Bear Market Equity-Only Quantitative Portfolio Strategy
Item Type text; Electronic Thesis
Authors McMillin, Michael Shaun
Publisher The University of Arizona.
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BEAR MARKET EQUITY-ONLY QUANTITATIVE PORTFOLIO STRATEGY
By
MICHAEL SHAUN MCMILLIN
______
A Thesis Submitted to The Honors College
In Partial Fulfillment of the Bachelors degree With Honors in
Finance
THE UNIVERSITY OF ARIZONA
M A Y 2 0 1 9
Approved by:
______
Matthew Haertzen, CFA, MBA Eller College of Management, Department of Finance McMillin, 2
Abstract The University of Arizona’s Chicago Quantitative Alliance Investment Challenge team used a bear market strategy to maximize the effectiveness of its all equity portfolio. The portfolio chose this strategy due to the macroeconomic climate at the beginning of the challenge in late October 2018. The escalating tensions of the China and U.S. trade war as well as a global economic slowdown spurred by a decrease in market optimism spurred the group’s decision to create a bearish strategy.
The portfolio optimized itself by focusing a majority of the portfolio’s risk into sector allocation. For the short portion of the portfolio, the group chose historically underperforming sectors in a bear market such as the communications and information technology sectors and historically overpoerfomring sectors such as consumer staples, real estate, and utilities. To further optimize the portfolio, the group used a select group of factors for each portion of the portfolio to maximize the portfolio performance. For further detail, refer to the pages 6 and 8.
In conclusion, the portfolio performed well during the market’s slowdown in December
2018 which culminated in a 5% drop in the market on Christmas eve. However, the market’s strong recovery over the 1st quarter of 2019 has hurt the portfolio’s performance. In future iterations of this strategy, an emphasis on momentum factors such as the comparison of 50-day average price to 360-day average price should be used to minimize the risk of changing market sentiment. McMillin, 3
Bear Market Equity-Only Quantitative Portfolio Strategy
The model used by the University of Arizona’s Chicago Quantitative Alliance (CQA)
Investment Challenge team looked to take advantage of waning market sentiment during the fourth quarter of 2018. A myriad of macroeconomic factors led the team to focus on creating a model that would perform well in a bear market and be able to capitalize on broad market losses during this period by being one of the first to recognize a shift in the market cycle. This paper will describe the competition and its rules, the model’s strategy and construction, and the how the model performed over the course of the CQA Investment Challenge.
Description of the Chicago Quantitative Alliance Investment Challenge The CQA Investment Challenge provides college students from various universities the opportunity to participate in a portfolio challenge that focuses on building a portfolio quantitatively. Groups compete against one another by investing based on quantitative model of their own design which considers any factors the students feel are relevant, to find the best mix of equities that will beat the market. The CQA wants this challenge to simulate, “real life hedge fund experience,” (“CQA Challenge.”). The winning team is decided based upon three factors: absolute return, risk adjusted return, and an evaluation of a strategy presentation video. The greatest emphasis is put on the risk adjusted return of the students’ portfolio. The challenge does have some rules that each participating team must follow throughout the competition to remain eligible for top prize. Each student group must be between 3 to 5 members and are overseen by a professor and CQA member. The portfolio had constraints as well. The stock portfolio must consist of
$1,000,000 in capital and use leverage of 2:1 but no more. Stock selections must come from the stock universe provided to each team by the CQA member assigned to them. The contest runs from McMillin, 4
the end of October 2018 through the end of March 2019. For more information about the contest please refer to the contest rules at www.cqa.org/investment_challenge.
Portfolio Strategy Thesis The UA team’s stock selection strategy was focused around key sectors that would perform either extremely well or poorly in a down market. Our thesis was the market would enter a down period during the competition due to trade tensions between the U.S. and China erupting into a full-scale trade war, higher Fed rates slowing down necessary capital expenditures to spur economic growth, and general insecurity from the market about being in the longest bull market in history. In January, the government shutdown continued leaving millions of government employees without checks and the promise of Donald Trump that the shutdown could last years, a economic downturn seemed eminent (Berman, 2019). The group believed the market would experience a market correction because the P/E ratios in sectors such as information technology and consumer discretionary were at all-time highs and were so overvalued that stock pickers would not support the prices these securities traded at during the bull period in a bear market. This combination of factors mentioned above spurred our decision to create a strategy designed to provide positive returns during a down market.
The sectors the team believed would perform well during a down market were real estate, consumer staples, and utilities. These sectors have traditional performed well during down periods due to their reputation as dividend paying stocks with stable earnings that sit in industries with inelastic demands or whose demands are heightened due to less discretionary income in the economy (Chen, 2019). Stocks from these sectors that best fit our quantitative model comprised the long portion of the portfolio. McMillin, 5
The sectors the team believed would perform poorly during a down market were the information technology, consumer discretionary, and communications sectors. Stocks from these three sectors comprised the short portion of the portfolio. The team believed these sectors held some of the markets’ most overvalued securities and would suffer the greatest drop in equity value during a down market (Goh, 2018). Stocks like Netflix (NFLX) and Paycom (PAYC) whose price to earnings ratios were 134.7 and 77.5 respectively, are representative of the types of stocks we hoped to short during a down market to ensure gains in our portfolio (“PAYC Stock Price,” 2019
& “NFLX Stock price,” 2019). Additionally, these sectors expand, and contract based on which part of the market cycle the economy sits. Since many of the companies that rest in the information technology, consumer discretionary, and communications sectors rely on discretionary income to fuel their businesses, they suffer during periods of economic hardship (Young, 2019).
