Investing Expertise

Do hedge funds rapidly move to exploit, and thereby weaken/extinguish, newly discovered stock return anomalies? In the December 2022 version of their paper entitled “Anomaly Discovery and Arbitrage Trading”, Xi Dong, Qi Liu, Lei Lu, Bo Sun and Hongjun Yan measure the post-publication role of hedge funds on 99 published stock return anomalies (or latest working paper dates if unpublished). For each anomaly, they:

1. Calculate a five-year rolling correlation of monthly returns between the extreme tenths (deciles 1 and 10) of anomaly stock sorts, minus the correlation between deciles 5 and 6 to control for unrelated trends.
2. Analyze via quarterly SEC Form 13F holdings aggregate U.S. hedge fund differential trading of extreme decile stocks.

Using monthly returns for the 99 anomalies as available starting in 1926 and hedge fund SEC Form 13F filings as available starting 1981, both through 2020, they find that: Keep Reading

Are their practical ways to suppress the sometimes large reduction in academic (gross) equity factor premiums due to trading frictions and other implementation obstacles? In their March 2023 paper entitled “Smart Rebalancing”, Robert Arnott, Feifei Li and Juhani Linnainmaa first examine the performance and related turnover of seven long-only factor premiums: annually reformed (end of June) value, profitability, investment, and a composite of the three; and, monthly reformed value and momentum, and a composite of the two. Their long-only factor portfolios hold market-weighted stocks in the top fourth of factor signals. They reinvest any dividends in all stocks in the portfolios, such that dividends do not affect portfolio weights. They test three ways to suppress periodic turnover via a turnover limit:

1. Proportional Rebalancing – trade all stocks proportionally to meet the turnover limit.
2. Priority Best – buy stocks with the strongest factor signals and sell stocks with the weakest, until reaching the turnover limit.
3. Priority Worst – buy stocks that only marginally qualify for the factor portfolio and sell those that just barely fall out (with the strongest buy and sell signals last), until reaching the turnover limit.

They also apply these three turnover suppression tactics to non-calendar reformation, triggered when the difference between the current and target portfolios exceeds a specified threshold. They ignore the 100% initial formation turnover common to all portfolios. Using  accounting data and common stock returns for all U.S. publicly listed firms during July 1963 through December 2020, with portfolio tests commencing July 1964, they find that: Keep Reading

Can machine learning perfect equity sector rotation? In the January 2023 version of their paper entitled “Deep Sector Rotation Swing Trading”, flagged by a subscriber, Joel Bock and Akhilesh Maewal present a sector rotation strategy guided by multiple-input, multiple output deep learning model. The strategy chooses weekly from among 11 U.S. sectors using exchange-traded fund (ETF) proxies. Specifically, each week during each year, they:

• Train the machine learning model on the last two years of weekly (Friday close) historical sector ETF prices and volumes and sometimes auxiliary economic data (10-year U.S. Treasury yield, USD currency index, crude oil proxy and stock market volatility) to predict next-week opening and closing prices for each ETF.
• Compare the predicted return estimate for each ETF to a dynamically updated threshold return to screen for potential buys.
• Apply additional filters to screen out potential buys with unusual past losses to accommodate investor loss aversion.
• At the next-week open, allocate available capital to surviving sector ETFs based on respective past win rate (profitable trade) and respective past sector trade momentum.
• Liquidate all positions just prior to the next-week close.

Their benchmark is buying and holding the S&P 500 Index with reinvested dividends. Using weekly inputs as described during January 2012 through December 2022, they find that:

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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 mid-February 2023, we find that: Keep Reading

Each year in December, Barron’s publishes its list of the best 10 stocks for the next year. Do these picks on average beat the market? To investigate, we scrape the web to find these lists for years 2011 through 2022, calculate the associated calendar year total return for each stock and calculate the average return for the 10 stocks for each year. We use SPDR S&P 500 ETF Trust (SPY) as a benchmark for these averages. We source most stock prices from Yahoo!Finance, but also use Historical Stock Price.com for a few stocks no longer tracked by Yahoo!Finance. Using year-end dividend-adjusted stock prices for the specified stocks-years from the end of 2010 through the end of 2022, we find that: Keep Reading

