Investing Expertise

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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Should practitioners and academics doing research on financial markets be especially careful (compared to researchers in other fields) when employing statistical inference. In the July 2021 version of their paper entitled “Finance is Not Excused: Why Finance Should Not Flout Basic Principles of Statistics”, David Bailey and Marcos Lopez de Prado argue that three aspects of financial research make it particularly prone to false discoveries:

1. Due to intense competition, the probability of finding a truly profitable investment strategy is very low.
2. True findings are often short-lived due to financial market evolution/adaptation.
3. It is impossible to verify statistical findings through controlled experiments.

Based on statistical analysis principles and their experience in performing and reviewing financial markets research, they conclude that: Keep Reading

When a public pension fund reports beating its benchmark, does that signify a job well done? In his July 2021 paper entitled “Cost, Performance, and Benchmark Bias of Public Pension Funds in the United States: An Unflattering Portrait”, Richard Ennis analyzes net returns of statewide pension funds in the U.S. He calculates both (1) net Sharpe ratio and (2) return versus a matched benchmark constructed via regression as the best-fit combination of the Russell 3000 stock index, the MSCI ACWI ex-U.S. stock index and the Bloomberg Barclays US Aggregate bond index. He compares performance relative to this benchmark and estimated costs for each fund. He then assesses performance of the funds versus the benchmarks they themselves construct and report against. Using self-reported data for a sample of 24 such funds self-reported via the Public Plans Data website during July 2010 through June 2020, he finds that: Keep Reading

Can machine learning-generated stock market crash predictions be amenable to human interpretation? In their June 2021 paper entitled “Explainable AI (XAI) Models Applied to Planning in Financial Markets”, Eric Benhamou, Jean-Jacques Ohana, David Saltiel and Beatrice Guez apply a gradient boosting decision tree (GBDT) to 150 technical, fundamental and macroeconomic inputs to generate daily predictions of short-term S&P 500 Index crashes. They define a crash as a 15-day S&P 500 Index return below its historical fifth percentile within the training dataset. The 150 model inputs encompass:

1. Risk aversion metrics such as asset class implied volatilities and credit spreads.
2. Price indicators such as returns, major stock index Sharpe ratios, distance from a long-term moving average and and equity-bond correlations.
3. Financial metrics such as 12-month sales growth and price-to-earnings ratio forecasts.
4. Macroeconomic indicators such Citigroup regional and global economic surprise indexes.
5. Technical indicators such as market breath and index put-call ratio.
6. Interest rates such as 10-year and 2-year U.S. Treasury yields and break-even inflation level.

They first rank and filter the 150 inputs based on GBDT to discard about two thirds of the variables. They then apply the Shapley value solution concept to identify the most important of the remaining variables and thereby support interpretation of methodology outputs. Using daily values of the 150 model inputs and daily S&P 500 Index roll-adjusted futures prices from the beginning of January 2003 through mid-January 2021 (with data up to January 2019 used for training, the next year for validation and the rest for testing), they find that:

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