Investing Expertise

Do some mutual fund managers game Morningstar ratings/benchmarks by shifting the styles of their funds? In their September 2024 paper entitled “Box Jumping: Portfolio Recompositions to Achieve Higher Morningstar Ratings”, Lauren Cohen, David Kim and Eric So investigate how mutual fund managers exploit investor reliance on Morningstar ratings by adjusting holdings to jump their funds into size/value styles with low benchmarks. They focus on active U.S. and global equity mutual funds during the period from five years before to five years after June 2002, when Morningstar began rating funds by style. They include dead funds to avoid survivorship bias. Using Morningstar style assignments, Morningstar ratings and performance data for active equity mutual funds during 1997 through 2007 (with some data through 2022), they find that:

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Can Google’s large language model (LLM), Gemini, beat simple benchmarks by picking a small portfolio of stocks? In their September 2024 paper entitled “Can AI Beat a Naive Portfolio? An Experiment with Anonymized Data”, Marcelo Perlin, Cristian Foguesatto, Fernanda Müller and Marcelo Righi test the ability of Gemini 1.5 Flash to weight a portfolio of five U.S. stocks for different investment horizons (1, 6, 12 or 36 months) using either financial data, price data or a combination of both. The tests employ real but anonymized financial data, ensuring that confounding training data about specific firms does not infect stock weighting decisions. Testing involves 18,000 iterations of five steps:

• Select a random date during 2004 through 2023 with equal daily probability.
• Based on this date, randomly select five U.S firms/stocks that have financial data, prices over $1.00 and average daily volumes over $250,000 over the prior five years.
• Create anonymized versions of the financial and price data.
• Ask Gemini to assign allocations from a fixed total amount to the five stocks, with the possibility of also allocating to the contemporaneous 5-year U.S. Treasury note yield.
• Compare performance of the Gemini-weighted portfolio during the given investment horizon to that of the S&P 500 Index and an equal-weighted portfolio of the same stocks/Treasury note yield.

Using inputs as specified for 1,522 distinct firms during 2004 through 2023, they find that: Keep Reading

Do exchange-traded funds (ETF) that seek to mimic holdings of top-ranked hedge funds, firm insiders or other investing gurus offer attractive performance? To investigate, we consider nine ETFs, five live and four 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.
  • Guru Favorite Stocks (GFGF) – tracks stock holdings of about 20 quality-oriented gurus who have publicly available records of at least 10 years.
  • Motley Fool Next Index (TMFX) – tracks the performance of mid- and small-capitalization U.S. companies recommended by The Motley Fool analysts and newsletters.

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 2024, we find that: Keep Reading

Can relatively modest large language models (LLM), pretrained with diverse financial information, effectively rank stocks? In their September 2024 paper entitled “Re(Visiting) Large Language Models in Finance”, Eghbal Rahimikia and Felix Drinkall introduce base and small versions of FinText, LLMs that are: (1) kept compact compared to state-of-the-art LLMs to allow practical use with personal computers; and, (2) pre-trained by calendar year with diverse financial information. They test FinText stock-ranking ability by each day:

• Asking it to review Dow Jones Newswires articles available by 9:00AM and tagged as having significant news about cited firms (available since February 2013).
• Measuring the accuracy of its resulting predictions for individual stock price directions.
• Tracking performance of an equal-weighted or value-weighted portfolio that is each day at the market open long (short) the fifth of stocks with the highest (lowest) probabilities of positive daily returns.

They use articles from 2013 through 2016 for FinText training and those from 2017 through 2023 for testing. They compare accuracy and performance of FinText to those of unspecialized LLMs much larger than base FinText. Using pretraining information, daily Dow Jones Newswires articles and daily returns for a broad sample of U.S. stocks during 2013 through 2023, they find that: Keep Reading

Would someone who knows tomorrow’s financial headlines today be a good day trader? In their September 2024 paper entitled “When a Crystal Ball Isn’t Enough to Make You Rich”, Victor Haghani and James White report results of “The Crystal Ball Challenge.” They ask 118 young adults trained in finance to trade the S&P 500 Index and 30-year U.S. Treasury bonds on 15 days with an initial stake of $50 (and up to 50X leverage), based on one-day-in-advance front pages of the Wall Street Journal with price data blacked out. The days are chosen randomly from: (1) first, all days in the top half of daily market volatilities; and then, (2) one third with employment reports, one third with Federal Reserve Bank announcements and one third random. They repeat the experiment with five highly experienced macro traders (head of trading at a top U.S. bank, founder of large macro hedge fund, senior trader at large macro fund, former senior government bond trader at a large U.S. primary dealer and former senior Jane Street trader). Using roughly 2,000 trades from these November 2023 experiments, they find that:

