DOCSLIB.ORG

Efficient Markets and the Sharpe Ratio

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Efficient Markets and the Sharpe Ratio Efficient Markets and the Sharpe Ratio Raleigh Deering John Granahan Professor Doremus Econ 464 Fall 2019 Deering & Granahan 2 1 Introduction: Investors are often faced with a choice: they can invest in the market benchmark, or they can select different investment vehicles or strategies. Investing in the market guarantees a relatively stable return over a long investing horizon, but investing with other strategies could open the possibility to outlier returns. These returns may be substantially higher or lower than the market return, and the claims about the upside potential may be an attractive alternative to the market. Over time, these underperforming investment vehicles could dramatically reduce the future value of an investor’s balance if they cannot outperform the market on a consistent basis. Furthermore, exchanges, brokers, and hedge funds directly benefit from people believing that they can beat the market through collecting fees. Therefore, our research question seeks to address the issue of whether a given asset class or option strategy can outperform the market on a risk-adjusted basis. In essence, this research question seeks to test the efficient market hypothesis, which posits that all stocks and options accurately reflect all information, making it impossible to outperform the market or achieve alpha. Alpha is defined as a financial performance metric that equates to a portfolio’s excess return over a market benchmark. If an asset class has a statistically significant alpha, investors could benefit by purchasing those funds. To test the efficient market hypothesis, we sought to test whether any popular asset class or option strategy has an alpha that is statistically significant from zero. Previous literature addresses the efficient market hypothesis, but no literature exists with respect to portfolio performance over the past 15 years for stocks, bonds, and option strategies. For example, one of the most commonly cited research papers on option strategies does not include data from the 2008 Financial Crisis and their paper concluded that option strategies can Deering & Granahan 3 outperform the market on a risk-adjusted basis. Therefore, our research intends to supplement existing literature with a 15 year time frame between 2004 to 2018 for equity, fixed income, commodity, and option returns. Although there are no public policy implications pertaining to this specific research question, there are still ethical concerns regarding active fund managers. If investors are aware of the data surrounding the efficient market hypothesis, they may be less willing to invest in various ETFs instead of the S&P 500. Over the past 15 years, throughout periods of bull and bear markets, our research indicates that asset classes have been unable to outperform the market on a risk-adjusted basis. The remainder of the paper will be structured between a review of existing academic literature, followed by a description of our methodology, relevant results from our data, and concluding remarks. 2 Full Literature Review: The main goal of financial economics is to be able to provide investors with a framework on how to make investment decisions. Markowitz (1952) founded what we now refer to as Modern Portfolio Theory (MPT) in his paper titled, “Portfolio Selection.” His theory stated that rational risk-averse investors will create portfolios that maximize expected return based on the level of market risk. Thus, in order to receive a higher rate of return, an investor must be exposed to more risk. Additionally, Markowitz’s theory concluded a portfolio of assets that has a higher rate of return for a given level of risk is more efficient or optimal. Several years later, Sharpe (1966) added on to the work of Markowitz and developed what is referred to as the Sharpe ratio. The Sharpe ratio can be calculated by taking the return of a given portfolio and subtracting the risk-free interest rate, then dividing that outcome by the standard deviation of the portfolio. Deering & Granahan 4 Annualized Sharpe ratios are standard for reporting purposes, and since references within our literature review report sharpe ratios on a yearly basis, we maintained consistency by analyzing the ratios along the same horizon. Therefore, maximizing the Sharpe ratio of a given portfolio will lead to more desirable outcomes for the investor. Since its introduction, the Sharpe ratio has been used as a benchmark to compare different asset returns along with the performance of active investment strategies. Additionally, Sharpe (1964) helped develop the Capital Asset Pricing Model, which does not allow for risk-adjusted returns over the market portfolio. In financial literature, the market portfolio has been largely defined as the S&P 500 because it captures the return of the top 500 U.S. publicly traded companies by