Are investors and traders cats, rationally and independently sniffing out returns? Or are they cows, flowing with a herd that must know something? These blog entries relate to behavioral finance, the study of the animal spirits of investing and trading.
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 2025, we find that:
Do optimists dominate the pricing of stocks for firms with unusual/difficult to interpret fundamentals, thereby overpricing them? In his December 2025 paper entitled “Hard to Process: Atypical Firms and the Cross-Section of Expected Stock Returns”, Sebastian Weibels relates future stock returns to a measure of the atypicality (ATYP) of firm fundamentals via an autoencoder (unsupervised machine learning model). The autoencoder learns the typical pattern of fundamentals across firms, and ATYP aggregates individual firm deviations from that pattern. High-ATYP firms present unusual combinations of characteristics difficult to understand. Using monthly values for 117 firm fundamentals and associated stock prices for NYSE, AMEX and NASDAQ common stocks, excluding financial and utility sectors and stocks trading below $1, during 1971 through 2023, he finds that:Keep Reading
Investor mood may affect financial markets. Sports may affect investor mood. The biggest mood-mover among sporting events in the U.S. is likely the National Football League’s Super Bowl. Is the week before the Super Bowl especially distracting and anxiety-producing? Is the week after the Super Bowl focusing and anxiety-relieving? Presumably, post-game elation and depression cancel between respective fan bases. Using past Super Bowl dates since inception and daily/weekly S&P 500 Index levels for 1967 through 2025 (59 events), we find that:Keep Reading
Having been trained by humans on human information, do Large Language Models (LLM) behave like human investors? In their January 2026 paper entitled “Artificially Biased Intelligence: Does AI Think Like a Human Investor?”, Javad Keshavarz, Cayman Seagraves and Stace Sirmans investigate whether 48 widely used LLMs exhibit any of 11 known cognitive biases in financial decision-making. They speculate that LLMs acquire biases via human-authored training data, statistical learning and responses that reward perceived helpfulness over logical consistency. Specifically, they test whether:
Any biases vary across LLMs with different levels of intelligence.
Users can intervene to suppress any biases in real-time LLM use.
Their prompt-pair methodology ensures that findings are causal rather than just correlational. Using 25 prompt-pairs per each of 11 biases across 48 LLMs, they find that:Keep Reading
Do analysts/investors predictably and exploitably misinterpret tones of earnings calls? In their October 2025 paper entitled “Do Investors Get It Right? Reaction Bias to Earnings Calls”, Zhenzhen Fan and Fred Liu study interactions between textual information in earnings conference calls and analyst revisions to their next-quarter earnings forecasts. Specifically, they:
For each stock and each following analyst: (1) link call transcripts to the closest pre-call and post-call analyst forecasts, and (2) measure analyst reaction as the fraction of the pre-announcement forecast error corrected in the post-announcement revision.
Use Shapley values to assess the contribution of each model feature to predictive power.
Across stocks and earnings calls, use a random forest model to test whether earnings call transcripts predict direction and magnitude of reaction bias. Use seven rolling years of the sample for model training/validation and test predictive accuracy each next year.
Each month sort stocks into fifths (quintiles) based on predicted reaction bias and form a value-weighted, monthly rebalanced portfolio that is long stocks with the highest signal and short the stocks with the lowest. Keep a stock within its quintile until the end of the month of its next earnings announcement.
Using earnings conference call transcripts, analyst earnings forecasts, actual earnings and monthly stock return data during 2006 through 2023, they find that:
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:
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 2025, we find that:Keep Reading
Is it reasonable to assume that strong earnings growth and price-to-earnings ratio (P/E) expansion will sustain the unusually strong U.S. stock market returns of the past decade? In his brief September 2025 paper entitled “Expected Stock Returns in Bullish Times”, Javier Estrada decomposes stock returns into: (1) dividend yield, (2) change in earnings and (3) change in P/E. He then employs this decomposition to compare the bullish environments at the end of the 1990s with that of the summer of 2025, including an outlook for the next decade. Using Robert Shiller’s prices, earnings and dividends for the S&P Composite Index during 1872 through June 2025, he finds that:Keep Reading
What variables drive differences in price-to-earnings ratios (P/E) across U.S. stocks? In the September 2025 revision of their paper entitled “The Cross-section of Subjective Expectations: Understanding Prices and Anomalies”, Ricardo Delao, Xiao Han and Sean Myers analyze cross-sectional P/E differences of U.S. common stocks by comparing:
Professional forecasts of earnings growth, return and P/E over the next four years.
Actual (realized) earnings growth, returns and P/E over the next four years.
Using the specified forecasted and actual data during 1999 (1982 for some inputs) through 2020, they find that:Keep Reading
Do individual investors trade carefully and cautiously, or do they make snap judgments? In their March 2025 paper entitled “The Research Behavior of Individual Investors”, Toomas Laarits and Jeffrey Wurgler employ browser histories for an approximately representative sample of U.S. individual investors to address:
How much time do they spend on stock research?
Which sites do they use?
On which stocks do they focus?
How proximate is their research to their trades or to corporate events?
What types of information do and do not attract their attention?
Using browser and trading histories, with confidential/personal data removed, from 484 volunteer households that make 2,911 individual U.S. stock trades via online brokerage accounts during January, February, March and June of 2007, they find that:
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
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