ETF.

34 UCLA Anderson Student Asset Management Annual Report | 35

THE EARNINGS ANNOUNCEMENT RETURN (EAR) STRATEGY OVERVIEW

This year the EAR team back-tested two research papers premised upon the earnings announcement return phenomena. The first was based on the research paper by Brandt, Kishore and Santa-Clara and the second was authored by Savor and Wilson. We knew we had a long year ahead of us after taking over this strategy from the previous class as EAR had not traded last year due to back-testing difficulties. As a result, the funds earmarked for the strategy were put in a place-holder short-term bond index fund. During our school year, a decision was made to reallocate the funds to the other three strategies, as short term fixed income had very little upside return prospects at the time.

Although we made significant strides, the back-testing of the Brandt paper proved to be extremely complex and the results were too inconsistent for the strategy to proceed further into implementation. Fortunately, later in the year our advisor Professor Robert Geske introduced the EAR team to a more recent research paper by Savor and Wilson that was also related to earning announcement returns. This paper shows promise for further testing and possible implementation by the incoming ASAM class.

STRATEGY #1 OVERVIEW – Earnings Announcements Are Full Of Surprises

The strategy attempts to outperform a given index by exploiting the phenomenon known as Post Earnings Announcement Drift (PEAD).15 The strategy is based on the theory that the movement of a stock’s price around the earnings announcement window is in itself predictive of future price movements. This phenomenon, which is well documented though not well explained, asserts that stock price performance tends to follow the direction of the “surprise” contained in a company’s earnings release.16

While other strategies such as Standardized Unexpected Earnings (SUE) attempt to explain PEAD as the “surprise” resulting from the difference between a firm’s realized earnings and the market’s expectations of earnings, this paper suggests that these are complementary rather than contradictory approaches. Some papers have attempted to predict future beneficiaries of PEAD price movement using other information contained in earnings reports, including common financial metrics such as P/E ratios, reported changes in sales or profit margins, and liquidity ratios, while others have studied the effect of metrics such as order backlog and patent counts.

EAR’s approach differs from prior research in that the strategy is primarily concerned with how the market reacts to the information presented in earnings announcements, as measured by stock price movement in a prescribed timeframe, rather than what information may be responsible for it. The paper suggests that while it is difficult to isolate what information may cause investors to expect a company’s earnings to grow, the movement of a stock’s price is in itself predictive of future price movements. Specifically, Brandt et al. assert that these predictive price movements take place in the three day “window” beginning one trading day before the company’s announcement through one day after the announcement.

If investors initially underreact to new information presented in corporate earnings reports, even those which produce abnormal returns in the firm’s “window,” it is possible to quickly purchase (sell) these stocks and generate alpha going

15 Earnings Announcements are Full of Surprises by Michael Brandt, Runeet Kishore, Pedro Santa-Clara, Mohan Venkatachalam; June 2007

16 Ray Ball and Philip Brown first introduced PEAD in “An Empirical Evaluation of Accounting Income Numbers,” a paper written in 1968 which presented the concept and implicitly questioned the theory of market efficiency. Victor Bernard and Jacob Thomas approached the topic with papers in 1989 and 1990 which explained PEAD as the result of autocorrelation arising from the mismatch of seasonality. Bernard and Thomas summarized the two schools of thought on PEAD as 1) price response to new information is delayed due to either the failure of the trader to aggregate all available information or because transaction and opportunity costs may exceed the gains from immediately executing trades on the new information and 2) there are additional risks not captured by asset pricing models that can explain the abnormal returns.

35 36 | UCLA Anderson Student Asset Management Annual Report

forward. EAR “window” returns can be divided into quintiles to determine the degree of performance abnormality. Stocks that fall in the top quintile should be purchased immediately while, conversely, stocks in the bottom quintile should be sold immediately. The paper suggests that outsized returns may be possible over 30, 60, 90, 120, 180, and 240 days after the initial earnings announcement. Research in the paper also suggests that this outperformance is greatest for companies with smaller market capitalizations, which ostensibly are less followed by investors and analysts. In summary, the research of Brandt et al. should produce return patterns depicted in Exhibit 1. As ASAM is restricted from short selling, the focus of the EAR strategy is to capture the excess returns from taking long positions in companies that meet the EAR investment criteria.

BACK-TESTING ASAM introduced the EAR strategy in 2010. The previous class was provided some guidelines and benchmarks for testing the EAR strategy, but struggled to calculate return data necessary to prove or disprove the EAR strategy due to certain technological and practical limitations. Given that most companies announce earnings on unique dates, and subsequent announcements do not exactly repeat on ninety day intervals, tens of thousands of stock and benchmark data points were required to populate the sample necessary for testing the strategy. Each of these prices would have to be manually considered to produce quarterly EAR “window” returns for each stock and then cross-referenced to correspond with their appropriate fiscal quarter. Although the strategy had traded in prior years, the previous class determined that more in-depth testing needed to be conducted to rationalize further investment in the strategy. The 2014 Class relied on SAS programming to manipulate the large and complex data set that was needed for testing.

Methodology

To reduce the complexity of tracking every company’s earnings announcement throughout the quarter we narrowed the scope to two days of earnings announcement within each quarter for back testing. We used 10 years of earnings data encompassing the second quarter of 2002 to the second quarter of 2012. For both simplicity and consideration of implementation constraints, we selected two dates per quarter. For consistency, we selected the following dates of earnings announcements:

Wednesday of the 2nd week of the 2nd month of an earnings quarter (February, May, August, November)

Wednesday of The last full week of the 1st month of an earnings quarter (January, April, July, September)

36 UCLA Anderson Student Asset Management Annual Report | 37

We did not exclude any industries since the sample size was already limited due to our self-imposed restriction to trading only two specific dates per quarter. Our rationale for doing this was that we could realistically trade for those few days a quarter if the back-testing proved that the strategy would produce reliable returns.

We analyzed the 3-day return around the earnings announcement of these companies and sorted them into quintiles, from highest to lowest return. We also tracked these specific stocks 90 days out to see if Quintile-1 outperformed Quintile-2, etc. in hopes of finding a monotonic relationship across all quarters for 10 years. We used the IBES database with consideration to the Russell 3000 universe. The daily price quotes of these companies came from CRSP database.

Summary of SAS Code

1. Aggregate data from CRSP and I/B/E/S for daily security prices and earnings announcement of the entire CRSP universe 2. Create a macro to capture all the companies that announce earnings in any particular day and the respective stock prices one day before, one day after, two days after and ninety two days after the actual earnings announcement 3. Using the security prices from the above macro, the 3-day and 90-day returns for the companies that report earnings on any given date are calculated 4. The universe is now ranked from highest to lowest based on the 3-day return and broken into quintiles using rank command in SAS 5. For each of the quintiles, the 90-day return is calculated and checked for a monotonic relationship

Back-Test Data Results

Week 4 of the 1st month of each quarter

90-­‐Day Return(2002-­‐2012 Average) Poly. (90-­‐Day Return(2002-­‐2012 Average))

2.50%

1.50%

0.50% 90 day return

-­‐0.50% 1 (Highest 3-­‐Day 2 3 4 5 (Lowest 3-­‐Day return) return) 3-­‐Day Return Quin+les

Location name visit, July 2013 Location name visit, December 2013

37 38 | UCLA Anderson Student Asset Management Annual Report

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5

ReturnQuintile Quintile 1 1.96%Quintile 1 Quintile 2 0.87%Quintile 2 Quintile 3 0.85% 3Quintile Quintile 4 -0.14% 4Quintile Quintile 5 0.93% 5

Return StdRetu. Devrn . 1.96% 12.66%1.96% 0.87% 10.45%0.87% 0.85% 0.85%9.66% -0.14% 10.55%-0.14% 0.93% 12.84%0.93%

StdQuarterly . DevSharpeStd. . Dev Ratio. 12.66% 12.66%0.16 10.45% 10.45%0.19 9.66% 9.66%0.20 10.55% 10.55%0.19 12.84% 12.84%0.15

Quarterly Quarterly Sharpe RatioSharpe Ratio 0.16 0.16 0.19 0.19 0.20 0.20 0.19 0.19 0.15 0.15

Return 7.85% 3.50% 3.40% -0.57% 3.72%

Return StdReturn. Dev . 7.85% 25.31%7.85% 3.50% 20.90%3.50% 3.40% 19.32%3.40% -0.57% -21.11%0.57% 3.72% 25.67%3.72% Annual Std. DevSharpeStd. . Dev Ratio. 25.31% 25.31%0.31 20.90% 20.90% 0.38 19.32% 19.32%0.41 21.11% 21.11%0.37 25.67% 25.67%0.31 Annual Annual Sharpe RatioSharpe Ratio 0.31 0.31 0.38 0.38 0.41 0.41 0.37 0.37 0.31 0.31

Week 2 of the 2nd month of each quarter Week 2 Weekof the 22 ndof monththe 2nd of month each ofquarter each quarter 90 Day Return(2002 -­‐ 2012 Average) Poly. (90 Day Return(2002 -­‐ 2012 Average))

90 3.00% Day 90 Return(2002 Day Return(2002 -­‐ 2012 Average) -­‐ Poly. 2012 Average) (90 Poly. Day (90 Return(2002 Day Return(2002 -­‐ 2012 Average)) -­‐ 2012 Average))

3.00% 2.50%3.00%

2.50% 2.00%2.50%

2.00% 1.50%2.00%

1.50% 90 day return 1.00%1.50% 90 day return 90 day return 90 day return 90 day return 1.00% 0.50%1.00%

0.50% 0.00%0.50% 1 2 3 4 5 0.00% -0.50%0.00% 1 1 2 2 3-Day3 Return 3Quintiles 4 4 5 5 -0.50% -0.50% 3-Day Return3-Day Quintiles Return Quintiles

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Error! Not a

Return QuintileQuintile 12.67% Quintile1 Quintile 2- 0.35% 2 Quintile Quintile 3 0.11% Quintile3 Quintile 41.07% Quintile4 Quintile 51.42% Error! 5 Error! valid Not a link. Not a

Return StdReturn. Dev . 2.67% 17.89%2.67% -0.35% 14.68%-0.35% 0.11% 15.05%0.11% 1.07% 12.94%1.07% 1.42% 17.70%1.42%valid link.valid link.

Quarterly Std. DevSharpeStd. . Dev Ratio. 17.89% 17.89%0.15 14.68% 14.68%0.18 15.05% 15.05%0.18 12.94% 12.94%0.21 17.70% 17.70%0.15

Quarterly Quarterly Sharpe RatioSharpe Ratio 0.15 0.15 0.18 0.18 0.18 0.18 0.21 0.21 0.15 0.15

Return 10.68% -1.38% 0.45% 4.27% 5.68%

Std. Dev. 35.78% 29.36% 30.10% 25.88% 35.39% Return Return 10.68% 10.68% -1.38% -1.38% 0.45% 0.45% 4.27% 4.27% 5.68% 5.68% Annual Std. DevSharpeStd. . Dev Ratio. 35.78% 35.78%0.30 29.36%29.36% 0.36 30.10% 30.10%0.35 25.88%25.88% 0.41 35.39% 35.39%0.30

Annual Annual Sharpe RatioSharpe Ratio 0.30 0.30 0.36 0.36 0.35 0.35 0.41 0.41 0.30 0.30

UCLA Anderson Student Asset Management Annual Report | 39 38 38 38

Back-Test Findings and Conclusions

The results from the above charts indicate that the top quintile outperforms the other quintiles, consistent with what the Brandt et. al research paper suggests. However, the data does not demonstrate a monotonic downward trend in the 90-day return as we move down the quintiles. We see a clear outperformance in Quintile-1 but surprisingly Quintile-5 is the second best performer. One hypothesis for this may be that the market overreacts to bad news in the first 3 days of the announcement and that this leads to a longer term “adjust-to-reality” for the 90-day holding period, thereby the 3-day lowest performing quintile shows relatively strong returns.

The data above suggests we can earn high returns by investing long in Quintile-1. Unfortunately, even though the 10 year aggregate number shows outperformance for Quintile-1, we could not see the same trend for each of the 40 quarters we back-tested. Only 22 of the 40 quarters showed this trend—the other quarters had mixed results. Accordingly, there is only a 55% chance that the strategy would work. Another result to note is that the risk adjusted returns as defined by the Sharpe ratios are smallest for Quintile-1 and Quintile-5. Since we have not been able to prove the strategy to work consistently nor can we explain why the Sharpe Ratios for the top and bottom quintiles was small in relation to the other quintiles, we could not move forward with this strategy.

STRATEGY #2 OVERVIEW – Earnings Announcements And Systemic Risk

There have been several studies done in the past surrounding the earnings announcement premium. While the premium was first discovered by William Beaver in 1968, academic research had not found an explanation for the premium as based on systemic risk until a recent publication.17 Pavel Savor and Mungo Wilson recently published a paper in December of 2013 entitled Earnings Announcements and Systemic Risk.

Savor and Wilson’s proposed strategy is based on the premise that firms earn high returns at times of earnings announcements as compensation to the investor for holding non-standard risks. The strategy relies on the theory that a portfolio that invests in all firms expected to report earnings in a given week and shorts non-announcing firms should earn an annualized abnormal return of 9.9% with a Sharpe ratio that is significantly higher than that of value and momentum strategies. This premium appears to be consistent across different time periods, not restricted to small cap stocks, and does not depend on choice of asset pricing model.

Announcement risk is a proxy for aggregate cash flow risk and investors use announcements to revise their earnings expectations for non-announcing firms, but may do so imperfectly. Firms report earnings each quarter, the timing of which is known in advance and differs across firms. Investors use individual firm announcements to update their expectations about aggregate market earnings. Announcers may be especially risky since it can be observed that the covariance between firm-specific and market cash-flow news spikes around the announcements.

The authors of the paper provide a risk based explanation based on two ideas. First, a firm’s earnings announcement contains valuable information related to other firms in its industry as well as to the general economy. Investors face a signal extraction problem in the sense that they must extract what cash-flow news in the announcement is applicable to aggregate market cash flows. This spillover of cash flow news to the market in general results in a high conditional covariance between firm and market level cash flow news, resulting in a high risk premium for the announcing firm. Non-announcing stocks

17 Some have suggested that the premium exists due to arbitrage limitations (Cohen, Dey, Lys, and Sunder, 2007) while others sought to explain it by limitations to investor attention (Frazzini and Lamont, 2007).

39 40 | UCLA Anderson Student Asset Management Annual Report

respond to the news in announcements, but their stock prices are less responsive to the news because investors learn less about these firms.

The second explanation provides that, in addition to expected future cash flows, the firm’s return is also affected by discount-rate news. If there is high correlation between discount-rate news across firms, then the market betas will mainly reflect covariance between firm- and market-level discount rate news, therefore an announcing firm may have higher fundamental risk than the market even after controlling for its market beta. Although a firm’s market beta may rise on the day it announces earnings, the increase in its expected return will be larger than can be explained just by its higher beta. Because of this, we can expect a positive announcement return even if the actual earnings surprise is zero.

The Authors conclude that the sensitivity of non-announcing firms to announcements increase with time elapsed since the non-announcer’s own last announcement. They further find that announcement risk premium is persistent such that those with high (low) historical announcement returns continue earning high (low) returns on future announcement dates. Different firms have different exposure to announcement risk and it is likely that this characteristic does not change frequently. Finally, we see that the market responds more strongly to the announcements of large firms that have low idiosyncratic volatility around past announcements, most likely because investors can more easily infer the common component of those firms’ earnings surprises. Note that these large firms do not necessarily earn a higher premia, which demonstrates that we cannot predict a monotonic relation between how much investors learn from a particular firm’s announcement and expected returns.

