Valuing the Capital
A Macroeconomic Perspective on Stock Market Valuation Ratios
Andrew Atkeson, Jonathan Heathcote & Fabrizio Perri
NBER Working Paper, January 2026
Abstract:
Traditional valuation metrics for the U.S. stock market based on a comparison of the aggregate market value of U.S. corporations to measures of dividends, earnings, output, and the replacement cost of measured capital have been above historical norms for the past 25-30 years. Will they return to their historical means? We use macroeconomic data to argue that the observed decline in labor’s share of corporate output in conjunction with relatively weak corporate investment mechanically generates a persistent rise in the ratio of corporate valuation relative to corporate earnings, even absent any changes in expected returns or growth rates.
Do Mutual Funds Respond to Mechanical Changes in ESG Ratings?
Seungju Choi, Fabrizio Ferri & Daniele Macciocchi
Management Science, forthcoming
Abstract:
Using a quasi-experimental setting, we study whether mutual fund investors respond to a purely mechanical change in environmental, social, and governance (ESG) ratings -- that is, a change independent of concurrent changes in firms’ actual ESG activities. We find that when a firm experiences a mechanical increase in ESG ratings, the probability of being selected by an ESG fund increases (extensive margin). In contrast, if the firm is already in the fund’s portfolio, its holdings do not change (intensive margin), consistent with portfolio weighting being based on market capitalization. The selection effect is observable not only among funds that follow an ESG index but also among active ESG funds, which presumably should have the resources and ability to identify and filter out the mechanical increase in ESG ratings. Among active ESG funds, the selection effect is stronger for funds with less assets under management, larger portfolios of firms, and lower expense ratios, consistent with the notion that resource constraints may impede a fund’s screening ability. Our findings imply that passive investing based on commercial ESG ratings -- whether due to resource constraints or portfolio indexing -- might result in portfolio allocations that do not reflect the actual ESG activities of firms.
Green Window Dressing
Gianpaolo Parise & Mirco Rubin
Journal of Finance, December 2025, Pages 3555-3588
Abstract:
This paper establishes that mutual funds strategically time their trades in environmental, social, and governance (ESG) stocks around disclosure dates to inflate their sustainability ratings. This claim is supported by three empirical findings. First, we show that funds' ESG betas increase shortly before disclosure and decrease shortly afterwards. Second, we document that post-disclosure fund returns are higher but have lower ESG exposure than disclosed portfolios. Third, we provide evidence that ESG stock prices temporarily rise before disclosure and decline afterwards. Overall, we establish that green window dressing positively impacts fund sustainability ratings, performance, and flows.
Kalshi and the Rise of Macro Markets
Anthony Diercks, Jared Dean Katz & Jonathan Wright
NBER Working Paper, January 2026
Abstract:
Prediction markets offer a new market-based approach to measuring macroeconomic expectations in real-time. We evaluate the accuracy of prediction market-implied forecasts from Kalshi, the largest federally regulated prediction market overseen by the CFTC. We compare Kalshi with more traditional survey and market-implied forecasts, examine how expectations respond to macroeconomic and financial news, and how policy signals are interpreted by market participants. Our results suggest that Kalshi markets provide a high-frequency, continuously updated, distributionally rich benchmark that is valuable to both researchers and policymakers.
The Inflation Attention Threshold and Inflation Surges
Oliver Pfäuti
University of Texas Working Paper, August 2025
Abstract:
The recent inflation surge brought inflation back on people’s minds. I quantify when and how much attention to inflation changes and derive the macroeconomic implications of these attention changes. I estimate an attention threshold at an inflation rate of 4%, that attention doubles when inflation exceeds this threshold, and that supply shocks have stronger and more persistent effects on inflation in times of high attention. Developing a model featuring the attention threshold, I show that the observed attention changes offer a joint explanation for the recent inflation surge, its interplay with inflation expectations, and the long last mile of disinflation.
GIFfluence: A Visual Approach to Investor Sentiment and the Stock Market
Ming Gu et al.
NBER Working Paper, January 2026
Abstract:
We study dynamic visual representations as a proxy for investor sentiment about the stock market. Our sentiment index, GIFsentiment, is constructed from millions of posts in the Graphics Interchange Format (GIF) on a leading investment social media platform. GIFsentiment correlates with seasonal mood variations and the severity of COVID lockdowns. It is positively associated with contemporaneous market returns and negatively predicts returns for up to four weeks, even after controlling for other sentiment and attention measures. These effects are stronger among portfolios that are more susceptible to mispricing. GIFsentiment positively predicts trading volume, market volatility, and flows toward equity funds and away from debt funds. Our evidence suggests that GIFsentiment is a proxy for misperceptions that are later corrected.
The Strengthening Link Between Donald Trump’s Online Attention and Wall Street Sentiment
Raúl Gómez Martínez et al.
American Behavioral Scientist, forthcoming
Abstract:
The influence of Donald Trump on media and public discourse has been a topic of extensive analysis. His unparalleled communication skills and ability to dominate online attention raise questions about the potential market implications of his media presence. This study examines weekly data from 2020 to 2025 to test the relationship between online search interest in Trump, measured by Google Trends, and bullish sentiment from the American Association of Individual Investors survey. Ordinary least squares and Granger causality analyses reveal that increases in Trump-related search activity not only coincide with but also precede rises in investor optimism. The effect strengthens markedly in the post-2024 U.S. election period, where the explanatory power of Trump-related attention is significantly higher. These findings demonstrate that Trump’s sustained media prominence continues to shape market psychology beyond formal policymaking, establishing political attention as a causal driver of investor sentiment. The results contribute to behavioral finance by showing how digital attention metrics capture the psychological transmission of political influence to financial markets.
Teaching Economics to the Machines
Hui Chen et al.
NBER Working Paper, January 2026
Abstract:
Structural economic models, while parsimonious and interpretable, often exhibit poor data fit and limited forecasting performance. Machine learning models, by contrast, offer substantial flexibility but are prone to overfitting and weak out-of-distribution generalization. We propose a theory-guided transfer learning framework that integrates structural restrictions from economic theory into machine learning models. The approach pre-trains a neural network on synthetic data generated by a structural model and then fine-tunes it using empirical data, allowing potentially misspecified economic restrictions to inform and regularize learning on empirical data. Applied to option pricing, our model substantially outperforms both structural and purely data-driven benchmarks, with especially large gains in small samples, under unstable market conditions, and when model misspecification is limited. Beyond performance, the framework provides diagnostics for improving structural models and introduces a new model-comparison metric based on data-model complementarity.