market capitalization. Thus, the financial literature began to conclude that, under the assumptions of the model, rational investors will not be able to predictively beat the risk-adjusted returns of the S&P 500. Starting in the early 1970’s, the financial literature started to test the findings of Sharpe (1964) and Markowitz (1952) to see if the majority of actively managed funds did underperform, merely holding the market portfolio. For example, Frino and Gallagher (2001) took a sample of 343 active mutual funds over an 8-year period and found that the overwhelming majority were not able to outperform the S&P 500 after fees. At the same time, there has been an ongoing concern about other asset classes possibly being able to outperform the S&P 500 after adjusting for risk. For instance, Merk and Osborne (2012) analyzed historical data from 2002 to 2012 and found that during that period gold had a Sharpe ratio of 0.85 compared to 0.30 of the S&P 500. Also, Hodges, Taylor, and Yoder (1997) found that the investment horizon changes the optimal portfolio, which many prior research papers failed to take into consideration. Given this point, Deering & Granahan 5 their data concluded that when looking at longer time horizons, long-term corporate bonds had higher Sharpe ratios compared to common stocks. This is contrary to the prior conclusion in financial literature that the optimal portfolio is the S&P 500. Since the creation of liquid financial exchanges, there have been academics that assert it is possible to consistently achieve risk-adjusted returns through “day trading” in financial markets. More specifically, these academics argue that there are behavioral biases reflected in the markets that rational investors can exploit. Although this notion usually runs contrary to the conclusions reached by the majority of academia in financial economics, it is important to stress ​ ​ this point in order to move forward in our discussion. We will do this by showing what has been concluded from the past literature pertaining to efficient markets and rule out the possibility of achieving risk-adjusted returns through day trading. Most notably, Barber et al. (2011) took the transaction history of 360,000 individuals who participated in day trading from 1992 to 2006 to determine whether retail investors could outperform a buy-and-hold strategy in the S&P 500. Their research found that only 1,000 out of the 360,000 day traders were able to achieve a risk-adjusted return for a given year, which was not statistically significant. Furthermore, the top 500 day traders were only able to achieve a risk adjusted return of 5% per year after fees and the day traders who were ranked below 10,000 lost on average 15% per year. These results are consistent with the vast majority of financial literature that have concluded that investors will be better off not attempting to actively manage their money through day trading while trying to achieve a non-existent risk-adjusted return. In recent years, there has been an expanding literature regarding the use of options as a means to increase the Sharpe ratio of the simple buy-and-hold strategy of the S&P 500. Guasoni, Deering & Granahan 6 Huberman, and Wang (2011) concluded that, even if there is no ability of the investor to predict future returns, holding options on indices will likely result in a higher risk-adjusted return. A strategy known as a covered call has been shown to be one straightforward way for investors to achieve a volatility risk premium, which would result in a higher Sharpe ratio. Put simply, a covered call is where you own shares in a stock and make an agreement with another party to sell those shares at a future date at a certain price. In return for making this agreement, you collect a premium today, which effectively reduces the cost basis for the shares purchased. Israelov and Nielsen (2015) backtested a variety of different covered call strategies on the S&P 500 and found that all of them outperformed buying the S&P 500 outright on a Sharpe ratio basis. A traditional notion in finance surrounds the standard normal distribution of returns, where annualized returns follow an approximate frequency “bell curve.” However, recent empirical studies reject the traditional notion of a symmetrical return distribution, instead favoring the "fat-tailed” distribution or kurtosis, meaning that large losses and gains are too frequent to fit within a standard normal model. For instance, Ivanovski, Stojanovski, and Narasanov (2015) looked at stock market data from 2001 to 2011 and found that asset prices tend to often exhibit skewness and can be characterized at leptokurtic. This means that asset price movements tend to have a slight bias towards the upside and have a higher frequency concentrated around the mean. As a result, rational investors should account for the performance of an asset over a time horizon that includes skewness and kurtosis during both peaks and troughs of the economic cycles.