Higher returns to the announcement portfolio may be earned when firms announce earlier in the quarter. The paper indicates that there is more information to be extracted by firms announcing earlier, when less is known about aggregate earnings. In addition, higher returns may also be gained when there areMore firms announcing. The response to the announcement portfolio return should be stronger when there are more firms announcing quarterly returns, since this provides a more precise signal of aggregate cash flow news.

As this this paper was released very recently, we have only begun to back test the strategy as revised and informed by this new paper. We are sharing our preliminary results with the ASAM Class of 2015, and hope that they will benefit from this research and modelling in an effort to develop an actively traded strategy.

BACK-TESTING

Methodology

Authors of the paper claim that earnings announcements generate a risk, which should be rewarded. The implementation is based on the idea of buying stocks on Monday, which will make earnings announcements this week. Then an investor should hold those stocks till the end of Friday. Savor and Wilson claim that the long position makes up to 18.3% of an annual excess return.

According to the paper the premium is consistent across different periods, not restricted to small stocks, and doesn’t depend on asset pricing models. It means that this strategy can earn alpha, even with Fama-French three factor model. Savor and Wilson found that the Sharpe ratio for the value-weighted (equal-weighted) long-short earnings announcement portfolio is 0.112 (0.055), which is higher than the market (0.049), value portfolio (0.076), and momentum portfolio (0.072).

40 UCLA Anderson Student Asset Management Annual Report | 41

Summary of Constraints

The team decided to modify the strategy to make it more easily testable and implementable:

• Increase the holding period from one week to twelve week. Summary• Increase of Constraintsthe holding period from one week to twelve week. • Exclude all small-cap and mid-cap companies. We found these stocks to have higher bid-ask spreads and thus higher The teamtransaction decided costs.to modify Since the this strategy strategy to hasmake high it more turnover easily (about testable 400% and a implementable: year), it would be cost prohibitive. • Exclude mega-cap companies. We found these stocks to be too efficient for our purposes as they did not generate an • acceptableIncrease the risk holding-adjusted period announcement from one week premium, to twelve which week. was demonstrated by the lower Sharpe Ratios of mega-cap • Excludestocks. all small-cap and mid-cap companies. We found these stocks to have higher bid-ask spreads and thus higher transaction costs. Since this strategy has high turnover (about 400% a year), it would be cost prohibitive. • Exclude mega-cap companies. We found these stocks to be too efficient for our purposes as they did not generate an acceptable risk-adjusted announcement premium, which was demonstrated by the lower Sharpe Ratios of mega-cap stocks.

Download quarterly Download quarterly financial data and daily Adjust stock prices for financial data and daily Select NYSE/NASDAQ/ Calculate market cap of returns data from the stockAdjust splits stock and prices reverse for SelectAMEX NYSE/NASDAQ/ stocks stock splits and reverse Calculatethe companies market cap of CRSP/Compustatreturns data from Merged the AMEX stocks the companies CRSP/Compustat Merged splits database splits database

Download quarterly financial data and daily Adjust stock prices for Select NYSE/NASDAQ/ stock splits and reverse Calculate market cap of Excludereturns dataall companies from the AMEX stocks the companies withCRSP/CompustatExclude a market all companiescap lessMerged than splits with a market cap less than Merge Fundamental data $5 bil anddatabase more than $20 Merge Fundamental data $5 bil and more than $20 with 12-week return data Calculate 12-week returns bil Add P/E, P/B and CF/P with 12-week return data bil using unique Historical Calculatefor each stock,12-week based returns on Add P/E, ratiosP/B and CF/P for each stock, based on ratios CRSPusing PERMNOunique Historical Link to CRSP PERMNO Link to adjjusted prices COMPUSTAT Record adjjusted prices COMPUSTAT Record Exclude all companies with a market cap less than Merge Fundamental data $5 bil and more than $20 with 12-week return data bil Calculate 12-week returns Add P/E, P/B and CF/P for each stock, based on ratios using unique Historical CRSP PERMNO Link to adjjusted prices COMPUSTAT Record Calculate a standard Calulate average returns deviationCalculate of returnsa standard and a ofCalulate 12-week average holding returns period deviation of returns and a of 12-week holding period Sharpe ratio Sharpe ratio

Calculate a standard Calulate average returns deviation of returns and a of 12-week holding period Sharpe ratio

Back-Test Data Results

Back-testing the strategy the team realized that the turnover would be extremely high in the case of the weekly trading. Weekly trading translates to about 2900 stocks needing to be purchased every quarter. We decided to narrow our scope Backwhile -hopingTest Datato still Results find an earnings announcement premium. We tested an extended holding period of 12 weeks. Surprisingly, the performance was not muted. This hints that the earnings announcement risk premium is persistent for at leastBack- 12testing weeks the after strategy the announcement. the team realized that the turnover would be extremely high in the case of the weekly trading. Weekly trading translates to about 2900 stocks needing to be purchased every quarter. We decided to narrow our scope whileNext, hopingwe tested to stillfor otherfind an constraints earnings announcement in order to reduce premium. the number We tested of stocks an extended that would holding need toperiod be traded of 12 weeks.while still Surprisingly,earning a premium. the performance We found wasthat notthe muted.earnings This announcement hints that the risk earnings premium announcement to be stable riskfor large premium cap stocks, is persistent which for we at leastdefined 12 weeksto have after a market the announcement. capitalization between $5 billion and $20 billion.

Next, we tested for other constraints in order to reduce the number of stocks that would need to be traded while still earning a premium. We found that the earnings announcement risk premium to be stable for large cap stocks, which we 41 defined to have a market capitalization between $5 billion and $20 billion.

41 42 | UCLA Anderson Student Asset Management Annual Report

Performance, Risk Measures, and Number of Quarterly Trades: November 1998 to August 2012 Holding Constraints Average # Trades Annualized Annualized Annualized Period (per quarter) Returns Standard Sharpe ratio (weeks) Deviation

1 All CRSP 2908 19.40% 24.60% 0.67 universe

12 All CRSP 228 21.10% 26.40% 0.69 universe

12 Market Cap $5B 25 23.50% 31.20% 0.66 to $20B

Cumulative Returns: EAR (back-tested) and S&P 500 (ex-dividends) November 1998 to August 2012 (log scale):

EAR cumulative return vs S&P 500

100

10

1

0.1

S&P500 EAR

Back-Test Findings and Conclusions

We found that the 12-week holding period and a market cap restriction of $5 billion to $20 billion shows promise for implementation with the caveat that additional dates should be tested. With our selected dates, we found that a hypothetical investment of $100 into this strategy in November 1998 would translate to about $1,500 in August 2012. These promising results need to be further tested with out-of-sample data before any trading can be done. We recommend that the 2015 EAR team conduct additional back-testing.

42 UCLA Anderson Student Asset Management Annual Report | 43

FUNDAMENTAL INDEX STRATEGY

Overview The Fundamental Index strategy is based on the 2005 FAJ paper “Fundamental Indexation” by Robert Arnott, Jason Hsu and Philip Moore.21 The paper is not based on market capitalization weighted indices and instead proposes alternate weighting methods, which are based on firm fundamental characteristics. Market capitalization weighted portfolios have gained wide acceptance due to the Capital Asset Pricing Model (CAPM), which asserts that all investors will choose some combination of the market portfolio and the risk free asset in order to maximize risk adjusted returns in a mean-variance setting. The market portfolio is composed of all assets in the market and held based on their market value proportion.

Empirical research has shown that the market cap weighted portfolio does not appear to be the mean-variance optimal portfolio. Equilibrium arguments are involved and CAPM referred to is static; dynamic CAPMs can move toward equilibrium. Possible explanations of why fundamental weighted portfolios may be better portfolios include the following FUNDAMENTALassumptions: INDEX STRATEGY

• Stock prices are a noisy process and vary from their intrinsic value but tend to exhibit mean reversion over time. For Overview this reason, at any time t, some stocks will be overvalued and other stocks will be undervalued. The error term is The Fundamental Index strategy is based on the 2005 FAJ paper “Fundamental Indexation” by Robert Arnott, Jason Hsu symmetrical around the true value with an equal probability of being both under and overvalued. and Philip Moore.21 The paper is not based on market capitalization weighted indices and instead proposes alternate • A stock that is currently over (under) valued will be over (under) weighted in market cap weighted indices. The weighting methods, which are based on firm fundamental characteristics. Market capitalization weighted portfolios have market cap weighted index will overweight overvalued securities, while underweighting undervalued securities. The gained wide acceptance due to the Capital Asset Pricing Model (CAPM), which asserts that all investors will choose some larger the error term, the less appropriate the weight in the index. combination of the market portfolio and the risk free asset in order to maximize risk adjusted returns in a mean-variance Givensetting. these The marketflaws in portfolio market capitalization is composed portfolios,of all assets the in paperthe market proposes and heldthat creatingbased on a their portfolio market based value on proportion. fundamental weighting heuristics is a more effective alternative. Examples of fundamental metrics noted in the paper include Book Value, EmpiricalCash Flow, research Revenue, has Sales, shown Dividends that the and market Employment. cap weighted Using portfolio these types does ofnot heuristics appear to effectively be the mean removes-variance the optimaltendency of portfolio.market capitalization Equilibrium argumentsweighted portfolios are involved to overweight and CAPM overvalued referred to stocks is static; and dynamic underweight CAPMs undervalued can move towardstocks. The results equilibrium.of the authors’ Possible back-testing explanations indicate of thatwhy fundamentally fundamental weighted weighted portfolios portfolios may have be historically better portfolios outperformed include theirthe following market- assumptions:cap weighted benchmarks on both a total-return and risk-adjusted basis.

• Stock prices are a noisy process and vary from their intrinsic value but tend to exhibit mean reversion over time. For this reason, at any time t, some stocks will be overvalued and other stocks will be undervalued. The error term is symmetrical around the true value with an equal probability of being both under and overvalued. • A stock that is currently over (under) valued will be over (under) weighted in market cap weighted indices. The

market cap weighted index will overweight overvalued securities, while underweighting undervalued securities. The larger the error term, the less appropriate the weight in the index.

Given these flaws in market capitalization portfolios, the paper proposes that creating a portfolio based on fundamental weighting heuristics is a more effective alternative. Examples of fundamental metrics noted in the paper include Book Value, Cash Flow, Revenue, Sales, Dividends and Employment. Using these types of heuristics effectively removes the tendency of market capitalization weighted portfolios to overweight overvalued stocks and underweight undervalued stocks. The results of the authors’ back-testing indicate that fundamentally weighted portfolios have historically outperformed their market- cap weighted benchmarks on both a total-return and risk-adjusted basis.

21 Arnott, Robert D., Jason C. Hsu, and Philip Moore, 2005. "Fundamental Indexation”, Financial Analysts Journal, Vol. 61, No. 2 (March / April, 2007), pp. 83-89.

August 2013 with Name of Person on the right, company name 43

21 Arnott, Robert D., Jason C. Hsu, and Philip Moore, 2005. "Fundamental Indexation”, Financial Analysts Journal, Vol. 61, No. 2 (March / April, 2007), pp. 83-89.

43 44 | UCLA Anderson Student Asset Management Annual Report Decile Weighting Performance Characteristics Decile Measure Market Cap Book Value Net Income Revenue Dividend Employees EBIT EBITDA OIBDPXINT Geometric Return 9.14% 10.67% 10.67% 11.49% 11.35% 10.66% 11.03% 11.34% 11.29% Standard Deviation 15.20% 15.73% 14.91% 16.16% 14.09% 16.95% 16.53% 15.99% 15.01% 1 Sharpe Ratio 0.25 0.34 0.36 0.38 0.43 0.32 0.35 0.38 0.40 Monthly α 0.00% 0.14% 0.14% 0.20% 0.22% 0.15% 0.16% 0.19% 0.19% t-stat Inf 1.95 2.40 2.50 2.92 1.40 1.96 2.39 2.83 Geometric Return 9.57% 12.19% 11.91% 12.97% 12.41% 12.13% 12.39% 12.16% 12.40% Standard Deviation 16.38% 16.49% 16.17% 17.85% 15.93% 18.44% 16.33% 16.30% 16.24% 2 Sharpe Ratio 0.26 0.42 0.41 0.43 0.45 0.37 0.43 0.42 0.44 Monthly α 0.00% 0.22% 0.20% 0.27% 0.25% 0.20% 0.24% 0.22% 0.24% t-stat Inf 3.48 3.25 3.39 3.45 2.38 3.70 3.50 3.97 Geometric Return 10.65% 11.83% 12.25% 13.32% 12.61% 13.34% 13.22% 12.35% 12.76% Standard Deviation 17.02% 17.31% 17.12% 18.15% 17.07% 19.61% 17.39% 17.42% 17.42% 3 Sharpe Ratio 0.31 0.38 0.41 0.44 0.43 0.41 0.46 0.41 0.43 Monthly α 0.00% 0.11% 0.14% 0.21% 0.18% 0.21% 0.21% 0.15% 0.18% t-stat Inf 1.67 2.52 2.92 2.50 2.19 3.19 2.23 2.72 Geometric Return 11.51% 11.85% 11.41% 13.09% 12.57% 12.05% 10.60% 11.88% 11.50% Standard Deviation 17.87% 18.21% 17.70% 18.60% 16.26% 19.06% 18.54% 17.90% 17.88% 4 Sharpe Ratio 0.35 0.36 0.35 0.42 0.45 0.35 0.29 0.37 0.35 Monthly α 0.00% 0.04% 0.02% 0.13% 0.14% 0.05% -0.05% 0.06% 0.03% t-stat Inf 0.68 0.33 1.90 2.09 0.68 -0.73 0.83 0.40 Geometric Return 11.03% 12.22% 12.44% 11.44% 12.07% 13.15% 12.94% 11.85% 13.58% Standard Deviation 18.85% 18.50% 17.88% 18.71% 17.19% 20.03% 18.45% 19.03% 18.38% 5 Sharpe Ratio 0.30 0.37 0.40 0.33 0.39 0.39 0.41 0.34 0.45 Monthly α 0.00% 0.12% 0.15% 0.07% 0.14% 0.19% 0.18% 0.01% 0.23% t-stat Inf 1.70 2.10 0.85 1.76 1.96 2.50 1.14 3.04 Geometric Return 11.36% 13.06% 13.09% 13.75% 11.84% 13.73% 12.98% 12.69% 11.90% Standard Deviation 18.37% 18.42% 17.84% 18.87% 17.21% 19.81% 18.95% 18.33% 18.33% 6 Sharpe Ratio 0.33 0.42 0.44 0.45 0.38 0.43 0.41 0.40 0.36 Monthly α 0.00% 0.15% 0.16% 0.20% 0.09% 0.20% 0.14% 0.13% 0.07% t-stat Inf 2.36 2.61 2.62 1.23 2.18 1.95 1.84 1.02 Geometric Return 12.28% 13.38% 12.30% 15.15% 11.59% 13.59% 12.07% 13.29% 13.10% Standard Deviation 18.57% 18.67% 18.16% 19.76% 17.82% 20.65% 19.30% 18.87% 18.24% 7 Sharpe Ratio 0.38 0.43 0.39 0.50 0.35 0.40 0.35 0.42 0.43 Monthly α 0.00% 0.11% 0.04% 0.22% 0.00% 0.01% 0.00% 0.10% 0.10% t-stat Inf 1.54 0.59 2.86 0.02 1.13 -0.06 1.39 1.43 Geometric Return 11.88% 12.00% 12.01% 13.98% 13.65% 13.81% 13.81% 13.54% 13.27% Standard Deviation 18.48% 19.56% 18.44% 19.85% 18.05% 20.18% 19.57% 19.15% 19.35% 8 Sharpe Ratio 0.36 0.34 0.36 0.44 0.46 0.42 0.44 0.43 0.41 Monthly α 0.00% 0.02% 0.04% 0.16% 0.18% 0.16% 0.16% 0.14% 0.12% t-stat Inf 0.26 0.56 2.09 2.29 1.75 1.97 1.95 1.54 Geometric Return 11.58% 12.34% 12.82% 14.51% 13.20% 13.88% 12.32% 13.16% 12.37% Standard Deviation 19.18% 19.30% 18.65% 20.38% 18.89% 20.13% 20.33% 19.48% 18.92% 9 Sharpe Ratio 0.33 0.37 0.40 0.45 0.42 0.43 0.35 0.40 0.37 Monthly α 0.00% 0.09% 0.13% 0.24% 0.17% 0.20% 0.07% 0.15% 0.09% t-stat Inf 1.13 1.79 2.69 1.93 2.20 0.87 1.86 1.31 Geometric Return 9.85% 12.86% 12.97% 12.80% 12.82% 12.83% 12.50% 12.95% 13.37% Standard Deviation 19.59% 19.79% 19.29% 20.56% 18.89% 20.32% 21.01% 20.61% 20.06% 10 Sharpe Ratio 0.23 0.38 0.40 0.37 0.40 0.37 0.34 0.37 0.40 Monthly α 0.00% 0.25% 0.26% 0.25% 0.26% 0.25% 0.21% 0.25% 0.28% t-stat Inf 3.44 3.59 2.75 3.10 2.95 2.66 3.07 3.92

