To understand the dynamics of investors' asset demands, we develop a general-equilibrium model driven by a single latent variable: heterogeneity in investors' confidence about mean endowment growth. The model predicts persistent heterogeneity in asset demands and concentrated portfolios. Consistent with the data, limited confidence reduces investors' demand elasticities and makes stock prices excessively volatile-driven by latent demand rather than observable characteristics. The underlying economic mechanisms are driven primarily by investors' desire to hedge changes in future beliefs instead of current disagreement. Finally, consistent with survey data, investors' expectations correlate positively with past returns and negatively with future returns.

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI

ABSTRACT In statistics, samples are drawn from a population in a data‐generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence‐generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer‐review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.

Many asset pricing models assume that expected returns are driven by common factors. We formulate a model where returns are driven by a string, and no-arbitrage restricts each expected return to capture the asset's granular exposure to all other asset returns: a correlation premium. The model predicts fresh properties for big stocks, which display higher connectivity in bad times, but also work as correlation hedges: they contribute to a negative fraction of the correlation premium, and portfolios that are more exposed to them command a lower premium. The string model performs at least as well as many existing linear factor models.

We present a nonparametric method to recover a bound on ex ante dispersion of beliefs (DBB) from asset prices with minimal assumptions. DBB constrains the dispersion among all possible distributions in an economy, consistent with observed prices and subject to a good-deal bound. In model-based economies, DBB effectively tracks belief heterogeneity and serves as a diagnostic tool for evaluating model calibrations. Empirically, DBB relates to common proxies of belief dispersion, offering a real-time, market-implied disagreement measure. Our versatile approach applies to both complete and incomplete markets represented by any asset class. This paper was accepted by Kay Giesecke, finance. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.01587 .

We estimate the risk premium for firm-level climate change exposure among S&P 500 stocks and its time-series evolution between 2005 to 2020. Exposure reflects the attention paid by market participants in earnings calls to a firm’s climate-related risks and opportunities. When extracted from realized returns, the unconditional risk premium is insignificant but exhibits a period with a positive risk premium before the financial crisis and a steady increase thereafter. Forward-looking expected return proxies deliver an unconditionally positive risk premium with maximum values of 0.5%–1% p.a., depending on the proxy, between 2011 and 2014. The risk premium has been lower since 2015, especially when the expected return proxy explicitly accounts for the higher opportunities and lower crash risks that characterize high-exposure stocks. This finding arises as the priced part of the risk premium primarily originates from uncertainty about climate-related upside opportunities. In the time series, the risk premium is negatively associated with green innovation; Big Three holdings; and environmental, social, and governance fund flows and positively associated with climate change adaptation programs. This paper was accepted by Colin Mayer, Special Section of Management Science on Business and Climate Change. Funding: Funding is provided by the Deutsche Forschungsgemeinschaft [Grant 403041268 – TRR 266] (L. van Lent and R. Zhang), the Institute for New Economic Thinking (L. van Lent), the 111 Project [Grant B18033] (R. Zhang), the Shanghai Pujiang Program (R. Zhang), and the Ministry of Education Project of Key Research Institute of Humanities and Social Science (R. Zhang). Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4686 .

We derive generalized bounds on conditional expected excess returns that can be computed from option prices. The generalized lower bound may serve as an expected excess return proxy for individual and basket-type assets, is conditionally tight, accounts for the entire risk-neutral distribution of returns, and outperforms existing variance-based models in out-of-sample predictions. Bounds calibrated to realized returns correspond to reasonable risk aversion and prudence. On average, expected stock returns given by the bounds decrease on even weeks of the Federal Open Market Committee cycle. Cross-sectional tests deliver a reasonable market risk premium. This paper was accepted by Haoxiang Zhu, finance. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4367 .

We show that an increase in stock return exposure to media attention to narratives, measured with standard methods for extracting topic attention from news text, leads to a lower stock price informativeness about future fundamentals. Empirically, narrative exposure explains over 86% of idiosyncratic variance in the cross-section, and both narrative exposure and non-systematic information channels—idiosyncratic variance and variance related to public information—decrease stock price informativeness. Moreover, stocks with high narrative exposure demonstrate elevated trading volume. To rationalize the empirical results, we suggest a mechanism based on biased media and investors. In the model, narrative exposure proxies for media bias-driven return volatility and is inversely related to price informativeness.

