Animal Spirits

Are there “trigger” words in risk sections of annual U.S. firm 10-K reports that materially influence buying and selling of associated stocks? In his December 2024 paper entitled “Risky Words and Returns”, Sina Seyfi tests a way to predict stock returns by analyzing the text of risk disclosures in respective firm 10-K reports. Specifically, he searches for words in the risk sections that predict the cross-section of stock returns by regressing future returns on specific words. He measures the import of findings by each month reforming an equal-weighted hedge portfolio that is long (short) firms with in the highest (lowest) tenth, or decile, of emphasis on predictive risk words. Using 10-K reports, firm characteristics and returns for a broad sample of U.S. stocks and stock factor returns during 2005 (when the SEC started requiring risk sections) through 2023, he finds that: Keep Reading

Experimental (researcher-imagined) asset markets provide a controlled environment for testing hypotheses about investor behaviors. Do limits on abilities of researchers to model markets realistically, and researcher incentives/motivations, jeopardize the credibility of associated studies? In their December 2024 paper entitled “Do Experimental Asset Market Results Replicate? High-powered Preregistered Replications of 17 Claims”, Christoph Huber, Felix Holzmeister, Magnus Johannesson, Christian König-Kersting, Anna Dreber, Juergen Huber and Michael Kirchler attempt to replicate findings on behavioral drivers of asset prices from four prominent studies based on experimental asset markets.  Specifically, they seek to replicate 17 published findings on: (1) associations between asset prices and emotions, self-control, experience and gender; and, (2) trader characteristics (cognitive reflection, fluid intelligence and theory of mind) that explain trading success. 14 of the 17 published findings are reported as statistically significant. To mitigate snooping bias, they fully document (preregister) study design, analyses and statistical tests before collecting any data. When evaluating replicability, they consider statistical significance (confirmation of prior finding with p < 0.05) and the ratio of effect size in the replication to that in the original study. Using new data from 166 experimental asset markets with 1,544 participants (more than used in the original studies), they find that:

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Do some stocks entail emotional relationships that alter investor perceptions of risk and return? Is the effect exploitable? In their November 2024 paper entitled “Investor Emotions and Asset Prices”, Shehub Bin Hasan, Alok Kumar and Richard Taffler develop and test a measure of the emotional state of the market and assess its implications for individual stocks. Specifically, they each month:

1. Use a bag-of-words approach encompassing 295 emotion words to construct a market-level emotion index as the ratio of emotion words to total number of words in newspaper articles about the S&P 500 Index.
2. Estimate for each stock an emotion beta by regressing monthly excess returns versus the market emotion index over the last 60 months.
3. Sort stocks into tenths (deciles) based on last-month emotion beta and compute monthly value-weighted returns of the decile portfolios.

Using 65,825 news articles about the S&P 500 Index from 21 national and local newspapers, monthly returns and firm/stock characteristics for a listed U.S. stocks and monthly returns for various stock factors during January 1990 through September 2022, they find that:

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How do exchange-traded-funds (ETF) focused on supplying renewable energy perform? To investigate, we consider nine of the largest renewable energy ETFs, all currently available, as follows:

• • iShares Global Clean Energy (ICLN)
  • Invesco Solar (TAN)
  • First Trust NASDAQ Clean Edge Green Energy Index (QCLN)
  • Invesco WilderHill Clean Energy (PBW)
  • ALPS Clean Energy (ACES)
  • Invesco Global Clean Energy (PBD)
  • SPDR S&P Kensho Clean Power (CNRG)
  • First Trust Global Wind Energy (FAN)
  • Global X Renewable Energy Producers (RNRG)

We use SPDR S&P 500 (SPY) as a benchmark, assuming investors look at renewable energy stocks to beat the market and not to beat the energy sector. We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly returns for the nine renewable energy ETFs and SPY as available through September 2024, we find that: Keep Reading

Morningstar finds in “Mind the Gap” that poor timing of trades by mutual fund investors results in 1.7% annual underperformance of buy-and-hold (6.0% versus 7.7%) during 2013 through 2022. Is this finding correct? In the July 2024 draft of their paper entitled “Bad Timing Does Not Cost Investors One Fifth of Their Funds’ Returns: An Examination of Morningstar’s ‘Mind the Gap’ Study”, Jon Fulkerson, Bradford Jordan, Timothy Riley and Qing Yan examine the methodology of the Morningstar study and repeat calculations using an amended approach. Using monthly fund returns, net flows and assets available to Morningstar clients by fund category for a broad sample of U.S. mutual funds during January 2013 through December 2022, they find that: Keep Reading

Do narrow exchange-traded funds (ETF), such as specific technology-focused funds, exhibit a predictable lifecycle of fund inflows that inflate prices of holdings followed by fund outflows that depress prices of holdings? In their May 2024 paper entitled “Ponzi Funds”, Philippe van der Beck, Jean-Philippe Bouchaud and Dario Villamaina decompose ETF returns into price pressure (self-inflated) and fundamental components, with the former a function of the concentration of ETF holdings and flows of investor money into and out of the fund. They then compare performances of funds with relatively high and relatively low self-inflated returns. Using daily holdings of U.S. equity ETFs during 2019 through 2023, they find that: Keep Reading

What happens to retail investor performance when brokers make trading apps game-like (gamification)? In his June 2024 paper entitled “Gamification of Stock Trading: Losers and Winners”, Eduard Yelagin examines how traders react to injections of gamification in mobile trading apps offered by major U.S. brokers. For each app update, he reviews developer notes about its purpose to identify whether it is trading gamification or a bug fix. He then employs execution price clues to identify concurrent buying and selling by retail investors (who are most likely to use mobile apps) associated with those updates. Using information for 1,419 mobile trading app updates from 17 major U.S. brokers and concurrent trading data for 1,404 stocks during 2018 through 2021, he finds that:

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Is proximity to doom good or bad for the U.S. stock market? To measure proximity to doom, we use the Doomsday Clock “Minutes-to-Midnight” metric, revised intermittently in late January via the Bulletin of the Atomic Scientists, which “warns the public about how close we are to destroying our world with dangerous technologies of our own making. It is a metaphor, a reminder of the perils we must address if we are to survive on the planet.” Using the timeline for the Doomsday Clock since inception in 1947 and contemporaneous end-of-year levels of the S&P 500 Index through 2023, we find that:

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In their 2023 book, The Missing Billionaires: A Guide to Better Financial Decisions, authors Victor Haghani and James White seek “to give you a practical framework, consistent with the consensus of university finance textbooks, for making good financial decisions that are right for you. Good decisions will take account of your personal circumstances, financial preferences, and your considered views on the risks and expected returns of available investments. …You will likely get the most out of this book if you have already accumulated a decent amount of financial capital or if you are young with a healthy measure of human capital. …The book is written from the perspective of a US individual or family…” Based on their many years of wealth management experience and portfolio systems development, they conclude that:

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What trading experience makes individual day traders quit trading? In their November 2023 paper entitled “Why Do Individuals Keep Trading and Losing?”. Fernando Chague, Bruno Giovannetti, Bernardo Guimaraes and Bernardo Maciel study the life cycle of individual traders who repeatedly open and close stock or futures positions on the same trading day. They focus on gross daily profit for individuals who begin day-trading during the sample period, day-trade for at least 30 trading days and then quit day-trading during the sample period. They ignore trading costs, costs of any trading courses taken and taxes. Using anonymized daily trade data for all Brazilian day traders during 2012 through 2018, they find that: Keep Reading