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Back Testing Results A summary of the team’s back-testing results against the original research paper is outlined in the following table:

Results vs. Research Paper Time Period: 1962 to 2004 Reference Book Value Net Income Revenue Dividend Employees Return 10.5% 11.6% 12.1% 13.1% 12.2% 12.6% Paper Return 10.4% 12.1% 12.6% 12.9% 12.0% 12.5% Difference 0.1% -­‐0.6% -­‐0.5% 0.3% 0.2% 0.1%

Standard Deviation 15.2% 15.2% 14.5% 15.8% 13.6% 16.7% Paper Standard Deviation 15.2% 14.9% 14.9% 15.9% 13.6% 15.9% Difference 0.0% 0.3% -­‐0.4% -­‐0.1% 0.0% 0.8%

Once our team determined that our back-testing results closely matched those of the reference paper, the team included a few additional fundamental measures:

EBIT: Earnings before interest and taxes. Common proxies for operating cash flows

EBITDA: Earning before interest, taxes, depreciation and amortization

OIBDPXINT: Operating income before depreciation/amortization after interest expense. This measure is also commonly referred to as “Direct Profitability.” The team was influenced to use Direct Profitability after research by Dimensional Fund Advisors indicated that among all cash flow measures, Direct Profitability most accurately predicts future performance among similar sized companies.

Although there are certainly other metrics that the team could have been included, our group chose to limit our back-testing to the original metrics in the paper as well as those noted above. The group wanted to focus on heuristics that reasonably measured a company’s economic footprint and wanted to avoid data-mining.

Analysis of Back Test Results As an additional consideration, the group decided to reduce the number of securities in our fundamental index portfolio to reduce trading costs. Using 1,000 stocks as noted in the reference paper would create substantial transaction costs that would erode any excess returns relative to the benchmark. As a result, the group tested a portfolio composed of the top 50 US stocks weighted according to their fundamental heuristic scores.

Back Testing Results for Top 1,000 Stocks

To ensure that fundamentally weighted portfolio outperformance is evident in different market capitalization size groups for the top 1000 stocks, the group back-tested data among different market capitalization deciles. Decile 1 consists of the top 100 stocks and Decile 10 consists of the bottom 100 stocks for each following respective weighting heuristic: book value, net income, revenue, dividend, number of employees, earnings before interest & taxes (EBIT), earnings before interest, taxes, depreciation and amortization (EBITDA), and operating income before depreciation/amortization after interest expense (OIBDPXINT or “Direct Profitability”). Portfolio returns and risk-adjusted performance measures were calculated from January 1962 to March 2013. A summary of the results are shown below:

45 46 | UCLA Anderson Student Asset Management Annual Report

Decile Weighting Performance Characteristics Decile Measure Market Cap Book Value Net Income Revenue Dividend Employees EBIT EBITDA OIBDPXINT Geometric Return 9.14% 10.67% 10.67% 11.49% 11.35% 10.66% 11.03% 11.34% 11.29% Standard Deviation 15.20% 15.73% 14.91% 16.16% 14.09% 16.95% 16.53% 15.99% 15.01% 1 Sharpe Ratio 0.25 0.34 0.36 0.38 0.43 0.32 0.35 0.38 0.40 Monthly α 0.00% 0.14% 0.14% 0.20% 0.22% 0.15% 0.16% 0.19% 0.19% t-­‐stat Inf 1.95 2.40 2.50 2.92 1.40 1.96 2.39 2.83 Geometric Return 9.57% 12.19% 11.91% 12.97% 12.41% 12.13% 12.39% 12.16% 12.40% Standard Deviation 16.38% 16.49% 16.17% 17.85% 15.93% 18.44% 16.33% 16.30% 16.24% 2 Sharpe Ratio 0.26 0.42 0.41 0.43 0.45 0.37 0.43 0.42 0.44 Monthly α 0.00% 0.22% 0.20% 0.27% 0.25% 0.20% 0.24% 0.22% 0.24% t-­‐stat Inf 3.48 3.25 3.39 3.45 2.38 3.70 3.50 3.97 Geometric Return 10.65% 11.83% 12.25% 13.32% 12.61% 13.34% 13.22% 12.35% 12.76% Standard Deviation 17.02% 17.31% 17.12% 18.15% 17.07% 19.61% 17.39% 17.42% 17.42% 3 Sharpe Ratio 0.31 0.38 0.41 0.44 0.43 0.41 0.46 0.41 0.43 Monthly α 0.00% 0.11% 0.14% 0.21% 0.18% 0.21% 0.21% 0.15% 0.18% t-­‐stat Inf 1.67 2.52 2.92 2.50 2.19 3.19 2.23 2.72 Geometric Return 11.51% 11.85% 11.41% 13.09% 12.57% 12.05% 10.60% 11.88% 11.50% Standard Deviation 17.87% 18.21% 17.70% 18.60% 16.26% 19.06% 18.54% 17.90% 17.88% 4 Sharpe Ratio 0.35 0.36 0.35 0.42 0.45 0.35 0.29 0.37 0.35 Monthly α 0.00% 0.04% 0.02% 0.13% 0.14% 0.05% -­‐0.05% 0.06% 0.03% t-­‐stat Inf 0.68 0.33 1.90 2.09 0.68 -­‐0.73 0.83 0.40 Geometric Return 11.03% 12.22% 12.44% 11.44% 12.07% 13.15% 12.94% 11.85% 13.58% Standard Deviation 18.85% 18.50% 17.88% 18.71% 17.19% 20.03% 18.45% 19.03% 18.38% 5 Sharpe Ratio 0.30 0.37 0.40 0.33 0.39 0.39 0.41 0.34 0.45 Monthly α 0.00% 0.12% 0.15% 0.07% 0.14% 0.19% 0.18% 0.01% 0.23% t-­‐stat Inf 1.70 2.10 0.85 1.76 1.96 2.50 1.14 3.04 Decile Weighting Performance Characteristics Decile Measure Market Cap Book Value Net Income Revenue Dividend Employees EBIT EBITDA OIBDPXINT Geometric Return 11.36% 13.06% 13.09% 13.75% 11.84% 13.73% 12.98% 12.69% 11.90% Standard Deviation 18.37% 18.42% 17.84% 18.87% 17.21% 19.81% 18.95% 18.33% 18.33% 6 Sharpe Ratio 0.33 0.42 0.44 0.45 0.38 0.43 0.41 0.40 0.36 Monthly α 0.00% 0.15% 0.16% 0.20% 0.09% 0.20% 0.14% 0.13% 0.07% t-­‐stat Inf 2.36 2.61 2.62 1.23 2.18 1.95 1.84 1.02 Geometric Return 12.28% 13.38% 12.30% 15.15% 11.59% 13.59% 12.07% 13.29% 13.10% Standard Deviation 18.57% 18.67% 18.16% 19.76% 17.82% 20.65% 19.30% 18.87% 18.24% 7 Sharpe Ratio 0.38 0.43 0.39 0.50 0.35 0.40 0.35 0.42 0.43 Monthly α 0.00% 0.11% 0.04% 0.22% 0.00% 0.01% 0.00% 0.10% 0.10% t-­‐stat Inf 1.54 0.59 2.86 0.02 1.13 -­‐0.06 1.39 1.43 Geometric Return 11.88% 12.00% 12.01% 13.98% 13.65% 13.81% 13.81% 13.54% 13.27% Standard Deviation 18.48% 19.56% 18.44% 19.85% 18.05% 20.18% 19.57% 19.15% 19.35% 8 Sharpe Ratio 0.36 0.34 0.36 0.44 0.46 0.42 0.44 0.43 0.41 Monthly α 0.00% 0.02% 0.04% 0.16% 0.18% 0.16% 0.16% 0.14% 0.12% t-­‐stat Inf 0.26 0.56 2.09 2.29 1.75 1.97 1.95 1.54 Geometric Return 11.58% 12.34% 12.82% 14.51% 13.20% 13.88% 12.32% 13.16% 12.37% Standard Deviation 19.18% 19.30% 18.65% 20.38% 18.89% 20.13% 20.33% 19.48% 18.92% 9 Sharpe Ratio 0.33 0.37 0.40 0.45 0.42 0.43 0.35 0.40 0.37 Monthly α 0.00% 0.09% 0.13% 0.24% 0.17% 0.20% 0.07% 0.15% 0.09% t-­‐stat Inf 1.13 1.79 2.69 1.93 2.20 0.87 1.86 1.31 Geometric Return 9.85% 12.86% 12.97% 12.80% 12.82% 12.83% 12.50% 12.95% 13.37% Standard Deviation 19.59% 19.79% 19.29% 20.56% 18.89% 20.32% 21.01% 20.61% 20.06% 10 Sharpe Ratio 0.23 0.38 0.40 0.37 0.40 0.37 0.34 0.37 0.40 Monthly α 0.00% 0.25% 0.26% 0.25% 0.26% 0.25% 0.21% 0.25% 0.28% t-­‐stat Inf 3.44 3.59 2.75 3.10 2.95 2.66 3.07 3.92 # of times measure has max t-­‐stat? 0 0 3 3 0 1 0 3

Using the results, the group confirmed that fundamental weighted portfolios outperformance was consistent among various market capitalization buckets. Additionally, of the eight fundamental measures, Revenue, Dividend and Direct Profitability consistently generated the highest performance.

46 UCLA Anderson Student Asset Management Annual Report | 47

Backing Results for ASAM’s Top 50 USA Stock Portfolio Composite

While these results wereRetu rencouraging,n Characteri swetic swanted by Dec toad furthere 1962 -reduce2013 the number of securities in our fundamental index in Portfolio/Indeorderx to l1ower/62- 1trading2/69 costs1/7 0relative-12/79 to the1/ 8size0-1 of2/ our89 portfolio.1/90-1 2Although/99 1 /the00 paper-12/0 9noted1 using/10-3 a/ 11,0003 stock portfolio, our A. Geometric returnsteam believed that a portfolio of 1,000 stocks would be difficult for ASAM to implement and the substantial trading costs Reference would erode5.93% any excess returns5.27% relative to 18.42%the benchmark. 19.94%Therefore, our team-3.36% tested a portfolio11.05% of the top 50 USA stocks Book Value to confirm that1.34% the results persisted8.92% with a18.65% more concentrated18.62% portfolio. The-1.38% table below 15.04%shows various performance and Income risk measures4.83% for the market8.44% weighting portfolio18.52% and each18.98% fundamental weighted-0.50% portfolio.14.29% The table also includes a 50- Revenue 6.19% 7.76% 20.09% 18.69% 1.22% 14.64% stock composite portfolio based on the following weightings: 40% Dividend, 40% Direct Profitability and 20% Revenue. Dividends 4.78% 8.47% 19.58% 16.27% 2.97% 15.32% Employment Our back-testing5.91% results show4.39% each fundamental19.62% weighted16.66% portfolio outperformed2.40% the market14.37% weighted benchmark on a EBIT total return 4.54%and risk-adjusted8.56% basis. 19.59% 21.07% -1.49% 13.78% EBITDA 4.82% 9.64% 19.50% 19.98% -1.53% 13.66% Direct ProfitabilityPerformance4.84% and Risk Measures:10.03% January19.14% 1962 to June18.19% 2013 -0.35% 15.64% Composite 4.99% 9.04% 19.52% 17.80% 0.56% 15.09%Direct B. Value added relative to reference Market portfolio Cap (pps)Book Value Net Income Revenue Dividend Employment EBIT EBITDA Profitability Composite Geometric Return 9.14% 9.58% 10.28% 11.05% 10.76% 10.13% 10.44% 10.53% 10.67% 10.67% Reference Standard Deviation -- 14.75% --15.61% 14.58% -- 15.69% 13.78%-- 16.39% 16.52%-- 15.88% -- 14.63% 14.40% Book Value Sharpe -4.60 0.28 3.640.29 0.36 0.23 0.38 0.41-1.32 0.31 1.980.32 0.34 3.99 0.38 0.39 Adj. Sharpe Ratio 0.27 0.29 0.34 0.37 0.38 0.31 0.33 0.34 0.36 0.37 Income Beta -1.10 1.00 3.170.96 0.92 0.10 0.95 0.84-0.96 0.93 2.851.00 0.96 3.24 0.91 0.89 Revenue Excess Return 0.25 0.00% 2.490.44% 1.14% 1.67 1.91% 1.62%-1.26 0.99% 4.581.30% 1.39% 3.591.53% 1.53% Dividends Tracking Error -1.15 0.00% 3.206.57% 5.31% 1.16 7.14% 6.62%-3.67 9.01% 6.337.52% 7.12% 4.275.86% 6.08% Information Ratio -­‐-­‐ 0.05 0.17 0.23 0.17 0.08 0.16 0.17 0.21 0.19 Employment Sortino Ratio -0.03 0.14 -0.880.15 0.18 1.20 0.19 0.20-3.28 0.16 5.750.17 0.18 3.32 0.19 0.19 EBIT Treynor Ratio -1.39 0.09 3.290.10 0.11 1.17 0.12 0.131.13 0.11 1.860.10 0.11 2.73 0.12 0.12 EBITDA Skew -1.11 -­‐0.34 4.36-­‐0.34 -­‐0.33 1.08 -­‐0.27 -­‐0.300.03 -­‐0.25 1.830.10 -­‐0.12 2.61 -­‐0.29 -­‐0.29 Kurtosis 4.31 4.69 4.61 4.90 4.73 5.38 6.57 5.46 4.54 4.69 Direct ProfitabilityMax Drawdown -1.09 -­‐52.99% 4.76-­‐61.47% -­‐58.49%0.72-­‐54.62% -­‐55.85%-1.75 -­‐46.42% 3.01-­‐68.95% -­‐66.58% 4.59-­‐57.11% -­‐57.03% Composite Pain Index -0.94 10.30% 3.778.28% 6.67% 1.09 6.63% 5.41%-2.14 6.77% 3.928.77% 8.14% 4.046.55% 6.10% Pain Ratio 0.39 0.54 0.78 0.90 1.05 0.75 0.61 0.67 0.85 0.92 B. Annualized standardUp Capture deviation Ratio of return1.00 0.99 0.98 1.01 0.92 0.98 1.04 1.02 0.98 0.96 Reference Down Capture 11.84% Ratio 1.00 14.97%0.95 0.8915.74%0.90 13.48%0.78 0.89 15.91%0.97 0.93 11.55%0.87 0.85 Up Number Ratio 1.00 0.91 0.93 0.88 0.89 0.86 0.91 0.91 0.93 0.92 Book Value Down Number 13.03% Ratio 1.00 14.77%0.86 0.8815.11%0.83 13.37%0.84 0.79 19.34%0.86 0.85 15.37%0.85 0.85 Income Up Percentage 11.76% Ratio 0.00 14.81%0.47 0.4614.93%0.51 13.01%0.44 0.49 16.94%0.51 0.50 11.57%0.47 0.45 Revenue Down Percentage 12.36% Ratio 0.00 16.20%0.55 0.5816.59%0.52 13.67%0.66 0.52 17.86%0.55 0.55 13.65%0.56 0.61 VaR Cornish-­‐Fisher (0.95%) -­‐6.46% -­‐6.81% -­‐6.26% -­‐6.63% -­‐5.80% -­‐6.96% -­‐6.42% -­‐6.52% -­‐6.21% -­‐6.10% Dividends VaR Historical 11.49% (0.95%) -­‐6.83% 14.30%-­‐6.52% -­‐6.06%13.73%-­‐6.08% -­‐5.68%11.57% -­‐6.79% 16.35%-­‐6.66% -­‐6.52%11.61%-­‐5.91% -­‐5.73% Employment VaR Gaussian(0.95%013.09% -­‐6.17% 18.16%-­‐6.54% -­‐6.01%18.34%-­‐6.46% -­‐5.60%14.69% -­‐6.86% 17.32%-­‐6.90% -­‐6.59%12.71%-­‐6.00% -­‐5.90% EBIT 11.76% 14.76% 16.07% 14.83% 21.74% 14.84% EBITDA 11.68% 14.23% 15.65% 14.09% 20.58% 13.96% Direct Profitability 11.73% 14.22% 14.99% 12.68% 17.40% 12.03% Composite 11.75% 14.37% 14.71% 12.13% 17.07% 12.14% B. Sharpe ratio Reference 0.14 -0.07 0.61 1.11 -0.38 0.95 Book Value -0.22 0.18 1.23 1.02 -0.21 0.98 Income 0.05 0.14 0.91 1.08 -0.19 1.23 Revenue 0.16 0.09 1.21 1.01 -0.09 1.07 Dividends 0.05 0.15 1.22 0.98 0.01 1.32 Employment 0.13 -0.11 1.07 0.80 -0.02 1.13 EBIT 0.03 0.15 1.22 1.09 -0.20 0.93 EBITDA 0.05 0.23 1.25 1.07 -0.21 0.98 Direct Profitability 0.05 0.26 1.28 1.05 -0.18 1.30 Composite 0.06 0.19 1.33 1.06 -0.13 1.24

