Investing Expertise

Is past rolling maximum drawdown, a simple measure of recent downside risk, a useful indicator of future mutual fund performance? In their June 2022 paper entitled “Maximum Drawdown as Predictor of Mutual Fund Performance and Flows”, Timothy Riley and Qing Yan investigate whether style-adjusted maximum drawdown based on daily returns over the last 12 months usefully predicts mutual fund performance. To adjust for fund style differences, they subtract from each individual unadjusted drawdown the average unadjusted drawdown across all funds in the same style during the measurement interval. Their principal performance metric is alpha based on a 4-factor (market, size, book-to-market, momentum) model of stock returns. Using daily net returns for 2,188 actively managed long-only U.S. equity mutual funds that are at least two years old and have at least $20 million in assets during January 1999 through December 2019, they find that: Keep Reading

Do presumably expert early-stage startup investors, whether individuals (Angels) or institutions (Venture Capitalists) invest efficiently? In his June 2022 paper entitled “Predictably Bad Investments: Evidence from Venture Capitalists”, Diag Davenport applies machine learning methods based on information known at the time of investment to evaluate decisions of early-stage investors. He defines early-stage investments as equity deals within two years of incubator completion categorized in Pitchbook as deal types Series A, Series B, Seed Round or Angel (Individual). He define late-stage exit as initial public offering, merger/acquisition or funding categorized in Pitchbook as Series C or later. He uses his first five years of quantitative data and numerical transformations of the qualitative data (text) in training a model with XGBoost to predict future venture success. He then applies the model to the next three years of data to build a portfolio that substitutes conventional investments (such as the S&P 500 Index) for predictably bad ventures. Using venture financials and qualitative information about the CEO from Pitchbook for 16,054 startups accepted into top accelerator programs during 2009 through 2016 (2009-2013 for model training and 2014-2016 for testing), he finds that:

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Do individual investors who chase stocks with high recent returns benefit from momentum or suffer from reversal? In their June 2022 paper entitled “Who Chases Returns? Evidence from the Chinese Stock Market”, Weihua Chen, Shushu Liang and Donghui Shi investigate the characteristics, performance and market impact of retail stock investors who exhibit return-chasing behavior. Each month, they measure:

1. Each retail investor’s return chasing propensity (RCP) as the average of returns during the 12 months prior to purchase across the stocks in the investor’s portfolio. For robustness they also consider past return intervals of one, two, three and six months.
2. Each stock’s return chasing ownership (RCO) by wealth-weighting the RCPs of its retail holders (excluding this stock from holder RCP calculations).

Using monthly stock holdings, trading records and investor demographics, plus associated monthly stock prices, for 18 million Shanghai Stock Exchange retail investors during January 2011 through December 2019, they find that:

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Why do the alphas generated by historical simulations/backtests disappear in live trading, with asset managers and brokers the only winners via fees and commissions. In their February 2022 paper entitled “Where’s the Beef?”, Robert Arnott, Amie Ko and Lillian Wu explore: (1) the ways that seasoned professionals fall prey to the simple blunders of data snooping and performance chasing; and, (2) how the industry could actually meet client expectations. Based on the body of research on investor behavior and fund performance and decades of investment management experience, they conclude that: Keep Reading

How have active equity investment managers performed over the past three decades? In his November 2021 paper entitled “Active Equity Management, 1991-2020”, Gene Hochachka examines whether: (1) active equity managers as a group beat their benchmarks over the last 30 years; and, (2) active equity manager relative performance is persistence. By active equity managers, he means:

• Live and dead U.S. mutual funds tracked by Morningstar Direct and not classified as an index fund or fund-of-funds, segmented into US LargeCap, US MidCap, US SmallCap and Foreign (International) LargeCap.
• Institutional strategies tracked as self-reported by Mercer Global Investment Manager Database and not classified as passive in mid-2021, segmented into US LargeCap, US MidCap, US SmallCap, US Small/MidCap, US AllCap and International LargeCap.