Load more

Recommended publications
  • Fact Sheet 2021
    Q2 2021 GCI Select Equity TM Globescan Capital was Returns (Average Annual) Morningstar Rating (as of 3/31/21) © 2021 Morningstar founded on the principle Return Return that investing in GCI Select S&P +/- Percentile Quartile Overall Funds in high-quality companies at Equity 500TR Rank Rank Rating Category attractive prices is the best strategy to achieve long-run Year to Date 18.12 15.25 2.87 Q1 2021 Top 30% 2nd 582 risk-adjusted performance. 1-year 43.88 40.79 3.08 1 year Top 19% 1st 582 As such, our portfolio is 3-year 22.94 18.67 4.27 3 year Top 3% 1st 582 concentrated and focused solely on the long-term, Since Inception 20.94 17.82 3.12 moat-protected future free (01/01/17) cash flows of the companies we invest in. TOP 10 HOLDINGS PORTFOLIO CHARACTERISTICS Morningstar Performance MPT (6/30/2021) (6/30/2021) © 2021 Morningstar Core Principles Facebook Inc A 6.76% Number of Holdings 22 Return 3 yr 19.49 Microsoft Corp 6.10% Total Net Assets $44.22M Standard Deviation 3 yr 18.57 American Tower Corp 5.68% Total Firm Assets $119.32M Alpha 3 yr 3.63 EV/EBITDA (ex fincls/reits) 17.06x Upside Capture 3yr 105.46 Crown Castle International Corp 5.56% P/E FY1 (ex fincls/reits) 29.0x Downside Capture 3 yr 91.53 Charles Schwab Corp 5.53% Invest in businesses, EPS Growth (ex fincls/reits) 25.4% Sharpe Ratio 3 yr 1.04 don't trade stocks United Parcel Service Inc Class B 5.44% ROIC (ex fincls/reits) 14.1% Air Products & Chemicals Inc 5.34% Standard Deviation (3-year) 18.9% Booking Holding Inc 5.04% % of assets in top 5 holdings 29.6% Mastercard Inc A 4.74% % of assets in top 10 holdings 54.7% First American Financial Corp 4.50% Dividend Yield 0.70% Think long term, don't try to time markets Performance vs S&P 500 (Average Annual Returns) Be concentrated, 43.88 GCI Select Equity don't overdiversify 40.79 S&P 500 TR 22.94 20.94 18.12 18.67 17.82 15.25 Use the market, don't rely on it YTD 1 YR 3 YR Inception (01/01/2017) Disclosures Globescan Capital Inc., d/b/a GCI-Investors, is an investment advisor registered with the SEC.
    [Show full text]
  • Lapis Global Top 50 Dividend Yield Index Ratios
    Lapis Global Top 50 Dividend Yield Index Ratios MARKET RATIOS 2012 2013 2014 2015 2016 2017 2018 2019 2020 P/E Lapis Global Top 50 DY Index 14,45 16,07 16,71 17,83 21,06 22,51 14,81 16,96 19,08 MSCI ACWI Index (Benchmark) 15,42 16,78 17,22 19,45 20,91 20,48 14,98 19,75 31,97 P/E Estimated Lapis Global Top 50 DY Index 12,75 15,01 16,34 16,29 16,50 17,48 13,18 14,88 14,72 MSCI ACWI Index (Benchmark) 12,19 14,20 14,94 15,16 15,62 16,23 13,01 16,33 19,85 P/B Lapis Global Top 50 DY Index 2,52 2,85 2,76 2,52 2,59 2,92 2,28 2,74 2,43 MSCI ACWI Index (Benchmark) 1,74 2,02 2,08 2,05 2,06 