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48 | UCLA Anderson Student Asset Management Annual Report

Growth of $1 from January 1962 to June 2013 $250 $250

Growth of $200$1 from January 1962 to June 2013 $200 $250

Revenue Revenue Dividend Dividend $150 Composite $150 Composite Direct Profitability $200 Direct Profitability EBITDA EBITDA EBIT EBIT Net Revenue Income $100 Net Income $100 Dividend Employees Employees $150 Composite Book Value Book Value Direct Profitability Market Cap Market Cap EBITDA

$50 $50 EBIT Net Income $100 Employees Book Value Market Cap $0 $0 12/31/1962 6/22/196812/31/196212/13/19736/22/1968 6/5/197912/13/1973 11/25/19846/5/1979 5/18/199011/25/1984 11/8/19955/18/1990 4/30/200111/8/1995 10/21/20064/30/2001 10/21/20064/12/2012 4/12/2012 $50

Top 50 US Stock Portfolio – Historical Returns by Decade When we analyze the risk and performance measures by decade, the fundamental weighted indices generally outperformed $0 the reference12/31/1962 index except6/22/1968 during12/13/1973 the 1990’s.6/5/1979 During11/25/1984 the late5/18/1990 90s, there11/8/1995 was a4/30/2001 massive10/21/2006 inflow as4/12/2012 assets into market capitalization weighted index products. This phenomenon strongly fueled the performance of “mega-cap” stocks, leading to outperformance in market capitalization weighted index products. The group believes that performance comparisons during thisTop decade 50 US is an Stock apples -Portfolioto-orange comparison. – Historical Returns by Decade When we analyze the risk and performance measures by decade, the fundamental weighted indices generally outperformed the reference index except during the 1990’s. During the late 90s, there was a massive inflow as assets into market capitalization weighted index products. This phenomenon strongly fueled the performance of “mega-cap” stocks, leading to outperformance in market capitalization weighted index products. The group believes that performance comparisons during this decade is an apples-to-orange comparison.

UCLA Anderson Student Asset Management Annual Report | 49 48

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We also conducted regression analyses based on the Fama French three-factor model: Return Characteristics by Decade 1962-­‐2013 2 Portfolio/Index 1/62-­‐12/69 1/70-­‐12/79 1/80-­‐12/89 1/90-­‐12/99 1/00-­‐12/09 1/10-­‐3/13 α MKT-B SmB HML Adj R Std Error A. Geometric returns Market Cap 0.00% 1.00 0.00 0.00 1.00 0.00 Reference 5.93% 5.27% 18.42% 19.94% -­‐3.36% 11.05% Std Error 0.00 0.00 0.00 0.00 Book Value 1.34% 8.92% 18.65% 18.62% -­‐1.38% 15.04% T-Stat 65535 65535 65535 65535 Income 4.83% 8.44% 18.52% 18.98% -­‐0.50% 14.29% Book Value -0.16% 1.03 0.15 0.37 0.87 0.02 Revenue 6.19% 7.76% 20.09% 18.69% 1.22% 14.64% Std Error 0.00 0.02 0.02 0.02 Dividends 4.78% 8.47% 19.58% 16.27% 2.97% 15.32% T-Stat -2.38 64.26 7.07 14.97 Employment 5.91% 4.39% 19.62% 16.66% 2.40% 14.37% -0.07% 0.98 0.10 0.31 0.91 0.01 EBIT 4.54% 8.56% 19.59% 21.07% -­‐1.49% 13.78% Net Income EBITDA 4.82% 9.64% 19.50% 19.98% -­‐1.53% 13.66% Std Error 0.00 0.01 0.02 0.02 Direct Profitability 4.84% 10.03% 19.14% 18.19% -­‐0.35% 15.64% T-Stat -1.28 78.84 6.01 16.16 Composite 4.99% 9.04% 19.52% 17.80% 0.56% 15.09% Revenue -0.07% 1.01 0.29 0.40 0.87 0.02 Std Error 0.00 0.02 0.02 0.02 B. Value added relative to reference portfolio (pps) T-Stat -1.10 62.75 13.30 16.24 Reference -­‐-­‐ -­‐-­‐ -­‐-­‐ -­‐-­‐ -­‐-­‐ -­‐-­‐ Dividends -0.06% 0.91 0.11 0.41 0.88 0.01 Book Value -­‐4.60 3.64 0.23 -­‐1.32 1.98 3.99 Std Error 0.00 0.01 0.02 0.02 Income -­‐1.10 3.17 0.10 -­‐0.96 2.85 3.24 T-Stat -1.11 65.71 6.15 19.43 Revenue 0.25 2.49 1.67 -­‐1.26 4.58 3.59 Employment -0.12% 0.98 0.45 0.37 0.81 0.02 Dividends -­‐1.15 3.20 1.16 -­‐3.67 6.33 4.27 Std Error 0.00 0.02 0.03 0.03 Employment -­‐0.03 -­‐0.88 1.20 -­‐3.28 5.75 3.32 T-Stat -1.42 47.09 16.05 11.57 EBIT -­‐1.39 3.29 1.17 1.13 1.86 2.73 EBITDA -­‐1.11 4.36 1.08 0.03 1.83 2.61 EBIT -0.11% 1.07 0.13 0.39 0.84 0.02 Direct Profitability -­‐1.09 4.76 0.72 -­‐1.75 3.01 4.59 Std Error 0.00 0.02 0.03 0.03 Composite -­‐0.94 3.77 1.09 -­‐2.14 3.92 4.04 T-Stat -1.39 56.54 5.12 13.51 EBITDA -0.10% 1.04 0.15 0.40 0.86 0.02 Return Characteristics by Decade 1962-­‐2013 Std Error 0.00 0.02 0.02 0.03 Portfolio/Index 1/62-­‐12/69 1/70-­‐12/79 1/80-­‐12/89 1/90-­‐12/99 1/00-­‐12/09 1/10-­‐3/13 T-Stat -1.45 59.99 6.43 15.24 B. Annualized standard deviation of return Direct Profitability -0.05% 0.98 0.14 0.34 0.90 0.01 Reference 11.84% 14.97% 15.74% 13.48% 15.91% 11.55% Std Error 0.00 0.01 0.02 0.02 Book Value 13.03% 14.77% 15.11% 13.37% 19.34% 15.37% T-Stat -0.96 71.89 7.48 16.47 Income 11.76% 14.81% 14.93% 13.01% 16.94% 11.57% Composite -0.07% 0.96 0.14 0.38 0.90 0.01 Revenue 12.36% 16.20% 16.59% 13.67% 17.86% 13.65% Std Error 0.00 0.01 0.02 0.02 Dividends 11.49% 14.30% 13.73% 11.57% 16.35% 11.61% T-Stat -1.28 72.11 7.93 18.74 Employment 13.09% 18.16% 18.34% 14.69% 17.32% 12.71% EBIT 11.76% 14.76% 16.07% 14.83% 21.74% 14.84% As shown above, the top 50 USA stock portfolio composite outperformed as a result of each portfolio’s overweight EBITDA 11.68% 14.23% 15.65% 14.09% 20.58% 13.96% Direct Profitability 11.73% 14.22% 14.99% 12.68% 17.40% 12.03% allocation to smaller cap (SmB > 0) and value (HML >0) stocks relative to the reference benchmark (Market Cap). Each Composite 11.75% 14.37% 14.71% 12.13% 17.07% 12.14% fundamental-weighted portfolio has a statistically significant relationship to the SMB and HML factors, with the B. Sharpe ratio Employment portfolio most heavily-weighted towards smaller cap stocks and the Dividend portfolio most heavily-weighted Reference 0.14 -­‐0.07 0.61 1.11 -­‐0.38 0.95 towards value stocks. After accounting for the SmB and HML factors, there is no alpha in the alternate portfolios (α < 0). Book Value -­‐0.22 0.18 1.23 1.02 -­‐0.21 0.98 Therefore, the team concludes that the fundamental-weighted portfolios add value by capturing the Fame-French factors at Income 0.05 0.14 0.91 1.08 -­‐0.19 1.23 a low cost. Revenue 0.16 0.09 1.21 1.01 -­‐0.09 1.07 Dividends 0.05 0.15 1.22 0.98 0.01 1.32 Employment 0.13 -­‐0.11 1.07 0.80 -­‐0.02 1.13 EBIT 0.03 0.15 1.22 1.09 -­‐0.20 0.93 EBITDA 0.05 0.23 1.25 1.07 -­‐0.21 0.98 Direct Profitability 0.05 0.26 1.28 1.05 -­‐0.18 1.30 Composite 0.06 0.19 1.33 1.06 -­‐0.13 1.24

Top 50 USA Stock Portfolio – Fama-French Regression Analysis

49 50 50 | UCLA Anderson Student Asset Management Annual Report

We also conducted regression analyses based on the Fama French three-factor model:

2 α MKT-B SmB HML Adj R Std Error Market Cap 0.00% 1.00 0.00 0.00 1.00 0.00 Std Error 0.00 0.00 0.00 0.00 T-Stat 65535 65535 65535 65535 Book Value -0.16% 1.03 0.15 0.37 0.87 0.02 Std Error 0.00 0.02 0.02 0.02 T-Stat -2.38 64.26 7.07 14.97 Net Income -0.07% 0.98 0.10 0.31 0.91 0.01 Std Error 0.00 0.01 0.02 0.02 T-Stat -1.28 78.84 6.01 16.16 Revenue -0.07% 1.01 0.29 0.40 0.87 0.02 Std Error 0.00 0.02 0.02 0.02 T-Stat -1.10 62.75 13.30 16.24 Dividends -0.06% 0.91 0.11 0.41 0.88 0.01 Std Error 0.00 0.01 0.02 0.02 T-Stat -1.11 65.71 6.15 19.43 Employment -0.12% 0.98 0.45 0.37 0.81 0.02 Std Error 0.00 0.02 0.03 0.03 T-Stat -1.42 47.09 16.05 11.57 EBIT -0.11% 1.07 0.13 0.39 0.84 0.02 Std Error 0.00 0.02 0.03 0.03 T-Stat -1.39 56.54 5.12 13.51 EBITDA -0.10% 1.04 0.15 0.40 0.86 0.02 Std Error 0.00 0.02 0.02 0.03 T-Stat -1.45 59.99 6.43 15.24 Direct Profitability -0.05% 0.98 0.14 0.34 0.90 0.01 Std Error 0.00 0.01 0.02 0.02 T-Stat -0.96 71.89 7.48 16.47 Composite -0.07% 0.96 0.14 0.38 0.90 0.01 Std Error 0.00 0.01 0.02 0.02 T-Stat -1.28 72.11 7.93 18.74

As shown above, the top 50 USA stock portfolio composite outperformed as a result of each portfolio’s overweight allocation to smaller cap (SmB > 0) and value (HML >0) stocks relative to the reference benchmark (Market Cap). Each fundamental-weighted portfolio has a statistically significant relationship to the SMB and HML factors, with the Employment portfolio most heavily-weighted towards smaller cap stocks and the Dividend portfolio most heavily-weighted towards value stocks. After accounting for the SmB and HML factors, there is no alpha in the alternate portfolios (α < 0). Therefore, the team concludes that the fundamental-weighted portfolios add value by capturing the Fame-French factors at a low cost.

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Portfolio Construction (Mean Variance Optimization) With robust data on the historical returns and volatility of each of the fundamental portfolios as well as the market-cap weighted reference benchmark, we next examined the correlations between each portfolio in an effort to design an optimal portfolio. The table below represents these correlations. As is to be expected, the correlations between the portfolios are relatively high on average with a minimum of 0.84 and a maximum of 0.99. Of notable interest is the Employees weighted portfolio, which demonstrates the widest swings in 12-month rolling correlations, reaching a low point of 0.3 during several time periods. Additionally, the Dividend weighted portfolio shows unique correlations in that it tends to be consistently correlated to the reference portfolio over time, however this correlation broke down during the market downturn of 2001, a period during which its correlation dropped to 0.3. With the results of the correlation analysis in mind, the team determined that a composite portfolio could take advantage of correlation effects. This composite portfolio will be discussed in further detail in following sections.

MarketCap Book value Net Income Revenue Dividend Employees EBIT EBITDA OIBDPXINT

MarketCap 1.00 0.91 0.93 0.89 0.89 0.84 0.89 0.89 0.92

Book value 0.91 1.00 0.97 0.95 0.95 0.87 0.95 0.97 0.97

Net Income 0.93 0.97 1.00 0.96 0.98 0.88 0.96 0.97 0.98

Revenue 0.89 0.95 0.96 1.00 0.94 0.94 0.94 0.96 0.97

Dividend 0.89 0.95 0.98 0.94 1.00 0.88 0.95 0.96 0.97

Employees 0.84 0.87 0.88 0.94 0.88 1.00 0.85 0.87 0.88

EBIT 0.89 0.95 0.96 0.94 0.95 0.85 1.00 0.99 0.96

EBITDA 0.89 0.97 0.97 0.96 0.96 0.87 0.99 1.00 0.98

OIBDPXINT 0.92 0.97 0.98 0.97 0.97 0.88 0.96 0.98 1.00

Mean Variance Optimization

With performance, volatility, and correlation data on each of the fundamental portfolios, our next task was to conduct a mean variance optimization analysis to provide further clues towards creating an effective composite portfolio. The chart below represents the risk and return profile of each strategy and the respective position of each portfolio relative to the efficient frontier. These results provide support to the underlying thesis behind fundamental indexation - market-cap weighted portfolios are not efficient. As is evident from the chart, the market-cap weighted index provides the lowest expected return, and has a higher standard deviation than several of the fundamental portfolios. Another observation is that the Dividend weighted portfolio is the most mean-variance optimal of the selected group as it directly intersects with the efficient frontier. However, the Direct Profitability and Revenue portfolios also warrant additional attention as they are both exhibit impressive risk and return characteristics compared to other portfolios.

