Knowledge that Transforms

Modern information technologies have fundamentally changed how information is disseminated in financial markets. Using the staggered implementation of the EDGAR system from 1993 to 1996 as a shock to information dissemination technologies, we find evidence that internet dissemination of corporate disclosures increases information production by corporate outsiders. Trades by individual investors, especially those with access to the internet, become more informative about future stock returns following the EDGAR implementation. The amount and accuracy of information produced by sell-side analysts increase after the implementation. These results suggest that greater and broader information dissemination facilitated by modern information technologies improves information production.

Research in finance and macroeconomics has routinely employed multiple horizons to test asset return predictability. In a simple predictive regression model, we find the popular scaled test can have zero power when the predictor is not sufficiently persistent. A new test based on implication of the short-run model is suggested and is shown to be uniformly more powerful than the scaled test. The new test can accommodate multiple predictors. Compared with various other widely used tests, simulation experiments demonstrate remarkable finite-sample performance. We reexamine the predictive ability of various popular predictors for aggregate equity premium.

We adapt structural models of default risk to take into account the special nature of bank assets. The usual assumption of lognormally distributed asset values is not appropriate for banks. Typical bank assets are risky debt claims with concave payoffs. Because of the payoff nonlinearity, bank asset volatility rises following negative shocks to borrower asset values. As a result, standard structural models with constant asset volatility can severely understate banks’ default risk in good times when asset values are high. Additionally, bank equity return volatility is much more sensitive to negative shocks to asset values than in standard structural models.

According to our survey about climate risk perceptions, institutional investors believe climate risks have financial implications for their portfolio firms and that these risks, particularly regulatory risks, already have begun to materialize. Many of the investors, especially the long-term, larger, and ESG-oriented ones, consider risk management and engagement, rather than divestment, to be the better approach for addressing climate risks. Although surveyed investors believe that some equity valuations do not fully reflect climate risks, their perceived overvaluations are not large.

The cross-section and time series of stock returns contains a wealth of information about the stochastic discount factor (SDF), the object that links cash flows to prices. A large empirical literature has uncovered many candidate factors—many more than seem plausible—to summarize the SDF. This special volume of the Review of Financial Studies presents recent advances in extracting information from both the cross-section and the time series, in dealing with issues of replication and false discoveries, and in applying innovative machine-learning techniques to identify the most relevant asset pricing factors. Our editorial summarizes what we learn and offers a few suggestions to guide future work in this exciting new era of big data and empirical asset pricing.

We examine institutional trading surrounding corporate news by combining a comprehensive database of newswire releases on U.S. firms with a high-frequency database of institutional trades. To identify the ability of institutions to predict or quickly interpret news, we form “news clusters” of related news about a particular firm that occurs in rapid succession. We find that institutions chiefly trade on the tone of news directly after the earliest news release in a cluster, and such news-motivated trading predicts returns over the following weeks. Our results suggest that institutional investors contribute to price efficiency through the speedy interpretation of public information.

We study the effects of policies proposed to address “short-termism” in financial markets. We examine a noisy rational expectations model in which investors’ exposures and information about fundamentals endogenously vary across horizons. In this environment, taxing or outlawing short-term investment doesn’t negatively affect the information in prices about long-term fundamentals. However, such a policy reduces short- and long-term investors’ profits and utility. Changing policies about the release of short-term information can help long-term investors—an objective of some policy makers—at the expense of short-term investors. Doing so also makes prices less informative and increases costs of speculation. Received June 24, 2018; editorial decision February 19, 2019 by Editor Stijn Van Nieuwerburgh.

We analyze early-venture fundraising from dispersed, endogenously informed investors. An entrepreneur chooses a payoff-maximizing offering, and investors communicate their information by either contributing capital or abstaining. The entrepreneur uses the information conveyed by fundraising amounts to decide whether or not to undertake a risky venture. His decision threshold hedges investors against bad projects, creating a “loser’s blessing” that encourages contributing without information. Making the offering less attractive to investors mitigates the loser’s blessing but can give rise to a winner’s curse. Both tensions reduce financing efficiency.

In this study, we take a comprehensive look at asymmetry in pay for luck, which is the finding that CEOs are rewarded for good luck, but are not penalized to the same extent for bad luck. Our main takeaway, which is based on over 200 different specifications, is that there is no asymmetry in pay for luck. Our finding is important given that the literature widely accepts the idea of asymmetry in pay for luck and typically points to this as evidence of rent extraction.

We investigate how communication within banks affects small business lending. Using travel times between a bank’s headquarters and its branches to proxy for the costs of communicating soft information, we exploit shocks to these travel times—the introduction of new airline routes—to evaluate the impact of within-bank communication costs on small business loans. We find that reducing headquarters-branch travel time boosts small business lending in the branch’s county. Several extensions suggest that new airline routes facilitate in-person communications that boost small-firm lending.