Fund/strategy and benchmark returns are for calendar years, including dividends/distributions, and are gross of all fees and expenses. Some analyses compare net-of-expense fund/strategy and net-of-expense benchmark returns. Using the specified annual returns during 1991 through 2020, he finds that: Keep Reading

Does actual performance support the view that university endowments are exemplary stewards of multi-asset class portfolios? In his November 2021 paper entitled “The Modern Endowment Story: A Ubiquitous U.S. Equity Risk Premium”, Richard Ennis re-examines aggregate allocations and performance of U.S. educational endowments. Specifically, he:

• Estimates effective aggregate endowment asset class allocations over different recent sample periods via multiple regressions of endowment returns versus returns of three indexes: Bloomberg Aggregate U.S. bonds; Russell 3000 stocks; and, currency-hedged MSCI ACWI ex-U.S. stocks.
• Applies these effective allocations to construct benchmark portfolios of these three indexes for the different sample periods.

Using investment data for over 100 U.S. educational endowments with assets over $1 billion during the 13 years ending June 2021, he finds that: Keep Reading

What is the best way to conduct quantitative investment research? In his September 2021 presentation package entitled “Escaping The Sisyphean Trap: How Quants Can Achieve Their Full Potential”, Marcos Lopez de Prado outlines the optimal way to tackle such research. Based on his experience, he concludes that: Keep Reading

Do motives of financial market researchers justify strong skepticism of their findings? In his brief August 2021 paper entitled “Be Skeptical of Asset Management Research”, Campbell Harvey argues that economic incentives undermine belief in findings of both academic and practitioner financial market researchers. Based on his 35 years as an academic, advisor to asset management companies and editor of a top finance journal, he concludes that: Keep Reading

Is the WisdomTree approach to exchange-traded fund (ETF) cost efficiency and performance potential (Modern Alpha) attractive? To investigate, we compare performance statistics of six WisdomTree ETFs, all currently available, to those of “easy substitute” (widely used and very liquid) benchmark ETFs, as follows:

1. WisdomTree U.S. Total Dividend Fund (DTD), with SPDR S&P 500 ETF Trust (SPY) as a benchmark.
2. WisdomTree U.S. Earnings 500 Fund (EPS), with SPY as a benchmark.
3. WisdomTree Europe Hedged Equity Fund (HEDJ), with Vanguard FTSE Europe Index Fund ETF Shares (VGK) as a benchmark.
4. WisdomTree Yield Enhanced U.S. Aggregate Bond Fund (AGGY), with iShares Core U.S. Aggregate Bond ETF (AGG) as a benchmark.
5. WisdomTree U.S. Multifactor Fund (USMF), with iShares Russell Mid-Cap ETF (IWR) as a benchmark.
6. WisdomTree 90/60 U.S. Balanced Fund (NTSX), with 90%-10% SPY-iShares 7-10 Year Treasury Bond ETF (IEF) as a benchmark.

We focus on average return, standard deviation of returns, compound annual growth rate (CAGR) and maximum drawdown (MaxDD), all based on monthly data. Using monthly dividend-adjusted returns for all specified ETFs since inceptions and for all benchmarks over matched sample periods through July 2021, we find that: Keep Reading

How well do derivatives traders perform, and why? In the July 2021 version of their paper entitled “Derivatives Leverage is a Double-Edged Sword”, Avanidhar Subrahmanyam, Ke Tang, Jingyuan Wang and Xuewei Yang study the performance of Chinese derivatives (futures) traders across 1,086 contracts on 51 underlying assets. They consider gross and net daily trader returns, turnover and degree of leverage implied by contracts held. They further investigate sources of profits/losses for these traders. To identify clearly skilled (unskilled) traders, they identify those in the top (bottom) 5% of Sharpe ratios who trade on at least 24 days during the first year of the sample period and isolate those with statistically extreme performance. They then analyze trading behaviors and results for these extreme performers the next two years. Using data from a major futures broker in China, including transaction histories, end-of-day holdings and account flows (injections and withdrawals) for 10,822 traders (315 institutional) during January 2014 through December 2016, they find that:

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