This empirical study is the first in a series documenting empirical properties of 0DTE options on the S&P500 index (SPX) and popular strategies built from these options for the sample period from 09/2016 to 05/2023. We focus on strategies established every trading day and held to expiry (i.e., unconditional and static trading rules). We show that 0DTEs deliver significant variance risk premium, i.e., the implied variance priced in these options is, on average, higher than the realized variance until settlement. Realized returns of individual options are highly volatile and skewed, though at the median, buying deep in-the-money calls and selling out-the-money calls and puts can be profitable. Median returns for popular options strategies are mostly negative (strangles, bull and bear spreads, call and put ratio spreads), except for risk reversal, which produces consistently positive mean and median returns in our sample period.

Strong regulatory actions are needed to combat climate change, but climate policy uncertainty makes it difficult for investors to quantify the impact of future climate regulation. We show that such uncertainty is priced in the option market. The cost of option protection against downside tail risks is larger for firms with more carbon-intense business models. For carbon-intense firms, the cost of protection against downside tail risk is magnified at times when the public's attention to climate change spikes, and it decreased after the election of climate change skeptic President Trump.

We show that an equal-weighted portfolio has a higher total return than a value-weighted portfolio. As one may expect, this is partly because the equal-weighted portfolio has higher exposure to value and size factors, but we show that a considerable part (42%) comes from rebalancing to maintain constant weights. We then demonstrate, through four applications, that inferences from asset-pricing tests are substantially different depending on whether one uses equal- or value-weighted portfolios. These four applications are tests of the: Capital Asset Pricing Model, spanning properties of the stochastic discount factor, relation between characteristics and returns, and pricing of idiosyncratic volatility.

To understand the dynamics of investor asset demands, we develop a multiperiod general-equilibrium model driven by a single latent variable, differences in beliefs, resulting from heterogeneity in investors' confidence regarding the return dynamics of assets. Consistent with the data, investors' asset holdings are concentrated and display large and persistent heterogeneity in asset demands across investors. Moreover, demand curves are steeper than with homogeneous beliefs. The time-series and cross-sectional variation in assets' realized and expected returns, as well as their volatilities, are driven by the mean and dispersion of latent demand.

We study learning and uncertainty under the factor investing paradigm using an endogenous information model with correlated assets. As investors shift attention from firms towards systematic risk factors, stock prices become less informative, increasing systematic uncertainty and incentivizing learning about the systematic risk. This learning complementarity leads to multiple regimes in systematic uncertainty and attention allocation. We specify and estimate a model-based, forward-looking measure of attention to systematic versus firm-level information. Consistent with the model, the measure follows a regime-switching process. The high-level regime is linked to lower stock price sensitivity to firm-specific information and a higher systematic risk concentration.

Standard asset pricing theories treat return volatility and correlations as two intimately related quantities, which hinders achieving a neat definition of a correlation premium. We introduce a model with a continuum of securities that have returns driven by a string. This model leads to new arbitrage pricing restrictions, according to which, holding any asset requires compensation for the granular exposure of this asset returns to changes in all other asset returns: an average correlation premium. We find that this correlation premium is both statistically and economically significant, and considerably fluctuates, driven by time-varying correlations and global market developments. The model explains the cross-section of expected returns and their counter-cyclicality without making reference to common factors affecting asset returns. It also explains the time-series behavior of the premium for the risk of changes in asset correlations (the correlation-risk premium), including its inverse relation with realized correlations.

We develop a simple dynamic general-equilibrium framework that can jointly rationalize many empirically observed features of household portfolios, investment returns, and wealth dynamics. The model differs from traditional models only along a single, natural dimension: households differ in their confidence about the return processes for risky assets. Less-confident households (but with unbiased beliefs) overinvest in safe assets, hold underdiversified portfolios concentrated in familiar assets, are trend chasers, and earn lower absolute and risk-adjusted investment returns. More confident households hold riskier positions and exhibit superior market-timing abilities. Despite Bayesian learning, this investment behavior persists for long periods, thereby exacerbating wealth inequality.

We develop a method that identifies the attention paid by earnings call participants to firms' climate change exposures. The method adapts a machine learning keyword discovery algorithm and captures exposures related to opportunity, physical, and regulatory shocks associated with climate change. The measures are available for more than 10,000 firms from 34 countries between 2002 and 2020. We show that the measures are useful in predicting important real outcomes related to the net-zero transition, in particular, job creation in disruptive green technologies and green patenting, and that they contain information that is priced in options and equity markets.