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Bear Market Equity-Only Quantitative Portfolio Strategy

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Bear Market Equity-Only Quantitative Portfolio Strategy Bear Market Equity-Only Quantitative Portfolio Strategy Item Type text; Electronic Thesis Authors McMillin, Michael Shaun Publisher The University of Arizona. Rights Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author. Download date 02/10/2021 23:10:14 Item License http://rightsstatements.org/vocab/InC/1.0/ Link to Item http://hdl.handle.net/10150/632857 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
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