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Can researchers train machine learning models to mimic top mutual fund managers? In his August 2024 paper entitled “Machine Learning from the Best: Predicting the Holdings of Top Mutual Funds”, Jean-Paul van Brakel seeks to anticipate and exploit the stock picking of top-performing U.S. equity mutual fund managers by:

• Using a large set of holdings-based measures and component firm/stock factor exposures and accounting ratios to rank stocks on the likelihood that top managers will pick them. He considers five models: three machine learning models (decision tree, random forest and gradient boosting), a linear (logit regression) model and simple selection based on recent holdings. For machine learning models, he each quarter uses quarterly data from four years ago to one year ago for training and quarterly data from last year for generating holdings likelihoods for next quarter.
• Calculating average absolute Shapley value for each firm/stock characteristic to assess its importance in the decision process.
• Assessing economic value of predictions for each model by each quarter creating six groups of stocks, first sorting stocks into large and small and then sorting each size sort into thirds based on likelihood that top fund managers will pick the stocks. He then each quarter reforms a probable-minus-improbable (PMI) factor portfolio that is long large and small stocks with the highest likelihoods and short those with the lowest.

He applies a 6-factor (market, size, book-to-market, profitability, investment, momentum) model based on daily calculations to compare alphas across funds, features and the PMI factor portfolio. Using quarterly/daily data for U.S. broad equity mutual fund holdings/returns and associated individual firm/stock data starting December 1998 and ending December 2022 and December 2023, respectively, he finds that: Keep Reading

Are large and sophisticated investors (whales) better than small retail investors (minnows) at timing established crypto-asset markets? In their August 2024 paper entitled “Beneath the Crypto Currents: The Hidden Effect of Crypto ‘Whales'”, Alan Chernoff and Julapa Jagtiani compare short-term timing abilities of whales and minnows trading Ethereum (ETH). Specifically, they explore relationships between next-day ETH returns and ETH holdings in e-wallets of four size groups: (1) more than $1 million (whales); (2) $100,000 to $1 million; (3) $10,000 to $100,000; and, (4) less than $10,000 (minnows). They control for supply of ETH in circulation and major crypto-asset market events. Using daily data for ETH from Coin Metrics, including price (midnight to midnight) and holdings/value by e-wallet size group, during January 2018 through December 2023, they find that:

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Do analysts systematically ignore the connection between future firm earnings and current economic conditions? In their July 2024 paper entitled “Predicting Analysts’ S&P 500 Earnings Forecast Errors and Stock Market Returns Using Macroeconomic Data and Nowcasts”, Steven Sharpe and Antonio Gil de Rubio Cruz examine the quality of bottom-up forecasts of near-term S&P 500 earnings aggregated from analyst forecasts across individual firms. Specifically, they:

• Model expected aggregate S&P 500 quarterly earnings growth as a function of GDP growth, output and wage inflation and change in dollar exchange rate. They also consider a simplified model based only on real GDP growth and change in the dollar exchange rate.
• Calculate the gap between modeled S&P 500 earnings growth and analyst-forecasted growth.
• Estimate how well this forecast gap predicts analyst forecast errors.
• Test the extent to which the forecast gap predicts S&P 500 Index total returns.

Using quarterly actual and forecasted S&P 500 earnings, S&P 500 Index total return and values for the specified economic variables during 1993 through 2023, they find that: Keep Reading

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 late July 2024, we find that: Keep Reading

Does Michael Farr, CEO and founder of Farr, Miller & Washington, offer good stock picks via his annual CNBC articles identifying the best 10 stocks for the next year? To investigate, we take his picks for 2022, 2023 and 2024, calculate the associated annual (first half only for 2024) total returns for each stock and compute the equal-weighted average return for the 10 stocks for each year. We use SPDR S&P 500 ETF Trust (SPY) as a benchmark for these averages. Using year-end or end of June 2024 dividend-adjusted stock prices for the specified stocks-year, we find that: Keep Reading