Research

Do illegal insiders internalize legal risk? We address this question with hand-collected data from 530 SEC 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 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.

In statistics, samples are drawn from a population in a data- generating process (DGP). Standard errors measure the uncer- tainty in sample estimates of population parameters. In sci- ence, evidence is generated to test hypotheses in an evidence- generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sam- ple. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.

We address the determination of bitcoin prices and decentralized security. Users forecast the transactional and resale value of holdings, pricing the risk of malicious systemic attacks. Miners contribute resources to protect against attackers, competing for block rewards. Bitcoin’s design leads to multiple equilibria: the same technology and fundamentals are consistent with sharply different price and security levels. Bitcoin’s monetary policy can lead to welfare losses and deviations from quantity theory. Price–security feedback amplifies fundamental shocks’ volatility impact and leads to boom–busts not driven by fundamentals. We show how Bitcoin’s viability versus fiat currency depends on relative acceptability and inflation protection.

Using over 5000 equity and option trades unequivocally based on nonpublic information about firm fundamentals, we find that commonly used asymmetric information proxies (AIPs) display abnormal values on days with informed trading. Volatility and trading volume are abnormally high, whereas illiquidity is low, both in equity and option markets. Daily returns reflect the sign of private signals but, on average, bid–ask spreads are 10% and 20% lower when informed investors are present in stock and option markets. Market makers’ learning under event uncertainty and the use of limit orders by informed investors help explain these findings. We characterize cross-sectional responses based on the duration of private information and find that informed traders select days with high uninformed volume to trade. Evidence from the U.S. Securities and Exchange Commission (SEC) Whistleblower Reward Program and the Financial Industry Regulatory Authority (FINRA) involvement address potential selection concerns.

We analyze trading speed and fragmentation in asset markets. In our model, trading venues make technological investments and compete for investors who choose where and how much to trade. Faster venues charge higher fees and attract speed-sensitive investors. Competition among venues increases investor participation, trading volume, and allocative efficiency, but entry and fragmentation can be excessive, and speeds are generically inefficient. Regulations that protect transaction prices (e.g., Securities and Exchange Commission trade-through rule) lead to greater fragmentation. Our model sheds light on the experience of European and U.S. markets since the implementation of Markets in Financial Instruments Directive and Regulation National Markets System.