We construct a "universe" of over 18,000 fundamental signals from financial statements and use a bootstrap approach to evaluate the impact of data mining on fundamental-based anomalies. We find that many fundamental signals are significant predictors of cross-sectional stock returns even after accounting for data mining. This predictive ability is more pronounced following high-sentiment periods and among stocks with greater limits to arbitrage. Our evidence suggests that fundamental-based anomalies, including those newly discovered in this study, cannot be attributed to random chance, and they are better explained by mispricing. Our approach is general and we also apply it to past return–based anomalies.

We study the long-run effects of product market advertising on the equity of firms conducting initial public offerings (IPOs). We find that firms going public with a greater extent of advertising prior to their IPOs are valued higher both in the IPO as well as in the immediate aftermarket, are associated with greater upward price revisions from the pre-IPO filing range means, and have smaller long-run post-IPO stock returns. The above results hold even after controlling for the effects of investor attention, as proxied by the pre-IPO media coverage received by firms going public. We show, using a number of additional tests, that the above findings are consistent with the implications of the heterogeneous beliefs theories of Miller (1977), Harrison and Kreps (1978), and Morris (1996), along with an assumption that product market advertising increases the heterogeneity in outside investor beliefs about firms going public.

We estimate the impact of a poor bank examination rating on the growth rates of individual bank loan portfolios. We use a novel approach to control for loan demand variation and estimate a fixed-effect model using an unbalanced panel with over 381,000 bank-quarter observations from the period 1994–2011. Our estimates show that a poor examination rating has a large negative impact on bank loan growth, even after controlling for the impact of monetary policy, bank capital and liquidity conditions, and any voluntary reduction in lending triggered by weak legacy loan portfolio performance or other bank losses. This previously unidentified effect is consistent with the hypothesis that the bank supervision process successfully constrains the lending activities of banks operating in an unsafe and unsound manner.

We construct a “universe” of over 18,000 fundamental signals from financial statements and use a bootstrap approach to evaluate the impact of data mining on fundamental-based anomalies. We find that many fundamental signals are significant predictors of cross-sectional stock returns even after accounting for data mining. This predictive ability is more pronounced following high-sentiment periods and among stocks with greater limits to arbitrage. Our evidence suggests that fundamental-based anomalies, including those newly discovered in this study, cannot be attributed to random chance, and they are better explained by mispricing. Our approach is general and we also apply it to past return–based anomalies. Received October 22, 2015; editorial decision October 27, 2016 by Editor Andrew Karolyi.

This paper studies the cross-sectional risk–return trade-off in the stock market. A fundamental principle in finance is the positive relation between risk and expected return. However, recent empirical evidence suggests the opposite. Using several intuitive risk measures, we show that the negative risk–return relation is much more pronounced among firms in which investors face prior losses, but the risk–return relation is positive among firms in which investors face prior gains. We consider a number of possible explanations for this new empirical finding and conclude that reference-dependent preference is the most promising explanation.

Each year approximately 10 million high school seniors in China compete for 6 million seats through a centralized college admissions system. Within the last decade, many provinces have transitioned from a "sequential" to a "parallel" mechanism to make their admissions decisions. In this study, we characterize a parametric family of application-rejection assignment mechanisms, including the sequential, deferred acceptance, and parallel mechanisms in a nested framework. We show that all of the provinces that have abandoned the sequential mechanism have moved toward less manipulable and more stable mechanisms. We also show that existing empirical evidence is consistent with our theoretical predictions.

We model conditional market beta and alpha as flexible functions of state variables identified via a formal variable-selection procedure. In the post-1963 sample, the beta of the value premium comoves strongly with unemployment, inflation, and the price–earnings ratio in a countercyclical manner. We also uncover a novel nonlinear dependence of alpha on business conditions: It falls sharply and even becomes negative during severe economic downturns but is positive and flat otherwise. The conditional capital asset pricing model (CAPM) performs better than the unconditional CAPM, but this does not fully explain the value premium. Our findings are consistent with a conditional CAPM with rare disasters.

Motivated by a theoretical model, we examine for 43 countries whether it is policy or policy uncertainty that affects technological innovation more. Innovation activities, measured by patent-based proxies, are not, on average, affected by which policy is in place. Innovation activities, however, drop significantly during times of policy uncertainty measured by national elections. The drop is greater for more influential innovations (citations in the right tail, exploratory rather than exploitative innovations) and for innovation-intensive industries. We use close presidential elections and ethnic fractionalization to address endogeneity concerns. We uncover the mechanism underlying the main result by showing that the number of patenting inventors decreases with policy uncertainty. Political compromise, we conclude, encourages innovation.

The de novo evolution of protein-coding genes from noncoding DNA is emerging as a source of molecular innovation in biology. Studies of random sequence libraries, however, suggest that young de novo proteins will not fold into compact, specific structures typical of native globular proteins. Here we show that Bsc4, a functional, natural de novo protein encoded by a gene that evolved recently from noncoding DNA in the yeast S. cerevisiae, folds to a partially specific three-dimensional structure. Bsc4 forms soluble, compact oligomers with high β sheet content and a hydrophobic core, and undergoes cooperative, reversible denaturation. Bsc4 lacks a specific quaternary state, however, existing instead as a continuous distribution of oligomer sizes, and binds dyes indicative of amyloid oligomers or molten globules. The combination of native-like and non-native-like properties suggests a rudimentary fold that could potentially act as a functional intermediate in the emergence of new folded proteins de novo.