Does aggregate insider stock buying and selling offer clues about future stock market returns? In their January 2023 paper entitled “Aggregate Insider Trading in the S&P 500 and the Predictability of International Equity Premia”, Andre Guettler, Patrick Hable, Patrick Launhardt and Felix Miebs investigate relationships between net aggregate insider trading and future stock market excess returns at horizons from one month to one year. They define net aggregate insider trading as unscheduled open market insider purchases minus sales, divided by purchases plus sales. They focus on S&P 500 firm insider trading and S&P 500 Index excess returns (relative to the U.S. Treasury bill yield). They also consider U.S. non-S&P 500 insider trading. They further look at insider trading and stock market excess returns within Canada, France, Germany, Great Britain and Italy. Using monthly aggregations of the specified insider trading data from 2iQ and monthly stock market index returns during January 2004 through December 2018, they find that:

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A subscriber suggested an update of “GMO’s Stunningly Accurate Forecast?” with out-of-sample testing of GMO forecasts. To investigate, we test GMO’s 7-Year asset class real return forecasts of December 31, 2010, July 31, 2013, June 30, 2014 and November 2015. We first match the 11 GMO asset classes covered in these forecasts to exchange-traded funds (ETF), as follows:

1. U.S. equities (large cap) – SPDR S&P 500 ETF Trust (SPY).
2. U.S. equities (small cap) – iShares Russell 2000 ETF (IWM).
3. U.S. high quality – Invesco S&P 500 Quality ETF (SPHQ).
4. International equities (large cap) – iShares MSCI EAFE ETF (EFA).
5. International equities (small cap) – iShares MSCI EAFE Small-Cap ETF (SCZ).
6. Emerging equities – iShares MSCI Emerging Markets ETF (EEM).
7. U.S. bonds (government) – iShares 20+ Year Treasury Bond ETF (TLT).
8. International bonds (government) – SPDR Bloomberg Barclays International Treasury Bond ETF (BWX).
9. Emerging bonds – iShares J.P. Morgan USD Emerging Markets Bond ETF (EMB).
10. Inflation-indexed bonds – iShares TIPS Bond ETF (TIP).
11. Short-term U.S. Treasuries (30 days to 2 years) – iShares 1-3 Year Treasury Bond ETF (SHY).

We adjust monthly ETF returns for inflation using monthly changes in U.S. Consumer Price Index (CPI). We then calculate the real compound annual growth rate (CAGR) for each over specified forecast horizons. Using GMO forecasts, dividend-adjusted ETF prices and CPI data during December 2010 through November 2022, we find that: Keep Reading

What is the critical success factor for experienced traders? In their December 2022 paper entitled “Strategic Sophistication and Trading Profits: An Experiment with Professional Traders”, Marco Angrisani, Marco Cipriani and Antonio Guarino compare results from a competitive trading game, a competitive guessing game and individual cognitive/risk preference/personality tests to determine what characteristics most strongly relate to trading success. They recruit two groups for testing: (1) professional traders and portfolio managers working in London financial markets; and, (2) as a control, a gender-matched (mostly male), multi-disciplinary group of undergraduate students with little or no experience trading real markets. For each group, they conduct:

1. A competitive group trading game wherein participants trade an asset that earns dividends over multiple periods in a continuous double auction. Profits derive from both dividends accrued (fundamental value) and any capital gain (ability to outwit other players by buying low and selling high).
2. A competitive group guessing game wherein each participant chooses a number from 0 to 100 with the goal of guessing the number is closest to two thirds of the average choice. Choices reveal how deeply participants reflect on the thinking of others.
3. An individual guessing game (to disentangle reasons for group guessing game outcomes), two tests of cognitive ability (intelligence), tests to elicit risk preferences/confidence and personality traits.

Using game/test results for 56 professional traders and 56 undergraduate students, they find that: Keep Reading

A reader asked: “I am wondering how come you have not rated Motley Fool guys. Any insight?” To augment the test of Motley Fool public stock picks in “‘Buy These Stocks for 2019’ Forward Test”, we look at two more lists of stock picks: “10 Top Stocks That Will Make You Richer in 2021” with publication date 1/5/2021; and, “7 Stocks That Could Make You Richer in 2022” with publication date 1/5/2022. We calculate total (dividend-reinvested) returns for stocks in the first list during 1/5/2021 through 12/31/2021 and for stocks in the second list during 1/5/2022 through 11/11/2022. We compare average returns for these lists to returns for SPDR S&P 500 ETF Trust (SPY) over matched sample periods. Using dividend-adjusted closing prices for SPY and each of the stocks in the two lists on the specified beginning and end dates, we find that: Keep Reading

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. This fund is dead as of August 2022.
  • 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 2022, we find that: Keep Reading