Animal Spirits

“Meme Stocks Over the Long Run” finds that meme stock portfolios generate attractive gross returns in recent years. Can investors exploit that finding via an exchange-traded fund (ETF)? To investigate, we look at MEME, an active ETF that aims to hold 15-25 meme stocks selected based on retail trading activity, volatility, liquidity and social media activity/sentiment. Portfolio weights for the stocks held by MEME are discretionary. Because of its very short history and high turnover, we look at daily returns, along with compound annual growth rate (CAGR) and maximum drawdown (MaxDD). We choose Vanguard Total Stock Market Index Fund ETF (VTI) as a benchmark. Using daily dividend-adjusted prices for MEME and VTI during October 8, 2025 (limited by MEME) through July 22, 2026, we find that: Keep Reading

Is the meme stock phenomenon new, or is it a persistent feature of stock markets made visible only recently via social media? In his July 2026 paper entitled “A Century of Meme Stocks: The Modern, Ephemeral Premium”, Chad Schmerling addresses this question by constructing meme stock selection models based on market data alone and extendable in some form back to 1926. The model specifications measure the extent to which a stock’s price is being set by coordinated attention rather than fundamentals. The specifications derive from the holdings of a prior passive incarnation of the Roundhill MEME exchange-traded fund (ETF), which selected stocks based on WallStreetBets mention scores and short interest. He translates these holdings into patterns based on the following data: shape of recent returns; trading volume/abnormality; option surface; option flow; short-sale constraints; institutional ownership; and, retail order flow proxies. He constructs five models based on the progression of historical data availability for these variables as of 1926, 1950, 1976, 1996 and 2010. Using the specified data for every U.S. common stock as available during January 1926 through August 2025, he finds that: Keep Reading

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:

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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:

• LLMs reliably exhibit economically meaningful cognitive biases in financial decision problems.
• 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:

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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 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