Security Selection and Portfolio Construction Strategy Once we decided on an overall strategy for the portfolio, stock selection came down to deciding what factors about the companies we wanted to measure and to measure those factors across the selected sectors. The team split which factors to focus on between the long portion and the short portion of the portfolio. The long portion of the portfolio focused on key metrics that demonstrated long term growth and consistent profit over a five-year period. The metrics the team chose to cover were Bloomberg Intelligence five-year default probability, Weighted Average Cost of Capital return on invested capital (WACC ROIC), Five-year average return on assets (ROA), and the stock’s beta. For a quick summary, please refer to the table at the end of the paragraph. Bloomberg
Intelligence five-year default probability is a function that uses share price, price volatility, and total debt in comparison with industry averages and industry peers with similar credit ratings to calculate the firm’s risk of defaulting (Zhang, 2015). For the long section of the portfolio, the McMillin, 6
group focused on companies with a low probability of default in the next five years. The WACC
ROIC factor focuses on companies’ ability to generate return on capital investment above the firm’s weighted average cost of capital and determine whether the firm has established a moat around its business (Collins, 2006). For this portion of the portfolio, the group wanted companies that had a historically high ROIC to solidify the presence of an economic moat. The five-year average ROA factor demonstrates a company’s ability to generate profits from their current assets over the last five years of invested capital (Hargrave, 2019). The group wanted companies to have a high ROA, especially over this past five-year period of substantial growth in the market, to solidify our position that this company could consistently grow regardless of market conditions.
Finally, beta allowed the group to identify which stocks would move with the markets and how substantially they would move when the market did shift (Kenton, “Understanding Beta and How to Calculate It.” 2019). A low beta was extremely important for this portion of the model. Finding companies that were not closely tied to the performance of the market but instead would perform well in down periods of the market.
Long Securities’ Factors Looking for?
Bloomberg Intelligence Five-year Default Low probability Probability
Weighted Average Cost of Capital Return on High return on invested capital Invested Capital (WACC ROIC)
Five-year average return on assets (ROA) High return on assets
Beta Low Beta
McMillin, 7
The short portion of the portfolio focused on metrics that demonstrated a company’s historical reliance on financing to support growth and reduced ability to access necessary amounts of cash.
The metrics the team chose to cover were Total Debt to EBITDA, Price to Earning ratio, the Quick ratio, and Beta. For a quick summary, please refer to the table at the end of the paragraph. The total debt to EBITDA ratio was used by the team to determine how healthy the company’s financial position is and how the company’s ability to pay off its obligations based on current revenues
(“Debt/EBITDA Ratio.” 2018). The team wanted to choose companies whose total debt to
EBITDA ratio was high. The price to earnings (PE) ratio was used to determine which companies stock price were extremely inflated in comparison to their earnings to better establish companies with higher than normal PE ratios particularly compared to their peers in their sector (Hayes,
2019). Again, the team was looking for companies with high PE ratios. Another factor the team used was the quick ratio. The quick ratio focuses on a company’s ability to pay-off short term payables and other obligations if the company brought in no revenue (Kenton, “How the Quick
Ratio Works.” 2019). Here, the team searched for companies with a low quick ratio because our theory projected that the lack of cheap financing available during a down market would make it difficult for companies with small cash reserves to remain afloat as revenues decreased. Finally, the group wanted to select stocks for the short portion of the portfolio that were highly correlated to the market and would move substantially more than market (Kenton, “Understanding Beta and
How to Calculate It.” 2019). The preference of the group was to select stocks with a high beta to follow the market especially during the down periods.
McMillin, 8
Short Securities’ Factors Looking for?
Total Debt to EBITDA ratio High Debt to EBITDA ratio
Price to Earnings (P/E) ratio High P/E
Quick ratio Low ratio between cash and short-term payables
Beta High Beta
Once the specific factors had been established, the team used Bloomberg to pull the appropriate factor data for each section of the portfolio. Using the relevant factor data, the group sorted the data of each factor into quintiles to normalize the data. Each stock’s factors were then assigned a quintile based on where the factor fell and the average of these scores was used as the stock’s score. The closer the stock’s average score was to 1, the more desirable the stock was to our portfolio’s strategy. For the short portion of the portfolio, the first 40 stocks with the lowest average factor score were selected. The group’s thought process behind selecting the first 40 stocks with the lowest average factor score was the realignment of the telecommunications sector to include technology stocks like Netflix (NFLX) and Facebook (FB). The group’s theory was a majority of the stocks selected from the communications sector would have been realigned from the technology sector. For the long portion of the portfolio, 20 stocks were selected from the consumer staples sector, ten from the real estate sector, and ten from the utilities sector. Here, the group’s thought process was to create as diverse portfolio that would be less exposed to the unsystematic risk of picking specific stocks spreading out the risk amongst a variety of different stocks. The stocks from each sector were selected based on the lowest average factor score. Each stock in the McMillin, 9
long and short portions of the portfolio were given an equal position to minimize any capital allocation risk in the portfolio. The group’s portfolio was rebalanced every three weeks by following the same process described above to maintain consistency and ensure our portfolio aligned with our strategy as accurately as possible. The group chose three weeks because the CQA challenge has a 1,000-trade maximum rule and the group wanted to ensure small fluctuations did not dictate a change in the model but instead consistent and sustained change in an equity’s fundamentals and technical would force the model to change instead.