2,35 2,02 2,43 2,80 P/S Lapis Global Top 50 DY Index 1,49 1,70 1,72 1,65 1,71 1,93 1,44 1,65 1,60 MSCI ACWI Index (Benchmark) 1,08 1,31 1,35 1,43 1,49 1,71 1,41 1,72 2,14 EV/EBITDA Lapis Global Top 50 DY Index 9,52 10,45 10,77 11,19 13,07 13,01 9,92 11,82 12,83 MSCI ACWI Index (Benchmark) 8,93 9,80 10,10 11,18 11,84 11,80 9,99 12,22 16,24 FINANCIAL RATIOS 2012 2013 2014 2015 2016 2017 2018 2019 2020 Debt/Equity Lapis Global Top 50 DY Index 89,71 93,46 91,08 95,51 96,68 100,66 97,56 112,24 127,34 MSCI ACWI Index (Benchmark) 155,55 137,23 133,62 131,08 134,68 130,33 125,65 129,79 140,13 PERFORMANCE MEASURES 2012 2013 2014 2015 2016 2017 2018 2019 2020 Sharpe Ratio Lapis Global Top 50 DY Index 1,48 2,26 1,05 -0,11 0,84 3,49 -1,19 2,35 -0,15 MSCI ACWI Index (Benchmark) 1,23 2,22 0,53 -0,15 0,62 3,78 -0,93 2,27 0,56 Jensen Alpha Lapis Global Top 50 DY Index 3,2 % 2,2 % 4,3 % 0,3 % 2,9 % 2,3 % -3,9 % 2,4 % -18,6 % Information Ratio Lapis Global Top 50 DY Index -0,24
    [Show full text]
  • Arbitrage Pricing Theory: Theory and Applications to Financial Data Analysis Basic Investment Equation
    Risk and Portfolio Management Spring 2010 Arbitrage Pricing Theory: Theory and Applications To Financial Data Analysis Basic investment equation = Et equity in a trading account at time t (liquidation value) = + Δ Rit return on stock i from time t to time t t (includes dividend income) = Qit dollars invested in stock i at time t r = interest rate N N = + Δ + − ⎛ ⎞ Δ ()+ Δ Et+Δt Et Et r t ∑Qit Rit ⎜∑Qit ⎟r t before rebalancing, at time t t i=1 ⎝ i=1 ⎠ N N N = + Δ + − ⎛ ⎞ Δ + ε ()+ Δ Et+Δt Et Et r t ∑Qit Rit ⎜∑Qit ⎟r t ∑| Qi(t+Δt) - Qit | after rebalancing, at time t t i=1 ⎝ i=1 ⎠ i=1 ε = transaction cost (as percentage of stock price) Leverage N N = + Δ + − ⎛ ⎞ Δ Et+Δt Et Et r t ∑Qit Rit ⎜∑Qit ⎟r t i=1 ⎝ i=1 ⎠ N ∑ Qit Ratio of (gross) investments i=1 Leverage = to equity Et ≥ Qit 0 ``Long - only position'' N ≥ = = Qit 0, ∑Qit Et Leverage 1, long only position i=1 Reg - T : Leverage ≤ 2 ()margin accounts for retail investors Day traders : Leverage ≤ 4 Professionals & institutions : Risk - based leverage Portfolio Theory Introduce dimensionless quantities and view returns as random variables Q N θ = i Leverage = θ Dimensionless ``portfolio i ∑ i weights’’ Ei i=1 ΔΠ E − E − E rΔt ΔE = t+Δt t t = − rΔt Π Et E ~ All investments financed = − Δ Ri Ri r t (at known IR) ΔΠ N ~ = θ Ri Π ∑ i i=1 ΔΠ N ~ ΔΠ N ~ ~ N ⎛ ⎞ ⎛ ⎞ 2 ⎛ ⎞ ⎛ ⎞ E = θ E Ri ; σ = θ θ Cov Ri , R j = θ θ σ σ ρ ⎜ Π ⎟ ∑ i ⎜ ⎟ ⎜ Π ⎟ ∑ i j ⎜ ⎟ ∑ i j i j ij ⎝ ⎠ i=1 ⎝ ⎠ ⎝ ⎠ ij=1 ⎝ ⎠ ij=1 Sharpe Ratio ⎛ ΔΠ ⎞ N ⎛ ~ ⎞ E θ E R ⎜ Π ⎟ ∑ i ⎜ i ⎟ s = s()θ ,...,θ = ⎝ ⎠ = i=1 ⎝ ⎠ 1 N ⎛ ΔΠ ⎞ N σ ⎜ ⎟ θ θ σ σ ρ Π ∑ i j i j ij ⎝ ⎠ i=1 Sharpe ratio is homogeneous of degree zero in the portfolio weights.
