Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for August 2022 (Final)
Cash TLT LQD SPY

Momentum Investing Strategy (Strategy Overview)

Allocations for August 2022 (Final)
1st ETF 2nd ETF 3rd ETF

Investing Expertise

Can analysts, experts and gurus really give you an investing/trading edge? Should you track the advice of as many as possible? Are there ways to tell good ones from bad ones? Recent research indicates that the average “expert” has little to offer individual investors/traders. Finding exceptional advisers is no easier than identifying outperforming stocks. Indiscriminately seeking the output of as many experts as possible is a waste of time. Learning what makes a good expert accurate is worthwhile.

Maximum Drawdown as Fund Performance Predictor

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

Machines Smarter than Expert Investors?

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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Active Investment Managers and Market Timing

Do active investment managers as a group successfully time the stock market? The National Association of Active Investment Managers (NAAIM) is an association of registered investment advisors. “NAAIM member firms who are active money managers are asked each week to provide a number which represents their overall equity exposure at the market close on a specific day of the week (usually Wednesday). Responses can vary widely [200% Leveraged Short; 100% Fully Short; 0% (100% Cash or Hedged to Market Neutral); 100% Fully Invested; 200% Leveraged Long].” The association each week releases (usually on Thursday) the average position of survey respondents as the NAAIM Exposure Index (NEI).” Using historical weekly survey data and Thursday-to-Thursday weekly dividend-adjusted returns for SPDR S&P 500 (SPY) over the period July 2006 through mid-July 2022, we find that: Keep Reading

Do Individual Investors Effectively Exploit Stock Momentum?

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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Online, Real-time Test of AI Stock Picking

Will equity funds “managed” by artificial intelligence (AI) outperform human investors? To investigate, we consider the performance of AI Powered Equity ETF (AIEQ). Per the offeror, the EquBot model supporting AIEQ: “…leverages IBM’s Watson AI to conduct an objective, fundamental analysis of U.S. domiciled common stocks, including Special Purpose Acquisitions Corporations (“SPAC”), and real estate investment trusts (“REITs”) based on up to ten years of historical data and apply that analysis to recent economic and news data… Each day, the EquBot Model…identifies approximately 30 to 200 companies with the greatest potential over the next twelve months for appreciation and their corresponding weights, targeting a maximum risk adjusted return versus the broader U.S. equity market. …The EquBot model limits the weight of any individual company to 10%. At times, a significant portion of the Fund’s assets may consist of cash and cash equivalents.” We use SPDR S&P 500 (SPY) as a simple benchmark for AIEQ performance. Using daily and monthly dividend-adjusted closes of AIEQ and SPY from AIEQ inception (October 18, 2017) through June 2022, we find that: Keep Reading

How to Avoid Stupid Beta?

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

Mutual Fund/Institutional Strategy Fund Performance and Performance Persistence

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

Endowments Now Just Passive Stock Market Investors?

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

Do ETFs Following Gurus/Insiders Work?

Do exchange-traded funds (ETF) that attempt to mimic holdings of hedge fund gurus and/or firm insiders offer attractive performance? To investigate, we consider seven ETFs, four live and three dead, in order of introduction:

    • Invesco Insider Sentiment (NFO) – focuses on stocks attracting interest of insiders such as company executives, fund managers and sell side analysts. This fund is dead as of February 2020.
    • Invesco BuyBack Achievers (PKW) – tracks the Nasdaq US BuyBack Achievers Index, comprised of stocks of U.S. firms with a net decline in shares outstanding of 5% or more in the last 12 months.
    • Direxion All Cap Insider Sentiment (KNOW) –  tracks the S&P Composite 1500 Executive Activity & Analyst Estimate Index, comprised of U.S. stocks that have favorable analyst ratings and are being acquired by firm insiders (top management, directors and large institutions). This fund is dead as of October 2020.
    • AlphaClone Alternative Alpha – (ALFA) – tracks the proprietary AlphaClone Hedge Fund Masters Index, comprised of U.S. securities held by the highest ranked managers of  hedge funds and institutions.
    • Global X Guru Index (GURU) – tracks the Solactive Guru Index, comprised of the highest conviction ideas from a select pool of hedge funds.
    • Direxion iBillionaire (IBLN) –  tracks the proprietary iBillionaire Index, comprised of 30 U.S. mid and large cap securities. This fund is dead as of April 2018.
    • Goldman Sachs Hedge Industry VIP (GVIP) – tracks the proprietary GS Hedge Fund VIP Index, comprised of stocks appearing most frequently among the top 10 equity holdings of fundamentally driven hedge fund managers.

We use SPDR S&P 500 (SPY) as a simple benchmark for all these ETFs. We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly returns for the above guru/insider-following ETFs and SPY as available through September 2021, we find that: Keep Reading

Optimal Approach to Investment Research

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

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