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Risk vs. Return 0.12

Revenue 0.11

Dividend Direct Profitability EBITDA EBIT

Net Income

Expected Return Employees 0.10

Book Value

Market Cap 0.09

0.130 0.142 0.153 0.165

Risk (Standard Deviation)

Given these observations, we conducted a Sharpe ratio analysis of various portfolios created using the above data on returns, volatility, and correlation. As is evident from the mean variance analysis, the optimal composite portfolio (without using any constraints) is heavily allocated to the Dividend-weighted portfolio with that portfolio receiving a 98.8% weight. Based on our data, this portfolio provides the most efficient risk-return profile of any of the portfolios we explored, with a Sharpe ratio of 0.78. The Revenue-weighted portfolio is also fairly efficient. Composite portfolios that allocate to the Revenue portfolio tend to have the highest returns, although they do so with higher levels of risk as can be seen in the lower Sharpe ratio of these portfolios.

Although from a pure mean variance optimization perspective, the optimal composite portfolio is essentially the Dividend portfolio, the team decided to add constraints to the mean variance optimizer for several reasons. First of all, ASAM cannot borrow at the risk-free rate or take short positions in accordance with the UCLA Foundation’s Investment Policy Statement, therefore our group cannot leverage the tangency portfolio. Also, several academic papers question the ability of mean- variance optimization to construct an ex-ante efficient portfolio.23 When using historical data to create a mean return and covariance matrix, mean variance optimization only indicates how the composite would have performed in the past, and

23 Demiguel, V., L. Garlappi, and R. Uppal. "Optimal Versus Naive Diversification: How Inefficient Is the 1/N Portfolio Strategy?" Review of Financial Studies 22.5 (2007): 1915-953. Print.

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thus should only be viewed a general guide for forward-looking results. Consequently the team has implemented a 40% maximum allocation to any individual fundamental portfolio such that the Composite portfolio will include a minimum of three constituent portfolios. While a maximum constraint will be used to motivate the inclusion of a minimum number of portfolios, a minimum constraint will not be set as the data does not provide support for certain fundamental portfolios. The Employees portfolio, for example, is not mean variance optimal, so there is no empirical data to support an allocation to this portfolio within the Composite.

The chart above includes a composite portfolio with constraints dictating a 40% allocation to the Dividend portfolio, a 40% allocation to the Direct Profitability portfolio, and a 20% allocation to the Revenue portfolio. The chart highlights the return and volatility of actual simulated portfolios over the observation period. These results are the basis for the Composite portfolio that the team ultimately implemented.

Risk vs. Return (40% Limit Constraint) 0.12

Revenue 0.11

Dividend Tangency Direct Profitability Composite EBITDA EBIT

Net Income

Expected Return Employees 0.10

Book Value

Market Cap 0.09

0.130 0.142 0.153 0.165

Risk (Standard Deviation)

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ASAM 2014 Fundamental Index Portfolio Based on the research and analysis sited above, the team created the ASAM 2014 Fundamental Index Portfolio. The portfolio was implemented on October 29th, 2013.

Custodial transaction costs are $5.95 per trade for a total of $297.50 to implement the portfolio. At inception the portfolio’s market value was $200,000.00, indicating an expense ratio of approximately 0.3% assuming a conservative estimate of 100% turnover every single year. As shown by back-testing results, each fundamentally weighted portfolio still produces positive excess returns even with the trading costs associated with a $200,000 size portfolio.

January 1962 through June 2013 Excess Portfolio Turnover Return at 0.3% Trade Reference 8.57% -­‐-­‐ Book Value 15.34% 0.14% Net Income 14.12% 0.84% Revenue 12.45% 1.61% Dividends 12.68% 1.32% Employment 14.84% 0.69% EBIT 12.92% 1.00% EBITDA 13.02% 1.10% Direct Profitability 12.83% 1.23% Composite 12.17% 1.23%

ASAM 2014 — Front row: Joseph Duronio, Name; Middle row: Thomas Gotsch, Name, Name, Name, Name; Top row; Name, Name, Name, Name, Name, Name, Name, Name, Name Can someone please provide copy for the names for the above caption

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The table below shows the list of the portfolio’s holdings sorted in descending order. The “Weight Differential” column includes a calculation of the tracking error of the implemented portfolio relative to the pure outputs from our model. This tracking error occurs due to the inability to purchase partial shares to accurately capture the exact component weights derived from our model. The tracking error at inception was approximately 0.663%.

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Below is a graph representing the sector allocation of the ASAM 2014 Fundamental Index Portfolio relative to its cap- weighted reference benchmark, the Russell Top 50 Mega Cap Index, as of the date of the Fundamental Index Portfolio’s inception (October 29th, 2013). The graph provides several valuable insights on how the portfolios differ. Most notably, the cap-weighted index has a substantial overweight to the technology sector at 26.4% and an underweight to the telecommunications sector at 4.5%. In contrast, the Fundamental Index is more evenly diversified across sectors. Another unique aspect of the Fundamental Index Portfolio is its largest component sector; energy. None of the three largest broad U.S. market indices including the S&P 500, the Dow Jones Industrial Average, or the Russell 1000 feature the energy sector in their top 3 sector weights. This suggests that the stocks of large energy companies have stronger fundamental characteristics on average than those of other sectors. One final point to note is that neither the fundamental index nor the Russell Top 50 index include meaningful exposure to the utilities or materials sectors. Stocks within each sector generally have lower market capitalizations, resulting in smaller relative weighting in diversified large-cap indices. Given that the two portfolios are comprised of the top 50 stocks, be it on a fundamental or cap-weighted basis, neither sector receives an allocation in either portfolio.

Fundamental Index vs Russell Top 50 Mega Cap Sector AllocaSon

Energy 17.2% 12.6%

Financials 17.1% 14.6%

Technology 12.8% 26.4%

Cons. Staples 11.5% 12.4%

Telecom 11.0% 4.5% Fundamental Index Cons. Discre+onary 10.9% 8.8% Russell Top 50

Healthcare 9.3% 11.7%

Industrials 9.0% 9.2% Source: Bloomberg LP

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Below is a list of the top 10 holdings by weight in the Fundamental Index portfolio and the Russell Top 50 Mega Cap Index. While the fundamental index is more diversified than the benchmark from a sector perspective, it is actually more concentrated at the individual security level. The fundamental index allocates 45.8% of its portfolio to the top 10 stocks while the benchmark holds 37.9% in its top 10. This is a result of the incrementally higher fundamental scores of those top 10 stocks relative to the other securities in the portfolio. Although there are 4 stocks that appear in the top 10 positions of both portfolios (Exxon Mobil, Chevron, General Electric, and Wells Fargo) the portfolios differ substantially in their weighting of these and other stocks - as is evident in the broader sector allocations of each portfolio.

Fundamental Index Weight Russell Top 50 Weight Exxon Mobil 7.54% Apple 6.51% AT&T 5.28% Exxon Mobil 5.40% Chevron 4.92% Microsoft 3.94% Walmart 4.81% General Electric 3.47% General Electric 4.51% Johnson & Johnson 3.44% JPMorgan Chase 4.20% Google 3.29% Bank of America 4.00% Chevron 3.21% Verizon 3.67% Procter and Gamble 3.01% Wells Fargo 3.45% Berkshire Hathaway 2.87% Pfizer 3.43% Wells Fargo 2.78% Top 10 Concentration 45.81% 37.92%

Performance Since the fund’s inception on October 29, 2013 until April 30, 2014, ASAM’s Fundamental Index strategy returned 7.6%, outperforming the Russell Megacap Index by 32 basis points. Please see the graph below illustrating asset growth of the Fundamental Index versus its benchmark during the time period:

$220,000 $220,000 $215,000 $215,000

$210,000 $210,000

$205,000 $205,000

$200,000 $200,000

$195,000 $195,000

$190,000 $190,000 $185,000 $185,000

Fundamental IndexFundamentalRussell Index Megacap Index Russell Megacap Index

57

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Fundamental Index Growth, October 29, 2013 to April 30, 20

The Fundamental Index strategy outperformed its benchmark due to positive security selection effects, mainly within Information Technology, Health Care, and Financials. Due to the bottom-up nature of this strategy, the portfolio’s sector weightings differ from its benchmark. Most notably, the portfolio has an underweight allocation to the Information Technology sector, with an average portfolio weight of 13.3% versus 26.9% for the Russell Megacap Index during the performance time period. This underweight to Information Technology is offset by an overweight allocation to Energy (17.1% of the portfolio versus 12.1% for the benchmark), Financials (17.8% of the portfolio versus 14.6% for the benchmark), and Telecommunications (8.3% of the portfolio versus 4.4% for the benchmark). These sector allocation effects detracted from relative returns during the performance time period, as Telecommunications (down 3.3%) was the worst-performing sector and as Information Technology was the best performing sector (up 10.5%) during the time period.

The negative performance drag produced by the portfolio’s sector weights was more than offset by positive security selection effects. The top contributor to relative return was the portfolio’s exposure to Hewlett-Packard, which returned 40.1% and comprised an average weight of 1.6% in the portfolio. Hewlett-Packard is not represented in the Russell Megacap Index. The next two most significant relative contributors were the portfolio’s exclusion of Amazon.com (down 16.2%) and Amgen (down 4.6%). The three largest detractors from relative performance was the exclusion of Walt Disney (up 16.5%) and Union Pacific (up 27.7%), and an exposure to Ford (down 6.3%). Please see the attribution tables below for expanded detail:

Attribution Summary, October 29, 2013 to March 30, 201424

Attribution Summary Tot Average % Weight Contribution to Return Total Return Attr Alloc Selec Port Bench +/- Port Bench +/- Port Bench +/- Fundamental Index 100.00 100.00 0.00 7.81 7.34 0.53 7.81 7.34 0.53 0.47 -0.69 1.16 Financials 17.81 14.64 3.17 1.41 1.05 0.36 8.05 7.28 0.77 0.14 0.00 0.14 Energy 17.12 12.13 4.99 1.75 1.19 0.57 10.36 9.82 0.54 0.21 0.11 0.10 Consumer Staples 14.48 11.87 2.61 0.52 0.43 0.10 3.63 3.63 0.01 -0.10 -0.10 0.00 - Information Technology 13.33 26.90 -13.57 2.00 2.80 0.80 15.48 10.49 4.99 0.21 -0.44 0.65 Health Care 12.39 11.87 0.52 1.30 1.03 0.27 10.55 8.78 1.77 0.22 0.00 0.22 Industrials 8.85 9.28 -0.43 0.64 0.74 -0.10 7.12 7.98 -0.86 -0.08 0.00 -0.08 - Telecommunication Services 8.31 4.41 3.90 -0.21 -0.12 0.09 -2.03 -3.26 1.23 -0.38 -0.49 0.11 Consumer Discretionary 6.62 8.90 -2.28 0.21 0.22 -0.01 3.18 2.40 0.78 0.16 0.13 0.03 Materials 0.69 0.00 0.69 0.18 0.18 28.35 28.35 0.13 0.13 0.00 Cash 0.40 0.00 0.40 0.00 0.00 0.00 0.00 -0.03 -0.03 0.00 The group performed a CAPM and Fama-French regression to further isolate risk factors which contributed to the portfolio’s performance. For these regressions, returns from the Russell Megacap Index were used as the returns for the market portfolio. All other factors were obtained from Kenneth French’s website.25 Initial back-testing results indicated that Fundamental Index portfolios have statistically insignificant alpha, and that these portfolios add value by loading up on small-cap (SmB) and value (HmL) risk factors. Indeed, the Fama-French regression indicates a statistically significant relationship to HmL, with a t-stat of 3.7. Counter to our back-testing results, the regression indicates a negative relationship with SmB, although this relationship is not statistically significant (t stat is -1.2). The portfolio’s positive relationship to HmL contributed to portfolio returns, as HmL returned 1.7% during the observation time period. Meanwhile, the SmB factor returned -0.7% during the time period.

24 Overall portfolio returns are gross of trading fees and may be higher than previously reported. The Russell Megacap ETF (XLG) was used as a proxy for the index to obtain position summary. Returns may differ slightly than index returns previously reported. 25 http://mba.tuck.dartmouth.edu/pages/faculty/ken.french. Accessed May 6, 2014.

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CAPM Factor Decomposition, Daily, October 29, 2013 to March 31, 2014

MKT-Rf R2 Std Error α Fundamental Index 0.01% 0.94 0.95 0.14

CAPM Factor Decomposition,Std Error Daily, October0.01 29, 20130.02 to March 31, 2014 T-Stat 0.55 44.72 2 α MKT-Rf R Std Error Fundamental Index 0.01% 0.94 0.95 0.14 Fama -French FactorStd Er rorDecomposition, 0.01 0.02 Daily, October 29, 2013 to March 31, 2014 T-Stat 0.55 44.72

2 α MKT-Rf SmB HmL Adj. R Std Error FundamentalFama-French Factor Index Decomposition, Daily,0.00% October 29,0.95 2013 to March-0.04 31, 2014 0.17 0.96 0.13

Std Error 0.01 0.02 0.03 0.042 α MKT-Rf SmB HmL Adj. R Std Error Fundamental Index T-Stat 0.00% 0.310.95 50.00-0.04 0.17-1.16 0.963.73 0.13 Std Error 0.01 0.02 0.03 0.04 T-Stat 0.31 50.00 -1.16 3.73

IV. ASAM FELLOWSHIP HIGHLIGHTS IV. ASAM FELLOWSHIP HIGHLIGHTS

DISTINGUISHEDDISTINGUISHED SPEAKER SPEAKER SERIES SERIES AsAs part part ofof its its curriculum, curriculum, ASAM ASAM regularly regularly sponsors sponsors money managers, money industry managers, specialists, industry as well specialists, as faculty members as well as faculty members throughthrough itsits Distinguished Distinguished Speaker Speaker Series. Series.Guest speakers Guest arespeakers invited to are present invited topics to relevantpresent to topics financial relevant management to financial to management to thethe ASAMASAM fellows fellows and and the Andersonthe Anderson community. community. The students The are students encouraged are to encouraged interact and ask to questions interact that and can ask questions that can extendextend theirtheir knowledge knowledge of finance of finance beyond beyond academia. academia. Furthermore, Furthermore, the Distinguished the Speaker Distinguished Series allows Speaker ASAM Seriesto allows ASAM to maintain relationships with these industry professionals from some of the most respected financial firms. maintain relationships with these industry professionals from some of the most respected financial firms. The Distinguished Speaker Series included the following speakers this year: The Distinguished Speaker Series included the following speakers this year: Howard Marks, Founder and Chairman, Oaktree Capital

HowardGrady Smith, Marks, Portfolio Founder Manager, and Dimensional Chairman,Chairman, Fund Oaktree Advisors Capital Grady Smith, Portfolio Manager, Dimensional Fund Advisors GradyJohn Brynjolfsson, Smith, Portfolio CIO, Armored Manager, Wolf Dimensional Fund Advisors JohnNicolas Brynjolfsson, Amato, Partner CIO, and Armored Director of WolfResearch & Risk Management, Dorchester Capital Advisors NicolasJohn Brynjolfsson, Amato, Partner CIO, Armoredand Director Wolf of Research & Risk Management, Dorchester Capital Advisors Kirk Hartman, President and CIO, Wells Capital Management KirkNicolas Hartman, Amato, President Partner and and Director CIO, Wells of Research Capital Management & Risk Management, Dorchester Capital Advisors Richard Roll, Joel Fried Chair in Applied Finance, Distinguished Professor - UCLA Anderson Richard Roll, Joel Fried Chair in Applied Finance, Distinguished Professor - UCLA Anderson KirkAvanidhar Hartman, Subrahmanyam, President Hearsh and CIO, Chair Wells in Money Capit andal Banking, Management Professor – UCLA Anderson Avanidhar Subrahmanyam, Hearsh Chair in Money and Banking, Professor – UCLA Anderson JasonRichardJason Hsu, Roll, Assistan Assistant Joel tFried Adjunct Adjunct Chair Professor in Professor Applied – UCLA –Finance,Anderson UCLA Anderson &Distinguished CIO and C &o- Founder,CIO Professor and Research Co-Founder, - UCLA Affiliates Anderson Research Affiliates Eric Dhall, Associate, Doubleline Capital EricAvanidhar Dhall, Associate,Subrahmanyam, Doubleline Hearsh Capital Chair in Money and Banking, Professor – UCLA Anderson Byron Douglass, VP Insurance Portfolio Management, AIG Investments Jason Hsu, Assistant Adjunct Professor – UCLA Anderson & CIO and Co-Founder, Research Affiliates 59 Hezy Shalev, Partner, Luminous Capital MacduffEric Dhall, Kuhnert,Associate, Portfolio Doubleline Manager Capital & Joe Gubler, Research Associate, Causeway Capital Gary Baierl PhD, CIO, Strategic Global Advisors Andres Berkin PhD, Director of Research, Bridgeway Capital 59 Patrick Kiefer, Doctoral Graduate Student - Finance, UCLA Anderson Jiasun Li, Doctoral Graduate Student - Finance, UCLA Anderson

The ASAM Class of 2014 would like to thank these individuals who generously volunteered their time and shared their knowledge of finance outside of our academic curriculum.