    [Show full text]
  • Sharpe Ratio
    StatFACTS Sharpe Ratio StatMAP CAPITAL The most famous return-versus- voLATILITY BENCHMARK TAIL PRESERVATION risk measurement, the Sharpe ratio, RN TU E represents the added value over the R risk-free rate per unit of volatility risk. K S I R FF O - E SHARPE AD R RATIO T How Is it Useful? What Do the Graphs Show Me? The Sharpe ratio simplifies the options facing the The graphs below illustrate the two halves of the investor by separating investments into one of two Sharpe ratio. The upper graph shows the numerator, the choices, the risk-free rate or anything else. Thus, the excess return over the risk-free rate. The blue line is the Sharpe ratio allows investors to compare very different investment. The red line is the risk-free rate on a rolling, investments by the same criteria. Anything that isn’t three-year basis. More often than not, the investment’s the risk-free investment can be compared against any return exceeds that of the risk-free rate, leading to a other investment. The Sharpe ratio allows for apples-to- positive numerator. oranges comparisons. The lower graph shows the risk metric used in the denominator, standard deviation. Standard deviation What Is a Good Number? measures how volatile an investment’s returns have been. Sharpe ratios should be high, with the larger the number, the better. This would imply significant outperformance versus the risk-free rate and/or a low standard deviation. Rolling Three Year Return However, there is no set-in-stone breakpoint above, 40% 30% which is good, and below, which is bad.
    [Show full text]
  • A Sharper Ratio: a General Measure for Correctly Ranking Non-Normal Investment Risks
    A Sharper Ratio: A General Measure for Correctly Ranking Non-Normal Investment Risks † Kent Smetters ∗ Xingtan Zhang This Version: February 3, 2014 Abstract While the Sharpe ratio is still the dominant measure for ranking risky investments, much effort has been made over the past three decades to find more robust measures that accommodate non- Normal risks (e.g., “fat tails”). But these measures have failed to map to the actual investor problem except under strong restrictions; numerous ad-hoc measures have arisen to fill the void. We derive a generalized ranking measure that correctly ranks risks relative to the original investor problem for a broad utility-and-probability space. Like the Sharpe ratio, the generalized measure maintains wealth separation for the broad HARA utility class. The generalized measure can also correctly rank risks following different probability distributions, making it a foundation for multi-asset class optimization. This paper also explores the theoretical foundations of risk ranking, including proving a key impossibility theorem: any ranking measure that is valid for non-Normal distributions cannot generically be free from investor preferences. Finally, we show that approximation measures, which have sometimes been used in the past, fail to closely approximate the generalized ratio, even if those approximations are extended to an infinite number of higher moments. Keywords: Sharpe Ratio, portfolio ranking, infinitely divisible distributions, generalized rank- ing measure, Maclaurin expansions JEL Code: G11 ∗Kent Smetters: Professor, The Wharton School at The University of Pennsylvania, Faculty Research Associate at the NBER, and affiliated faculty member of the Penn Graduate Group in Applied Mathematics and Computational Science.
    [Show full text]
  • In-Sample and Out-Of-Sample Sharpe Ratios of Multi-Factor Asset Pricing Models
    In-sample and Out-of-sample Sharpe Ratios of Multi-factor Asset Pricing Models RAYMOND KAN, XIAOLU WANG, and XINGHUA ZHENG∗ This version: November 2020 ∗Kan is from the University of Toronto, Wang is from Iowa State University, and Zheng is from Hong Kong University of Science and Technology. We thank Svetlana Bryzgalova, Peter Christoffersen, Victor DeMiguel, Andrew Detzel, Junbo Wang, Guofu Zhou, seminar participants at Chinese University of Hong Kong, London Business School, Louisiana State University, Uni- versity of Toronto, and conference participants at 2019 CFIRM Conference for helpful comments. Corresponding author: Raymond Kan, Joseph L. Rotman School of Management, University of Toronto, 105 St. George Street, Toronto, Ontario, Canada M5S 3E6; Tel: (416) 978-4291; Fax: (416) 978-5433; Email: [email protected]. In-sample and Out-of-sample Sharpe Ratios of Multi-factor Asset Pricing Models Abstract For many multi-factor asset pricing models proposed in the recent literature, their implied tangency portfolios have substantially higher sample Sharpe ratios than that of the value- weighted market portfolio. In contrast, such high sample Sharpe ratio is rarely delivered by professional fund managers. This makes it difficult for us to justify using these asset pricing models for performance evaluation. In this paper, we explore if estimation risk can explain why the high sample Sharpe ratios of asset pricing models are difficult to realize in reality. In particular, we provide finite sample and asymptotic analyses of the joint distribution of in-sample and out-of-sample Sharpe ratios of a multi-factor asset pricing model. For an investor who does not know the mean and covariance matrix of the factors in a model, the out-of-sample Sharpe ratio of an asset pricing model is substantially worse than its in-sample Sharpe ratio.
