Abstract We identify a novel economic mechanism through which passive ownership positively affects informational efficiency in the cross-section of firms. Passive investors’ inelastic demand lowers a firm’s cost-of-capital, inducing it to take more risk. The higher cash flow variance, in turn, incentivizes active investors to acquire more precise private information, pushing up price informativeness for firms with high passive ownership. High passive ownership also implies higher stock prices and higher stock-return variances. An increase in the aggregate size of passive investors amplifies these cross-sectional differences. We also document complementarities in firms’ real investment and investors’ information choices that can cause information crashes. Received May 31, 2020; editorial decision January 4, 2023 by Editor Holger Mueller. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online

We study the joint portfolio and information choice problem of institutional investors who are concerned about their performance relative to a benchmark. Benchmarking influences information choices through two distinct economic mechanisms. First, benchmarking reduces the number of shares in investors’ portfolios that are sensitive to information. Hence, the value of private information declines. Second, benchmarking limits investors’ willingness to speculate. This not only reduces the value of private information but also adversely affects information aggregation. In equilibrium, investors acquire less information and informational efficiency declines. As a result, return volatility increases, and less-benchmarked institutional investors outperform more-benchmarked ones. Received May 31, 2017; editorial decision July 4, 2018 by Editor Stijn Van Nieuwerburgh. Authors have furnished supplementary code, which is available on the Oxford University Press Web site next to the link to the final published paper online.

[We use forward-looking information from option prices to estimate option-implied correlations and to construct an option-implied predictor of factor betas. With our implied market betas, we find a monotonically increasing risk-return relation, not detectable with standard rolling-window betas, with the slope close to the market excess return. Our implied betas confirm a risk-return relation consistent with linear factor models because, when compared to other beta approaches: (i) they are better predictors of realized betas, and (ii) they exhibit smaller and less systematic prediction errors. The predictive power of our betas is not related to known relations between option-implied characteristics and returns.]

We use forward-looking information from option prices to estimate option-implied correlations and to construct an option-implied predictor of factor betas. With our implied market betas, we find a monotonically increasing risk-return relation, not detectable with standard rolling-window betas, with the slope close to the market excess return. Our implied betas confirm a risk-return relation consistent with linear factor models because, when compared to other beta approaches: (i) they are better predictors of realized betas, and (ii) they exhibit smaller and less systematic prediction errors. The predictive power of our betas is not related to known relations between option-implied characteristics and returns. The Author 2012. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: [email protected]., Oxford University Press.

ABSTRACT We incorporate trading fees into a dynamic, multiagent general‐equilibrium model in which traders optimally decide when to trade. For that purpose, we propose an innovative algorithm that synchronizes the traders. Securities prices are not so much affected by the payment of the fees itself, but rather by the trade‐off that the traders face between smoothing consumption and smoothing holdings. In calibrated examples, the interest rate and welfare decline with trading fees, while risk premia and volatilities increase. Liquidity risk and expected liquidity are priced, leading to deviations from the consumption‐CAPM. With trading fees, capital is slow‐moving, generating slow price reversal.

Abstract 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.