60 | UCLA Anderson Student Asset Management Annual Report

Byron Douglass, VP Insurance Portfolio Management, AIG Investments

Hezy Shalev, Partner, Luminous Capital

Macduff Kuhnert, Portfolio Manager & Joe Gubler, Research Associate, Causeway Capital

Gary Baierl PhD, CIO, Strategic Global Advisors

Andres Berkin PhD, Director of Research, Bridgeway Capital

Patrick Kiefer, Doctoral Graduate Student - Finance, UCLA Anderson Byron Douglass, VP Insurance Portfolio Management, AIG Investments Jiasun Li, Doctoral Graduate Student - Finance, UCLA Anderson Hezy Shalev, Partner, Luminous Capital The ASAM Class of 2014 would like to thank these individuals who generously volunteered their time and shared their Macduff Kuhnert, Portfolio Manager & Joe Gubler, Research Associate, Causeway Capital knowledge of finance outside of our academic curriculum. Gary Baierl PhD, CIO, Strategic Global Advisors

Andres Berkin PhD, Director of Research, Bridgeway Capital FIRM VISITS Patrick Kiefer, Doctoral Graduate Student - Finance, UCLA Anderson Each year, ASAM student managers visit respected money management firms in the Southern California, the Bay Area, and NewJiasun York Li, Doctoral City to Graduate learn about Student the - Finance,latest investment UCLA Anderson strategies and stay closely connected to the Asset Management Industry.The ASAM The Cl assfollowing of 2014 firms would hosted like to usthank and these gave individuals us a great who opportunity generously to volunteered interact with their their time portfolio and shared managers their and researchknowledge analysts. of finance outside of our academic curriculum.

Los Angeles/Orange County: San Francisco: New York:

FIRM CapitalVISITS Group BlackRock 3i Investments Each year,DFA ASAM student managers visit Contangorespected Capitalmoney Advisorsmanagement firmsAIG in Investments the Southern California, the Bay Area, and New York City to learn about the latest investment strategies and stay closely connected to the Asset Management DoubleLine Franklin Templeton Investments Bloomberg Industry. The following firms hosted us and gave us a great opportunity to interact with their portfolio managers and research analysts. Oaktree Capital Hall Capital Partners Guggenheim LosLos Angeles/Orange Angeles/Orange County: County: SanSan Francisco: Francisco: NewNew York: York: CapitalPAAMCO Group BlackRockOsterweis Capital Management ICAP3i Investments DFACapital Group ContangoBlackRock Capital Advisors 3i InvestmentsAIG Investments PIMCO PineBridge Investments DoubleLine Franklin Templeton Investments Bloomberg DFA Contango Capital Advisors AIG Investments OaktreePineBridge Capital Investments Hall Capital Partners SunAmericaGuggenheim Asset Management Corp. PAAMCO Osterweis Capital Management ICAP DoubleLine Franklin Templeton Investments Bloomberg PIMCOPRIMECAP management PineBridge Investments PineBridge Investments SunAmerica Asset Management Corp. Oaktree Capital Hall Capital Partners Guggenheim PRIMECAPResearch managementAffiliates ResearchPAAMCO Affiliates Osterweis Capital Management ICAP TCWTCW WAMCOPIMCO PineBridge Investments WAMCO PineBridge Investments SunAmerica Asset Management Corp. Firm visits are extremely beneficial and give ASAM Fellows a well-rounded view of the Asset Management industry.

Through PRIMECAPthese firm visits,management ASAM fellows learn about the firms’ organizational structure, their strategic goals, investment strategies and processes, and in some cases, their outlook for future market conditions. Additionally, time is generally allotted duringResearch these Affiliates visits to discuss advice and feedback with respect to pursuing career opportunities in the investment management industry. Since one of the important functions of ASAM is to provide a platform for members to develop60 a requisite TCWskill set and to successfully transition to a career in investment management, the career advice and interview tips given by the firms’ managers during the visits were of tremendous value. The ASAM Class of 2014 would like to sincerely thank all WAMCOof the portfolio managers, research analysts and the human resource departments at these firms for making our visits a memorable experience. Firm visits are extremely beneficial and give ASAM Fellows a well-rounded view of the Asset Management industry. ASAMThrough Dinners these firm visits, ASAM fellows learn about the firms’ organizational structure, their strategic goals, investment strategies and processes, and in some cases, their outlook for future market conditions. Additionally, time is generally The 2014 ASAM class carried on a new tradition of dinners with distinguished practitioners in the field of investment management. In the spring we joined members of the 2013 class for intimate dinners arranged with industry legends 60 Howard Marks of Oaktree Capital and Rick Kayne of Kayne Anderson. In the fall we broke bread with former PIMCO executive and current hedge fund manager John Brynjolfsson, who enlightened us on the niche world of catastrophe bonds. This winter we were also honored to entertain two guests from the Chicago Quantitative Alliance, Dan Cardell and Sandip Bhagat, along with UCLA Assistant Professor Ehud Peleg, over dinner and drinks in Westwood. These social events went far to raise the level of awareness of ASAM in both the academic community and investment industry.

BUFFET TRIP We at ASAM were thrilled in finally getting access to Warren Buffett and Berkshire Hathaway this past year. Buffett hosts several schools each year with a decreasing number of schools each year and more limited access due to his age. Following up on last year’s class’ persistence and our relentless push, we secured a spot on this roster. On January 31st, 2014, we visited Berkshire Hathaway. UCLA Anderson was one of eight schools invited that day; each school was allotted 20 student slots. The UCLA Anderson group included 9 ASAM members (8 current and one former) as well as 7 members from the Student Investment Fund, and 3 members from the Anderson Investment Association. UCLA Anderson Student Asset Management Annual Report | 61

The following article summarizes our trip and ran in the Anderson Exchange – Anderson’s own paper:

UCLA Anderson Students Meet Warren Buffet

Veronica Kalyna (FEMBA 2015)

January 31, 2014 – Omaha, Nebraska

On a chilly winter morning, 20 UCLA Anderson students, members of the Anderson Student Asset Management Fund, Student Investment Fund, and the Anderson Investment Association, began the pilgrimage to meet the Oracle of Omaha, Mr. Warren Buffett. Along with students from 6 other U.S. universities and a group of Brazilian business students, we began the day at Nebraska Furniture Mart (NFM), the first of three Berkshire Hathaway-owned company visits. As we walked through the flagship store, we learned about the “Historic Omaha Handshake” and the two-page contract by which Warren Buffet and Rose Blumkin, NFM’s founder, sealed the sale of NFM to Berkshire Hathaway in 1983. Mrs. B’s simple business tenet of “Sell cheap and tell the truth” has positioned the retailer to rapidly expand in the Mid-West.

Our next stop was Berkshire Hathaway’s headquarters, inconspicuously located on the 15th floor of the Keiwit Building. Stepping off the top floor, we entered the marble-and-wood paneled Cloud Room and enjoyed cold cans of Coca-Cola while waiting for the Oracle to arrive. Soon after Mr. Buffett made his entrance, a two-hour question and answer ensued. With great candor and modesty, Mr. Buffett spoke on a wide range of topics. While holding a 1951 Moody’s Manual, he quipped, “People who want to relive their youth buy old Playboys. I buy old Moody’s.” Mr. Buffett spoke at length of his philosophy of investing in simple, undervalued

61

allotted during these visits to discuss advice and feedback with respect to pursuing career opportunities in the investment management industry. Since one of the important functions of ASAM is to provide a platform for members to develop a requisite skill set and to successfully transition to a career in investment management, the career advice and interview tips given by the firms’ managers during the visits were of tremendous value. The ASAM Class of 2014 would like to sincerely thank all of the portfolio managers, research analysts and the human resource departments at these firms for making our visits a memorable experience.

ASAM Dinners

The 2014 ASAM class carried on a new tradition of dinners with distinguished practitioners in the field of investment management. In the spring we joined members of the 2013 class for intimate dinners arranged with industry legends Howard Marks of Oaktree Capital and Rick Kayne of Kayne Anderson. In the fall we broke bread with former PIMCO executive and current hedge fund manager John Brynjolfsson, who enlightened us on the niche world of catastrophe bonds. This winter we were also honored to entertain two guests from the Chicago Quantitative Alliance, Dan Cardell and Sandip Bhagat, along with UCLA Assistant Professor Ehud Peleg, over dinner and drinks in Westwood. These social events went far to raise the level of awareness of ASAM in both the academic community and investment industry.

firms with solid operations and management teams, as well as his hands-off management style. He also spoke about how Berkshire BUFFET TRIP Hathaway’s size has led him and his long-time business partner and friend, Charlie Munger, to “hunt for elephants” – a metaphor for We at ASAM were thrilled in finally getting access to Warren Buffett and Berkshire Hathaway this past year. Buffett hosts deals in excess of $1 Billion – as smaller deals, though more appealing, do not provide significant returns to his investors. However, several schools each year with a decreasing number of schools each year and more limited access due to his age. Following not all questions were about the markets and investing. Mr. Buffet spoke greatly about personal and social matters such as up on last year’s class’ persistence and our relentless push, we secured a spot on this roster. On January 31st, 2014, we philanthropy and the pointlessness of ostentatious lifestyles among the wealthy. Some excerpts from his speech: visited Berkshire Hathaway. UCLA Anderson was one of eight schools invited that day; each school was allotted 20 student On friends: “Associate with people you think are better than you and you’ll start behaving like them.” slots. The UCLA Anderson group included 9 ASAM members (8 current and one former) as well as 7 members from the Student Investment Fund, and 3 members from the Anderson Investment Association. On passion: “The degree at which people who love what they do jumps out so much more from the crowd. Too many people are sleepwalking through life…you want to do something you love and do it with people you like.” The following article summarizes our trip and ran in the Anderson Exchange – Anderson’s own paper: On marriage: “The most important investment decision is who you marry.” UCLA Anderson Students Meet Warren Buffet On philanthropy: “Every life has equal value…Do things that you know will work to improve people’s lives.” Veronica Kalyna (FEMBA 2015) On his approach to investing: “Emotional stability and guts.” January 31, 2014 – Omaha, Nebraska On his lifestyle: “You [the students] and I pretty much wear the same kind of clothes, eat the same sorts of food, enjoy spending time On a chilly winter morning, 20 UCLA Anderson students, members of the Anderson Student Asset Management Fund, with our friends, watch football, and live in comparable neighborhoods. Other than travel, where I travel on a private jet, our lives are Student Investment Fund, and the Anderson Investment Association, began the pilgrimage to meet the Oracle of Omaha, Mr. not that different. I hope you too spend your time doing what you enjoy.” Warren Buffett. Along with students from 6 other U.S. universities and a group of Brazilian business students, we began the day at Nebraska Furniture Mart (NFM), the first of three Berkshire Hathaway-owned company visits. As we walked through the flagship After the Q&A session, we lunched at Piccolo Pete’s, an Omaha favorite that is also owned by Berkshire. Here, we enjoyed store, we learned about the “Historic Omaha Handshake” and the two-page contract by which Warren Buffet and Rose Blumkin, the Oracle’s favorite treat, root beer floats, while feasting on steak, salad, and fries. The root beer floats were a first for many of the NFM’s founder, sealed the sale of NFM to Berkshire Hathaway in 1983. Mrs. B’s simple business tenet of “Sell cheap and tell the international MBA students. Before leaving Piccolo Pete’s, our group presented Mr. Buffett with a signed copy of Security Analysis by truth” has positioned the retailer to rapidly expand in the Mid-West. Benjamin Graham and an UCLA Anderson baseball cap, which he kindly wore during the group photo shoot. We thanked Mr. Buffett for hosting us and as we left the restaurant, he drove off in his brown 2006 Cadillac DTS – a symbol of Mr. Buffett’s choice to live Our next stop was Berkshire Hathaway’s headquarters, inconspicuously located on the 15th floor of the Keiwit Building. simply. Stepping off the top floor, we entered the marble-and-wood paneled Cloud Room and enjoyed cold cans of Coca-Cola while waiting for the Oracle to arrive. Soon after Mr. Buffett made his entrance, a two-hour question and answer ensued. With great candor and The next stop was a shopping center housing Borsheims Jewelry, a family-owned jewelry store purchased by Berkshire in modesty, Mr. Buffett spoke on a wide range of topics. While holding a 1951 Moody’s Manual, he quipped, “People who want to relive 1989. Borsheims emphasizes its Mid-Western friendliness and thrift in providing the lowest prices and unsurpassed customer service their youth buy old Playboys. I buy old Moody’s.” Mr. Buffett spoke at length of his philosophy of investing in simple, undervalued in the high-end jewelry market. Borsheims is also the location of Berkshire’s Annual Shareholder Meeting that takes place each firms with solid operations and management teams, as well as his hands-off management style. He also spoke about how Berkshire spring. We had the opportunity to shop and receive the “Buffett” discount on designer watches and fine jewelry. Hathaway’s size has led him and his long-time business partner and friend, Charlie Munger, to “hunt for elephants” – a metaphor for 61 deals in excess of $1 Billion – as smaller deals, though more appealing, do not provide significant returns to his investors. However, The final stop was to the Oriental Trading Company (OTC), a party supply distributor purchased by Berkshire in 2012. We not all questions were about the markets and investing. Mr. Buffet spoke greatly about personal and social matters such as met Sam Taylor, OTC’s CEO and a Los Angeles native, who presented us with special edition Warren Buffet and Charlie Munger philanthropy and the pointlessness of ostentatious lifestyles among the wealthy. Some excerpts from his speech: rubber duckies. We toured the company’s impressive 1.5 million sq. ft. warehouse and distribution center, one of the largest and most efficient in North America. Housing over 100 million items and 4 miles of conveyor belts, OTC processes approximately 40,000 On friends: “Associate with people you think are better than you and you’ll start behaving like them.” items per hour at a 98.5% service level. For many of us, the aisles after aisles of party supplies brought flashbacks of Operations Management and a greater appreciation for inventory management. On passion: “The degree at which people who love what they do jumps out so much more from the crowd. Too many people are sleepwalking through life…you want to do something you love and do it with people you like.” On our return trip to sunny California, we felt fortunate for the unique chance and couldn’t help but to reflect on the Oracle’s advice, “Follow your passion, work for a company you admire, surround yourself with people you love and who love you On marriage: “The most important investment decision is who you marry.” back.” No words ring truer to aspiring MBAs. On philanthropy: “Every life has equal value…Do things that you know will work to improve people’s lives.” M. Reza Banki (FTMBA 14) with help from Aylon Ben-Shlomo (FTMBA 14) arranged, planned and organized the UCLA On his approach to investing: “Emotional stability and guts.” Anderson trip to Omaha to see Mr. Buffett and contributed to this article.