    [Show full text]
  • Picking the Right Risk-Adjusted Performance Metric
    WORKING PAPER: Picking the Right Risk-Adjusted Performance Metric HIGH LEVEL ANALYSIS QUANTIK.org Investors often rely on Risk-adjusted performance measures such as the Sharpe ratio to choose appropriate investments and understand past performances. The purpose of this paper is getting through a selection of indicators (i.e. Calmar ratio, Sortino ratio, Omega ratio, etc.) while stressing out the main weaknesses and strengths of those measures. 1. Introduction ............................................................................................................................................. 1 2. The Volatility-Based Metrics ..................................................................................................................... 2 2.1. Absolute-Risk Adjusted Metrics .................................................................................................................. 2 2.1.1. The Sharpe Ratio ............................................................................................................................................................. 2 2.1. Relative-Risk Adjusted Metrics ................................................................................................................... 2 2.1.1. The Modigliani-Modigliani Measure (“M2”) ........................................................................................................ 2 2.1.2. The Treynor Ratio .........................................................................................................................................................
    [Show full text]
  • Investment Performance Performance Versus Benchmark: Return and Sharpe Ratio
    Release date 07-31-2018 Page 1 of 20 Investment Performance Performance versus Benchmark: Return and Sharpe Ratio 5-Yr Summary Statistics as of 07-31-2018 Name Total Rtn Sharpe Ratio TearLab Corp(TEAR) -75.30 -2.80 AmerisourceBergen Corp(ABC) 8.62 -2.80 S&P 500 TR USD 13.12 1.27 Definitions Return Sharpe Ratio The gain or loss of a security in a particular period. The return consists of the The Sharpe Ratio is calculated using standard deviation and excess return to income and the capital gains relative to an investment. It is usually quoted as a determine reward per unit of risk. The higher the SharpeRatio, the better the percentage. portfolio’s historical risk-adjusted performance. Performance Disclosure The performance data quoted represents past performances and does not guarantee future results. The investment return and principal value of an investment will fluctuate; thus an investor's shares, when sold or redeemed, may be worth more or less than their original cost. Current performance may be lower or higher than return data quoted herein. Please see the Disclosure Statements for Standardized Performance. ©2018 Morningstar. All Rights Reserved. Unless otherwise provided in a separate agreement, you may use this report only in the country in which its original distributor is based. The information, data, analyses and ® opinions contained herein (1) include the confidential and proprietary information of Morningstar, (2) may include, or be derived from, account information provided by your financial advisor which cannot be verified by Morningstar, (3) may not be copied or redistributed, (4) do not constitute investment advice offered by Morningstar, (5) are provided solely for informational purposes and therefore are not an offer to buy or sell a security, ß and (6) are not warranted to be correct, complete or accurate.
    [Show full text]
  • Chapter 4 Sharpe Ratio, CAPM, Jensen's Alpha, Treynor Measure
    Excerpt from Prof. C. Droussiotis Text Book: An Analytical Approach to Investments, Finance and Credit Chapter 4 Sharpe Ratio, CAPM, Jensen’s Alpha, Treynor Measure & M Squared This chapter will continue to emphasize the risk and return relationship. In the previous chapters, the risk and return characteristics of a given portfolio were measured at first versus other asset classes and then second, measured to market benchmarks. This chapter will re-emphasize these comparisons by introducing other ways to compare via performance measurements ratios such the Share Ratio, Jensen’s Alpha, M Squared, Treynor Measure and other ratios that are used extensively on wall street. Learning Objectives After reading this chapter, students will be able to: Calculate various methods for evaluating investment performance Determine which performance ratios measure is appropriate in a variety of investment situations Apply various analytical tools to set up portfolio strategy and measure expectation. Understand to differentiate the between the dependent and independent variables in a linear regression to set return expectation Determine how to allocate various assets classes within the portfolio to achieve portfolio optimization. [Insert boxed text here AUTHOR’S NOTES: In the spring of 2006, just 2 years before the worse financial crisis the U.S. has ever faced since the 1930’s, I visited few European countries to promote a new investment opportunity for these managers who only invested in stocks, bonds and real estate. The new investment opportunity, already established in the U.S., was to invest equity in various U.S. Collateral Loan Obligations (CLO). The most difficult task for me is to convince these managers to accept an average 10-12% return when their portfolio consists of stocks, bonds and real estate holdings enjoyed returns in excess of 30% per year for the last 3-4 years.