Portfolio Strategy Analysis As the title of this paper suggests, the portfolio’s strategy was focused on performing as well as possible during a market downturn. From the division of the stock universe into defensive and cyclical sectors, to the fundamental factors chosen to create the stock score, steps were taken to maximize performance during a bear market. Per the Axioma reports, provided to the team by the
CQA, our portfolio’s strategy would perform well during the past four significant downturns, particularly the Dot-Com Bubble Burst of 2000 and the Financial Crisis – Lehman Brothers
Collapse of 2008 (Figure 1). This strategy worked well during the first two months of the portfolio challenge, culminating on December 24, 2018, the day before Christmas, when the market hit its most significant low over the past 12 months.
Figure 1. McMillin, 10
From there, the team’s portfolio struggled as the information technology and communications sectors regained their losses and as noted in Figure 2, and the long positions, particularly in the consumer staples sector, began to struggle. In summary, our strategy struggled as the market turned around due to changing sentiment and strong economic reports from early in the year.
Figure 2. McMillin, 11
After reviewing the portfolio’s performance over the past four months, there are some areas the portfolio’s strategy could be improved to minimize our downside when the market is up. The first area that requires change is the P/E factor used in the stock score for the shorts portion of the portfolio. Instead of relying on the past 12 months of P/E data, the factor should focus on earnings guidance released in earnings transcripts to better project how a company’s management expects to perform over the next 12 months. The next area for improvement is to include a momentum factor in the short section of the portfolio to reduce our exposure to a significant and sustained market turnaround. A rule that the group would like to implement in the short portion of the portfolio, is to add a qualification factor to each stock reviewed by the short’s strategy. This qualification would alert the group when the stock price of an equity has crossed its one year to McMillin, 12
date high price and is still above its one year to date average price (Segal, 2019). This will reduce the heavy losses the portfolio took from momentum stocks that fit the short qualifications. The portfolio’s strategy is focused on performing well during a down market, so there is a timing aspect to this portfolio. Since the competition restricts what time a strategy may be implemented, the group’s best alternative is to reduce our exposure to momentum stocks that the portfolio is shorting.
In conclusion, the portfolio maximized its gains during the down periods of the past four months while performing poorly during the growth periods. The portfolio performed as designed, however, market conditions did not perform as poorly as expected and this upswing over quarter one of 2019 has hurt the performance of the portfolio greatly. As highlighted above, the portfolio could be improved if changes are made to how the long portion of the portfolio is established, and the P/E ratios used in the factor scores of the stock in the short portion of the portfolio are changed to be forward focused. These changes to the portfolio can help minimize the effects of a bull market on this portfolio. Overall, this strategy is a specific short-term play that should only be used during periods of high volatility that indicate shifting sentiment on the strength of the market coupled with strong technical factors that point to an imminent downturn. I believe this strategy will become more useful over the next 11 to 24 months following the inversion of the Fed’s treasury yield curve and global economies such as German’s dip into recessions (Nelson, 2019).
McMillin, 13
Statement of Roles & Responsibilities The University of Ariozna’s Chicago Quantitative Alliance Investment Challenge team was comprised of three members; Carter Gerado, Michael McMillin, and Alex Wisthoff. Each team member had unique responsibilities within the group to ensure the smooth function of the team. The responsibilities of each member is listed below.
Carter Gerardo oversaw executing the trades throughout the project every three weeks to ensure the model was kept up to date. Additionally, Carter contributed to the formation of the initial strategy as well as the research behind the strategy.
Michael McMillin oversaw creating the quantitative model that was the basis of the portfolio’s creation as well as model revisions throughout the competition. Also, he contributed to the formation of strategy’s initial formation and subsequent revisions throughout the competition as well as the research behind the strategy.
Alex Wisthoff oversaw a majority of the research behind the strategy that went into the portfolio and the portfolio’s creation. Additionally, he helped Carter execute trades during particularly when there was a lot of portfolio rebalancing. Finally, Alex assisted Michael in model revisions throughout the competition.
McMillin, 14
Works Cited
Berman, Russell. "The Impact of the Government Shutdown Is About to Snowball." The Atlantic. 11 Jan. 2019. Atlantic Media Company. 27 Mar. 2019
"PAYC Stock Price - Paycom Software Inc. Stock Quote (U.S.: NYSE)." MarketWatch. 25 Mar. 2019. Dow Jones & Company. 25 Mar. 2019