We propose a text‐based method for measuring the cross‐border propagation of large shocks at the firm level. We apply this method to estimate the expected costs, benefits, and risks of Brexit and find widespread reverberations in listed firms in 81 countries. International (i.e., non‐U.K.) firms most exposed to Brexit uncertainty (the second moment) lost significant market value and reduced hiring and investment. International firms also overwhelmingly expected negative first‐moment impacts from the U.K.'s decision to leave the European Union (EU), particularly related to regulation, asset prices, and labor market impacts of Brexit.
Ravi's research made methodological contributions to the field of asset pricing that helped in the use of asset markets data to guide the development of superior models, which has improved our understanding of how asset prices are linked to the economy, and how investors behave.His joint work with Lars Hansen (Journal of Political Economy, 1991 and The Journal of Finance, 1997) shows that the mean variance frontier of returns, familiar to finance academics as well as professionals, contains interesting information about how investors trade current payoffs for risky future payoffs even when risk is not characterized just by the variances of the payoffs.They showed that observed returns on securities provide additional information about investors' preferences when there are no arbitrage opportunities in financial markets.The market risk premium (MRP), which is the expected return on the market above the risk-free return, plays an important role in corporate finance as a component of the cost of capital.It is well-recognized that historical average MRP is a very noisy measure, and theoretical models of how investors behave are necessary to provide confidence in estimates of what the MRP will be going forward.The papers developed two measures summarizing the information in asset returns about investors' preferences, Hansen-Jagannathan Bound and Hansen-Jagannathan Distance, both of which provide guidance for developing theoretical models of MRP.
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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.
Studying China's credit market using a structural default model that integrates credit risk, liquidity, and bailout, we document improved price discovery and a deepening divide between state‐owned enterprises (SOEs) and non‐SOEs. Amidst liquidity deterioration, the presence of government bailout helps alleviate the heightened liquidity‐driven default, making SOE bonds more valuable and widening the SOE premium. Meanwhile, the increased importance of government support makes SOEs more sensitive to bailout, while the heightened default risk increases non‐SOEs' sensitivity to credit quality. Examining the real impact, we find severe performance deteriorations of non‐SOEs relative to SOEs, reversing the long‐standing trend of non‐SOEs outperforming SOEs.
Do illegal insiders internalize legal risk? We address this question with hand‐collected data from 530 SEC (the U.S. Securities and Exchange Commission) investigations. Using two plausibly exogenous shocks to expected penalties, we show that insiders trade less aggressively and earlier and concentrate on tips of greater value when facing a higher risk. The results match the predictions of a model where an insider internalizes the impact of trades on prices and the likelihood of prosecution and anticipates penalties in proportion to trade profits. Our findings lend support to the effectiveness of U.S. regulations' deterrence and the long‐standing hypothesis that insider trading enforcement can hamper price informativeness.
We examine the timing of returns around the publication of anomaly trading signals. Using a database that captures when information is first publicly released, we show that anomaly returns are concentrated in the first month after information release dates, and these returns decay soon thereafter. We also show that the academic convention of forming portfolios in June underestimates predictability because it uses stale information, which makes some anomalies appear insignificant. In contrast, we show many anomalies do predict returns if portfolios are formed immediately after information releases. Finally, we develop guidance on forming portfolios without using stale information.
We show that utility tokens can limit the rent‐seeking activities of two‐sided platforms with market power while preserving efficiency gains due to network effects. We model platforms where buyers and sellers can meet to exchange services. Tokens serve as the sole medium of exchange on a platform and can be traded in a secondary market. Tokenizing a platform commits a firm to give up monopolistic rents associated with the control of the platform, leading to long‐run competitive prices. We show how the threat of entrants can incentivize developers to tokenize and discuss cases where regulation is needed to enforce tokenization.