    [Show full text]
  • Fixed-Income Insights
    Fixed-Income Insights Reassessing Risk—and Opportunity—in Short-Duration Fixed Income Investors seeking attractive income in a yield-starved world—with lower risk—may wish to take a fresh look at short-maturity credit. December 27, 2016 by Timothy Paulson, Investment Strategist, Fixed Income IN BRIEF n The low investment yields available over the past few years have created difficult tradeoffs for investors. But are there opportunities in established asset categories that might improve a portfolio’s return profile without increasing overall risk? n One option to consider is short-maturity credit, which historically has featured attractive yields and low price volatility. n In this report, we will examine in detail five key attributes of short credit, and their influence on factors such as price volatility, return, credit ratings, and credit risk, across key short-maturity categories such as corporate debt, asset backed securities, and commercial mortgage-backed securities. n The key takeaway: At a time when many investors are struggling to be adequately compensated for risks, short term credit may provide some real solutions. Investors have been for some time now grappling This paper will focus on five key attributes of with historically low expected returns across all asset short-maturity paper and their implications for classes. As central bank actions have left the global investors: financial system awash in liquidity, yield-starved inves- tors have squeezed value out of many traditional asset classes. While price appreciation of many assets has 1) The “pull to par” mitigates price volatility for short-term bonds. helped in recent years, investors have been left with a difficult trade-off: accepting lower returns or changing 2) Credit spreads over-compensate investors for their traditional asset mix.
    [Show full text]
  • Seeing Beyond the Complexity: an Introduction to Collateralized Loan Obligations
    September 2019 Seeing Beyond the Complexity: An Introduction to Collateralized Loan Obligations Collateralized loan obligations are robust, opportunity-rich debt instruments that offer the potential for above- average returns versus other fixed income strategies. Here, we explore how these instruments work, as well as the benefits and risks investors should consider when allocating to this asset class. Collateralized loan obligations (CLOs) are robust, opportunity-rich debt instruments Authors: that have been around for about 30 years. And while they’re well established, they’re also complex enough that even sophisticated investors may hesitate to dig into the Steven Oh, CFA details – and end up avoiding them instead. Global Head of Credit CLOs have been gaining wider prominence in markets in recent years, and it’s no and Fixed Income surprise why. They offer a compelling combination of above-average yield and potential appreciation. But for many investors, the basics of how they work, the benefits they can provide, and the risks they pose are wrapped in complication. And they’re also often Laila Kollmorgen, CFA misconstrued by the financial media and some market participants. CLO Tranche Portfolio In spite of this, we believe CLOs are attractive investments and well worth the time and Manager effort required to understand them. What is a CLO? Put simply, a CLO is a portfolio of leveraged loans that is securitized and managed as a fund. Each CLO is structured as a series of tranches that are interest-paying bonds, along with a small portion of equity. CLOs originated in the late 1980s, similar to other types of securitizations, as a way for banks to package leveraged loans together to provide investors with an investment vehicle with varied degrees of risk and return to best suit their investment objectives.
    [Show full text]
  • Securitization & Hedge Funds: Creating a More Efficient
    SECURITIZATION & HEDGE FUNDS: COLLATERALIZED FUND OBLIGATIONS SECURITIZATION & HEDGE FUNDS: CREATING A MORE EFFICIENT MARKET BY CLARK CHENG, CFA Intangis Funds AUGUST 6, 2002 INTANGIS PAGE 1 SECURITIZATION & HEDGE FUNDS: COLLATERALIZED FUND OBLIGATIONS TABLE OF CONTENTS INTRODUCTION........................................................................................................................................ 3 PROBLEM.................................................................................................................................................... 4 SOLUTION................................................................................................................................................... 5 SECURITIZATION..................................................................................................................................... 5 CASH-FLOW TRANSACTIONS............................................................................................................... 6 MARKET VALUE TRANSACTIONS.......................................................................................................8 ARBITRAGE................................................................................................................................................ 8 FINANCIAL ENGINEERING.................................................................................................................... 8 TRANSPARENCY......................................................................................................................................
    [Show full text]