On his lifestyle: “You [the students] and I pretty much wear the same kind of clothes, eat the same sorts of food, enjoy spending time In order from left: Veronica Kalyna (FEMBA 2015), Constantinos (Dean) Pagonis (FTMBA 2014), Nedal Alqam (FEMBA 2015), Debika Seth (FEMBA 2015), Jernine Kim (FTMBA 2014), Ksenia Yudina (FEMBA 2014), Alex Jorion (FEMBA 2015), Joseph Duronio (FEMBA 2015), M. Reza Banki (FTMBA 2014), Ryan Rosen (FTMBA 2014), Warren with our friends, watch football, and live in comparable neighborhoods. Other than travel, where I travel on a private jet, our lives are Buffett, Tom Morgan (FTMBA 2014), Sean Haydon (FTMBA 2014), Danielle Zainer (FTMBA 2014), Jacob Gore (FTMBA 2014), Beth Sackovich (FTMBA 2014), Kevin Zhang not that different. I hope you too spend your time doing what you enjoy.” (FEMBA 2015), Wenting Shen (FTMBA 2014), Aylon Ben-Shlomo (FTMBA 2014), Jason Stokes (FEMBA 2015), Ignacio Silva (FTMBA 2014).

After the Q&A session, we lunched at Piccolo Pete’s, an Omaha favorite that is also owned by Berkshire. Here, we enjoyed 62 the Oracle’s favorite treat, root beer floats, while feasting on steak, salad, and fries. The root beer floats were a first for many of the 62 | UCLA Anderson Student Asset Management Annual Report international MBA students. Before leaving Piccolo Pete’s, our group presented Mr. Buffett with a signed copy of Security Analysis by Benjamin Graham and an UCLA Anderson baseball cap, which he kindly wore during the group photo shoot. We thanked Mr. Buffett for hosting us and as we left the restaurant, he drove off in his brown 2006 Cadillac DTS – a symbol of Mr. Buffett’s choice to live simply.

The next stop was a shopping center housing Borsheims Jewelry, a family-owned jewelry store purchased by Berkshire in 1989. Borsheims emphasizes its Mid-Western friendliness and thrift in providing the lowest prices and unsurpassed customer service in the high-end jewelry market. Borsheims is also the location of Berkshire’s Annual Shareholder Meeting that takes place each spring. We had the opportunity to shop and receive the “Buffett” discount on designer watches and fine jewelry.

The final stop was to the Oriental Trading Company (OTC), a party supply distributor purchased by Berkshire in 2012. We met Sam Taylor, OTC’s CEO and a Los Angeles native, who presented us with special edition Warren Buffet and Charlie Munger rubber duckies. We toured the company’s impressive 1.5 million sq. ft. warehouse and distribution center, one of the largest and most efficient in North America. Housing over 100 million items and 4 miles of conveyor belts, OTC processes approximately 40,000 items per hour at a 98.5% service level. For many of us, the aisles after aisles of party supplies brought flashbacks of Operations Management and a greater appreciation for inventory management.

On our return trip to sunny California, we felt fortunate for the unique chance and couldn’t help but to reflect on the Oracle’s advice, “Follow your passion, work for a company you admire, surround yourself with people you love and who love you back.” No words ring truer to aspiring MBAs.

M. Reza Banki (FTMBA 14) with help from Aylon Ben-Shlomo (FTMBA 14) arranged, planned and organized the UCLA Anderson trip to Omaha to see Mr. Buffett and contributed to this article.

In order from left: Veronica Kalyna (FEMBA 2015), Constantinos (Dean) Pagonis (FTMBA 2014), Nedal Alqam (FEMBA 2015), Debika Seth (FEMBA 2015), Jernine Kim (FTMBA 2014), Ksenia Yudina (FEMBA 2014), Alex Jorion (FEMBA 2015), Joseph Duronio (FEMBA 2015), M. Reza Banki (FTMBA 2014), Ryan Rosen (FTMBA 2014), Warren Buffett, Tom Morgan (FTMBA 2014), Sean Haydon (FTMBA 2014), Danielle Zainer (FTMBA 2014), Jacob Gore (FTMBA 2014), Beth Sackovich (FTMBA 2014), Kevin Zhang (FEMBA 2015), Wenting Shen (FTMBA 2014), Aylon Ben-Shlomo (FTMBA 2014), Jason Stokes (FEMBA 2015), Ignacio Silva (FTMBA 2014).

62

firms with solid operations and management teams, as well as his hands-off management style. He also spoke about how Berkshire Hathaway’s size has led him and his long-time business partner and friend, Charlie Munger, to “hunt for elephants” – a metaphor for deals in excess of $1 Billion – as smaller deals, though more appealing, do not provide significant returns to his investors. However, not all questions were about the markets and investing. Mr. Buffet spoke greatly about personal and social matters such as philanthropy and the pointlessness of ostentatious lifestyles among the wealthy. Some excerpts from his speech:

On friends: “Associate with people you think are better than you and you’ll start behaving like them.”

On passion: “The degree at which people who love what they do jumps out so much more from the crowd. Too many people are sleepwalking through life…you want to do something you love and do it with people you like.”

On marriage: “The most important investment decision is who you marry.”

On philanthropy: “Every life has equal value…Do things that you know will work to improve people’s lives.”

On his approach to investing: “Emotional stability and guts.”

On his lifestyle: “You [the students] and I pretty much wear the same kind of clothes, eat the same sorts of food, enjoy spending time with our friends, watch football, and live in comparable neighborhoods. Other than travel, where I travel on a private jet, our lives are not that different. I hope you too spend your time doing what you enjoy.” firms with solid operations and management teams, as well as his hands-off management style. He also spoke about how Berkshire After the Q&A session, we lunched at Piccolo Pete’s, an Omaha favorite that is also owned by Berkshire. Here, we enjoyed Hathaway’s size has led him and his long-time business partner and friend, Charlie Munger, to “hunt for elephants” – a metaphor for the Oracle’s favorite treat, root beer floats, while feasting on steak, salad, and fries. The root beer floats were a first for many of the deals in excess of $1 Billion – as smaller deals, though more appealing, do not provide significant returns to his investors. However, international MBA students. Before leaving Piccolo Pete’s, our group presented Mr. Buffett with a signed copy of Security Analysis by not all questions were about the markets and investing. Mr. Buffet spoke greatly about personal and social matters such as Benjamin Graham and an UCLA Anderson baseball cap, which he kindly wore during the group photo shoot. We thanked Mr. Buffett philanthropy and the pointlessness of ostentatious lifestyles among the wealthy. Some excerpts from his speech: for hosting us and as we left the restaurant, he drove off in his brown 2006 Cadillac DTS – a symbol of Mr. Buffett’s choice to live Onsimply. friends: “Associate with people you think are better than you and you’ll start behaving like them.”

On passion:The “The next degree stop wasat which a shopping people center who love housing what Borsheimsthey do jumps Jewelry, out so a familymuch -moreowned from jewelry the crowd. store purchased Too many by people Berkshire are in sleepwalking1989. Borsheims through emphasizes life…you itswant Mid to-Western do something friendliness you love and and thrift do init withproviding people the you lowest like.” prices and unsurpassed customer service in the high-end jewelry market. Borsheims is also the location of Berkshire’s Annual Shareholder Meeting that takes place each Onspring. marriage: We had “The the most opportunity important to investmentshop and receive decision the is “Buffett” who you discount marry.” on designer watches and fine jewelry.

On philanthropy: The final “Every stop life was has to equalthe Oriental value…Do Trading things Company that you (OTC), know will a party work supply to improve distributor people’s purchased lives.” by Berkshire in 2012. We met Sam Taylor, OTC’s CEO and a Los Angeles native, who presented us with special edition Warren Buffet and Charlie Munger Onrubber his approach duckies. Weto investing: toured the “Emotional company’s stability impressive and guts.”1.5 million sq. ft. warehouse and distribution center, one of the largest and most efficient in North America. Housing over 100 million items and 4 miles of conveyor belts, OTC processes approximately 40,000 On his lifestyle: “You [the students] and I pretty much wear the same kind of clothes, eat the same sorts of food, enjoy spending time items per hour at a 98.5% service level. For many of us, the aisles after aisles of party supplies brought flashbacks of Operations with our friends, watch football, and live in comparable neighborhoods. Other than travel, where I travel on a private jet, our lives are Management and a greater appreciation for inventory management. not that different. I hope you too spend your time doing what you enjoy.” On our return trip to sunny California, we felt fortunate for the unique chance and couldn’t help but to reflect on the After the Q&A session, we lunched at Piccolo Pete’s, an Omaha favorite that is also owned by Berkshire. Here, we enjoyed Oracle’s advice, “Follow your passion, work for a company you admire, surround yourself with people you love and who love you the Oracle’s favorite treat, root beer floats, while feasting on steak, salad, and fries. The root beer floats were a first for many of the back.” No words ring truer to aspiring MBAs. international MBA students. Before leaving Piccolo Pete’s, our group presented Mr. Buffett with a signed copy of Security Analysis by Benjamin GrahamM. Reza and Banki an (FTMBA UCLA Anderson 14) with baseball help from cap, Aylon which Ben he-Shlomo kindly wore (FTMBA during 14) the arranged, group photo planned shoot and. We organized thanked the Mr. UCLA Buffett forAnderson hosting ustrip and to Omahaas we left to thesee restaurant,Mr. Buffett heand drove contributed off in his to brown this article. 2006 Cadillac DTS – a symbol of Mr. Buffett’s choice to live simply. In order from left: Veronica Kalyna (FEMBA 2015), Constantinos (Dean) Pagonis (FTMBA 2014), Nedal Alqam (FEMBA 2015), Debika Seth (FEMBA 2015), Jernine Kim (FTMBA 2014),The nextKsenia stop Yudina was (FEMBA a shopping 2014), Alex center Jorion (FEMBAhousing 2015), Borsheims Joseph Duronio Jewelry, (FEMBA a family 2015), -M.owned Reza Banki jewelry (FTMBA stor 2014),e purchased Ryan Rosen by (FTMBA Berkshire 2014), inWarren 1989.Buffett, Borsheims Tom Morgan emphasizes (FTMBA 2014), its Sean Mid Haydon-Western (FTMBA friendliness 2014), Danielle and Zainer thrift (FTMBA in providing 2014), Jacob the Gore lowest (FTMBA prices 2014), and Beth unsurpassedSackovich (FTMBA customer 2014), Kevin service Zhang (FEMBA 2015), Wenting Shen (FTMBA 2014), Aylon Ben-Shlomo (FTMBA 2014), Jason Stokes (FEMBA 2015), Ignacio Silva (FTMBA 2014). in the high-end jewelry market. Borsheims is also the location of Berkshire’s Annual Shareholder Meeting that takes place each spring. We had the opportunity to shop and receive the “Buffett” discount on designer watches and fine jewelry. 62 The final stop was to the Oriental Trading Company (OTC), a party supply distributor purchased by Berkshire in 2012. We met Sam Taylor, OTC’s CEO and a Los Angeles native, who presented us with special edition Warren Buffet and Charlie Munger rubber duckies. We toured the company’s impressive 1.5 million sq. ft. warehouse and distribution center, one of the largest and most efficient in North America. Housing over 100 million items and 4 miles of conveyor belts, OTC processes approximately 40,000 items per hour at a 98.5% service level. For many of us, the aisles after aisles of party supplies brought flashbacks of Operations Management and a greater appreciation for inventory management.

On our return trip to sunny California, we felt fortunate for the unique chance and couldn’t help but to reflect on the Oracle’s advice, “Follow your passion, work for a company you admire, surround yourself with people you love and who love you back.” No words ring truer to aspiring MBAs.

M. Reza Banki (FTMBA 14) with help from Aylon Ben-Shlomo (FTMBA 14) arranged, planned and organized the UCLA Anderson trip to Omaha to see Mr. Buffett and contributed to this article.

In order from left: Veronica Kalyna (FEMBA 2015), Constantinos (Dean) Pagonis (FTMBA 2014), Nedal Alqam (FEMBA 2015), Debika Seth (FEMBA 2015), Jernine Kim (FTMBA 2014), Ksenia Yudina (FEMBA 2014), Alex Jorion (FEMBA 2015), Joseph Duronio (FEMBA 2015), M. Reza Banki (FTMBA 2014), Ryan Rosen (FTMBA 2014), Warren Buffett, Tom Morgan (FTMBA 2014), Sean Haydon (FTMBA 2014), Danielle Zainer (FTMBA 2014), Jacob Gore (FTMBA 2014), Beth Sackovich (FTMBA 2014), Kevin Zhang (FEMBA 2015), Wenting Shen (FTMBA 2014), Aylon Ben-Shlomo (FTMBA 2014), Jason Stokes (FEMBA 2015), Ignacio Silva (FTMBA 2014).

62

UCLA Anderson Student Asset Management Annual Report | 63

ACCOMPLISHMENTS AND HONORS

This year’s ASAM Students also competed in a number of competitions, and received high honors and accolades in a number of areas worth noting. ASAM 2014s Joseph Duronio, Kevin Zhang and Alexandre Jorion, along with ASAM 2015 Zack Conroy, competed as a team against over 200 other students from dozens of top business schools to take first place in the 2014 National Investment Banking Competition (“NIBC”) in Vancouver. The team won for both their quantitative analysis as well as their presentation in front of senior executives from a number of top investment banks.

Recently, Reza Banki delivered a distinguished and highly acclaimed Campfire TED Talk to students, faculty and guests during UCLA Anderson’s Ted Week 2014. In addition, Jason Stokes led a team to the KNAPP Business Plan Competition Semi Finals with a business plan for a global lending company.

Most notably, we would like to honor the recent 2013 ASAM alumni who have dominated the J. Fred Weston Award for excellence in finance for the past two years including 2013 winners Farshid Pousartip, Felix Lorenzo, and Mahyar Kagar, along with 2014 winners Ryan Hughes and Kesnia Yudina. Their academic success is an inspiration and further demonstrates the value of ASAM fellows in the UCLA Anderson and broader financial community.

ASAM Fellows Recent Internships, Jobs and Opportunities

Current and recent ASAM fellows have also been rewarded for their efforts and skills with a number of opportunities in the academic and professional realm. During the year, Kevin Zhang received a competitive internship in the treasury department of Pacific Life Insurance Company, and then received yet another summer internship at Western Asset Management Company. Joseph Duronio, changed roles from Legal Counsel to Associate Director of Strategic Initiatives at American International Group. Thomas Gotsch received a full-time opportunity at the boutique investment bank, MHT MidSpan, in San Francisco. Tommy Taw also received a full-time opportunity as a consultant in the financial consulting division of Ernst and Young in San Francisco.

From the class of 2013, both the ASAM President Andy Yin and the ASAM Vice President Doug Longo have taken full-time opportunities with Dimensional Fund Advisers. Julian Serafini has taken a position with Oppenheimer Funds in New York City. Mahyar Kargar has entered the PhD program in finance here at UCLA Anderson. Felix Lorenzo is now the Head of Andean Region and Portfolio Manager at LarrainVial in Chile. Brian Sterz has accepted a position in Commercial Real Estate at Charles Dunn Company after completing an investment banking internship with Bank of America Merrill Lynch in Los Angeles. Ksenia Yudina also completed a coveted wealth management internship with JP Morgan. Finally, we would like to congratulate Ryan Hughes who recently opened his own investment advisory firm, Bull Oak Capital.

RECRUITING: In the spring quarter of every year, current class of Anderson Student Asset Management selects the next class of student managers from a pool of interested students. Through orientations, social events and active marketing effort, this year’s recruiting initiative was successful, offering a large pool of highly qualified candidates to select the future ASAM fellows from. We received strong interest from the full-time MBA program, FEMBA program, FEMBA FLEX program, as well as EMBA program at UCLA Anderson. As usual, the selection process for the ASAM class of 2015 was extremely competitive. After much thought and consideration, based on the skill sets and fit, we have selected 16 ASAM fellows with various work experiences and educational backgrounds to form the ASAM class of 2015.

63

64 | UCLA Anderson Student Asset Management Annual Report

To showcase our strong and promising new class, here are a few highlights of their achievements.

-­‐ 2 PHD degrees -­‐ 5 CFA designations -­‐ 1 CPA -­‐ 1 Attorney

The current ASAM class of 2014 is pleased to announce the ASAM Class of 2015. Please join us in congratulating the new ASAM Fellows:

Andrew Holloway David Cruz, CFA Jackie Chan, CFA Razmig Der-Tavitian, CFA, CAIA

Chris Carlson David Soong James Wooten Shireesh Verma, PHD

Chris Martinez, CFA George Ku Jeff Martin Stephany Anavim

Dan Troost Han Park, PHD Jonathan Lea, CPA Zack Conroy UCLA ASAM Class of 2014: Joseph Duronio, President Joseph Duronio is a practicing securities lawyer and serves as Counsel at AIG Life and Retirement, where he specializes in investment company law, federal securities law and insurance product regulation. Previously, Joe worked as Counsel at SunAmerica Asset Management Company, AIG's investment adviser with over $54.8 billion in assets. Joe graduated from New York University in 2005 with a bachelor's degree in both mathematics and philosophy. In 2008, he received his Juris Doctorate with a concentration in intellectual property law from Seton Hall University School of Law and is a member of the New York and New Jersey Bar. Joe is currently a student in the UCLA Anderson FEMBA program and is pursuing a concentration in finance.

Thomas Gotsch, Vice President Tom graduated with distinction from California Polytechnic State University, San Luis Obispo with B.S. and M.S. degrees in Industrial Engineering. After graduating, Tom immediately joined Raytheon where he held a variety of roles, ultimately attaining the role of Program Manager where he led multiple multi-million dollar thermal-imaging programs. Tom recently completed an associate level internship at Cappello Capital, one of the largest boutique investment banks in Southern California. During his time at UCLA Anderson, Tom has worked to leverage his strong quantitative background and focus on developing a career in finance. Tom has a passion for financial markets and a strong interest in value investing.

Nedal Alqam, Chief Investment Officer Nedal Alqam is a servicing specialist at Pacific Investment Management Company (PIMCO), focusing on institutional client servicing. He is also a member of PIMCO's Total Return Strategy product management team. Prior to joining PIMCO in 2008, he spent one year at KPMG as an international tax associate, preparing inpatriate and expatriate returns for high wealth executives. He holds an undergraduate degree in finance from California State University, Fullerton. He is currently pursuing an MBA at the Anderson School of Management at the University of California, Los Angeles and is a CFA Level III candidate.

Reza Banki, Buffet Visit Coordinator M. Reza Banki is a full-time student at UCLA Anderson and focusing on the finance track. Reza spent several years at McKinsey & Company in New York as a senior associate in management consulting working in private equity and M&A projects mostly in the pharmaceutical and medical device space. He graduated from the PhD program in chemical engineering from Princeton University with a thesis in biotechnology in 2005. He also received a BS in chemical engineering and a BA in mathematics from University of California at Berkeley.

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UCLA Anderson Student Asset Management Annual Report | 65

Matt Corbitt, Co-Guest Speaker Coordinator

Matt is a Vice President at Wells Fargo Securities in the Fixed Income Sales & Trading division. In his role, Matt provides fixed income investment Matt Corbitt, advisory Co and-Guest brokerage Speaker services Coordinator to buy-side institutional clients including public and private corporations, universities, municipalitiesMatt is a Vice Presidentand asset atmanagers. Wells Fargo Prior Securities to joining in Wells the Fixed Fargo Income Securities, Sales Matt & Trading was a Principaldivision. Inat hisa large role, independent Matt provides broker fixed- dealerincome investmentwhere he supervised advisory andthe salesbrokerage and trading services of to equities, buy-side fixed institutional income, and clien derivatives.ts including Matt public began and hisprivate career corporations, at Wells Fargo universities, Investments municipalitiesas a Private Wealth and asset Advisor managers. to high Priornet worth to joining individuals. Wells Fargo He received Securities, a BA Matt in Political was a PrincipalScience fromat a large the University independent of Southern broker-dealer whereCalifornia he supervisedand is currently the sales enrolled and tradingin the Fully of equities, Employed fixed MBA income, program and atderivatives. UCLA Anderson Matt began pursuing his career a degree at Wellsfocused Fargo on finance Investments and asreal a estate.Private MattWealth is a Advisor FINRA licensedto high net General worth Securities individuals. Principal He received and a amember BA in Political of Mensa. Science from the University of Southern California and is currently enrolled in the Fully Employed MBA program at UCLA Anderson pursuing a degree focused on finance and real estate. Matt is a FINRA licensed General Securities Principal and a member of Mensa. Nail Edikhanov, Firm Visit Coordinator

Nail Edikhanov is a full-time student in the UCLA Anderson School of Management program focusing on finance. Previously he worked inNail Russian Edikhanov, investment Firm management Visit Coordinator companies and managed more than $45 mil portfolio of Russian stock and bonds. Nail is a CFA Nailcharterholder. Edikhanov He is a graduated full-time studentfrom Moscow in the U StateCLA IAndersonnstitute of School International of Management Relations programin 2006 withfocusing a Master on finance. in Economics Previously degree. he worked in Russian investment management companies and managed more than $45 mil portfolio of Russian stock and bonds. Nail is a CFA charterholder. He graduated from Moscow State Institute of International Relations in 2006 with a Master in Economics degree. Jacob Gore , Co-Risk Manager

Jacob graduated from the University of Michigan with a BSE in Mechanical Engineering and a BSE in Industrial and Operations Engineering.Jacob Gore Prior , Co to- Anderson,Risk Manager he worked for The Boeing Company as an industrial engineer, improving the efficiency of production and supplyJacob graduated chains for fromaircraft the interiors. University During of Michigan his time with at Boeing, a BSE in Jacob Mechanical completed Engineering a rigorous and Computational a BSE in Industrial Finance and Certificate Operations program at thEngineering.e University Prior of Washington to Anderson, that he focusedworked foron Thethe use Boeing of R Company programming as an for industrial financial engineer, modeling. improving He is currently the efficiency a full-time of production MBA student and andsupply enjoys chains hiking for aircraftand home interiors. beer brewing. During his time at Boeing, Jacob completed a rigorous Computational Finance Certificate program at th e University of Washington that focused on the use of R programming for financial modeling. He is currently a full-time MBA student and enjoys hiking and home beer brewing. Alex Jorion, Co-Risk Manager

Alex graduated from the University of California, Los Angeles in 2008 with a B.S. in Mechanical Engineering, where he also earned a CertificateAlex Jorion, in Finance Co-Risk with aManager concentration in Investment Management and Analysis. Alex currently works as an analyst at Angeles InvestmentAlex graduated Advisors, from the an institutionalUniversity of investment California, Losconsulting Angeles comp in 2008any withwith $40a B.S. billion in Mechanical of assets underEngineering, advisement. where In he his also role, earned he is a responsibleCertificate in for Finance portfolio with analysis a concentration and client in servicing Investment for over Management $7 billion inand assets Analysis. from Alex multiple currently endowments, works as foundations,an analyst at and Angeles pension plans.Investment Prior toAdvisors, working an at institutional Angeles, Alex investment worked for consulting Southern comp Californiaany with Edison $40 as billion a nuclear of assets power under plant advisement. engineer, where In his he role, was he a is first - lineresponsible responder for to portfolio emergency analysis operational and client events. servicing While for at over the $7UCLA billion Anderson in assets School from multipleof Management, endowments, Alex isfoundations, focused on andleveraging pension his quantitativeplans. Prior to skills working to advance at Angeles, his car Alexeer workedin investment for Southern management. California Alex Edison is a CFA as a Levelnuclear II Candidate. power plant engineer, where he was a first- line responder to emergency operational events. While at the UCLA Anderson School of Management, Alex is focused on leveraging his quantitative skills to advance his career in investment management. Alex is a CFA Level II Candidate. James Lee, Internal Representative James Lee graduated from the University of Pennsylvania in 2002 with a BA in Economics. He is currently managing his family's Jameswholesale Lee, clothing Internal business Representative which is based in Los Angeles. Prior to this, he had worked as an Equity Research Associate at Dahlman JamesRose & Lee Company graduated LLC fromin New the York University covering of Pennsylvaniathe agricultural in chemicals2002 with and a BA chemicals in Economics. industries He is andcurrently had worked managing as an his Equity family's Resea rch wholesaleAssociate atclothing Banc of business America which Securities is based LLC in covering Los Angeles. the shipping Prior to andthis, coal he had-mining worked industries. as an Equity Prior toResearch his four Associate-year tenure at Dahlmanon Wall RoseStreet, & JamesCompany had LLCworked in New at Hanjin York covering Shipping the Co., agricultural Ltd as a global chemicals management and chemicals trainee industrieswhere he hadand thehad opportunity worked as an to Equitywork in Resea variousrch overseasAssociate locatio at Bancns of such Ame asrica Hamburg, Securities London LLC covering and Seoul. the James shipping is currently and coal an-mining MBA industries.student at Priorthe UCLA to his Anderson four-year Schooltenure ofon Wall ManagementStreet, James hadwith worked a specialization at Hanjin in Shipping Finance Co.,and LtdEntrepreneurship. as a global management trainee where he had the opportunity to work in various overseas locations such as Hamburg, London and Seoul. James is currently an MBA student at the UCLA Anderson School of Management with a specialization in Finance and Entrepreneurship. Vinod Radhakrishnan, VP of Marketing

Vinod currently works at Directv in the Business analytics group focused primarily on building advanced predictive models to optimize marketingVinod Radhakrishnan, strategy and to increase VP of revenue Marketing and profitability. He joined the firm in 2008 as an information technology analyst and was Vinodinstrumental currently in buildingworks at the Directv custo inmer the risk Busine modelsss analytics for the company. group focused He graduated primarily from on building UT Dallas advanced with a master'spredictive in models electrical to optimize marketingengineering. strategy Vinod isand currently to increase pursuing revenue his MBAand profitability. at the UCLA He Anderson joined the School firm in of 2008 Management as an information and is working technology on advancing analyst andhis ca wasreer instrumentalin investment in managment. building the He custo is amer CFA risk level models III candidate. for the company. He graduated from UT Dallas with a master's in electrical engineering. Vinod is currently pursuing his MBA at the UCLA Anderson School of Management and is working on advancing his career in investment managment. He is a CFA level III candidate.

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Alex Revy, External Representative Alex is an operations specialist at DoubleLine Capital LP, where he specializes in daily pricing of structured fixed income instruments such as MBS, ABS, CMBS, CLOs, and CDOs as well as corporate, government, and emerging markets debt for the firm's mutual funds. Prior to working at DoubleLine, Alex worked at SSI Investment Management in Beverly Hills, an investment management firm with long- short equity and convertible strategies, supporting the convertible trading desk. Alex's other duties at SSI included researching corporate actions, on-boarding new clients, updating the security master files, monitoring account restrictions, and pricing the convertible portfolios. Alex graduated from UC Santa Barbara in 2006 with a BA in Religious Studies and minor in Sports Management. He earned his CFA charter in 2012. As the strategy lead for the Tactical Asset Allocation team, Alex is pursuing his interest in utilizing ETFs to construct diversified portfolios that provide competitive risk-adjusted returns while minimizing costs.

Debika Seth Debika graduated from UCLA with a BA in Economics. Soon after, she joined RNC Genter Capital management where she was an Assistant Portfolio Manager in the municipal fixed income department. Currently she is an Investment Manager at Wells Fargo where she manages various trust, private foundation, and individual accounts across multiple asset classes. In addition to being the Investment Finance Association FEMBA 2015 Director, she is also involved in the local CFA Society where she has been coordinator for various courses and continues to maintain the chapter's social media. Ms. Seth holds the Chartered Financial Analyst (CFA) designation.

Jason Stokes, Co-Guest Speaker Coordinator For the past three years, Jason Stokes has worked at Wells Fargo as a Risk Consultant on the Fraud/Risk Analytics team. As a Risk Consultant, he developed and optimized fraud/risk strategies that have saved the bank approximately $4.1 million in avoidance dollars and approximately $750,000 in charge-off losses. Prior to Wells Fargo, he worked as a MIS Business Analyst at JPMorgan on the corporate reporting team and prior to JPMorgan, he worked at Washington Mutual as a Business Analyst on the eCommerce Analytics team. At UCLA Anderson, Jason is currently pursuing his MBA and plans to focus on finance with an interest in asset portfolio management and asset securitization. He passed the CFA Level I in 2009 and plans to be a CFA Level II candidate in the near future. At the 2013 National Investment Banking Conference, NIBC, in Vancouver, Canada, he assisted his research team to win the MDA sales and trading simulation out of five teams. He received his B.Mus. from the University of Toronto and completed course work in economics, political science and marketing at McGill University.

Tommy Taw Tommy Taw currently serves as a finance manager at Bank of America Home Loans. He has over 7 years experience in financial modeling and analysis. At Bank of America, Tommy is responsible for managing a team to develop financial models used to forecasts the bank's credit exposure to possible economic conditions. Prior to joining Bank of America, he served as a consultant at Countrywide Financial and managed a team responsible for combining Bank of America and Countrywide's offshore operations in India and Costa Rica. Tommy graduated from UC Irvine in 2004 with a bachelor's degree in Economics and currently pursuing his CFA designation. Tommy is a member of the FEMBA class of 2014 and is focusing his studies in finance to transition to the asset management or investment banking industry.

Kevin Zhang, VP of Recruiting Kevin Zhang is a Research Analyst intern at Toma Capital Management, a Private Equity group, where he works on deal sourcing, industry/company research, and broker relationship management. Previously, Kevin worked as a manager at Walgreen's Co. focusing on store operations. Kevin graduated from U.C. Riverside in 2008 with a bachelor's degree in Business Administration with concentrations in marketing and general management. Kevin is currently a student in the UCLA Anderson FEMBA program and is pursuing a concentration in finance.

66 ASAM CLASS OF 2014 ANNUAL REPORT

ASAM CLASS OF 2014 ANNUAL REPORT

ANDERON STUDENT ASSET MANAGEMENT