In this paper, I examine survivorship bias in hedge fund returns by comparing two large databases. I find that the survivorship bias exceeds 2% per year. Results of survivorship bias by investment styles indicate that the biases are different across styles. I reconcile the conflicting results about survivorship bias in previous studies by showing that the two major hedge fund databases contain different amounts of dissolved funds. Empirical results show that poor performance is the main reason for a fund's disappearance. Furthermore, I find that there are significant differences in fund returns, inception date, net assets value, incentive fee, management fee, and investment styles for the 465 common funds covered by both databases. Mismatching between reported returns and the percentage changes in NAVs can partially explain the differences in returns.

A long-standing question in finance is why companies donate to charity, often attributing it to either managerial agency problems or strategic behavior. Based on a global sample of donation announcements by firms providing relief to disaster-affected communities, we test the relative importance of these two motives and the conditions under which each dominates. We exploit disaster-specific factors in an event study setting around corporate donation announcement dates to show that, on average, relief donations decrease returns. However, the strategic benefits of donating around salient events can mitigate these negative effects. To account for firms’ donation decisions, we rely on exogenous variation in the availability of corporate charitable funds due to the timing of disasters relative to firms’ financial years. We show that donations provide new information to the market and that negative returns are primarily driven by cash donations made via corporate foundations.

In this paper, we model earnings management as a consequence of the interaction among self‐interested economic agents ‐ namely, the managers, the shareholders, and the regulators. In our model, a manager controls a stochastic production technology and makes periodic accounting reports about his or her performance; an owner chooses a compensation contract to induce desirable managerial inputs and reporting choices by the manager; and a regulatory body selects and enforces accounting standards to achieve certain social objectives. We show that various economic trade‐offs give rise to endogenous earnings management. Specifically, the owner may reduce agency costs by designing a compensation contract that tolerates some earnings management because such a contract allocates the compensation risk more efficiently. The earnings‐management activity produces accounting reports that deviate from those prescribed by accounting standards. Given such reports, the valuation of the firm may be nonlinear and s‐shaped, thereby recognizing the manager's reporting incentives. We also explore policy implications, noting that (1) the regulator may find enforcing a zero‐tolerance policy ‐ no earnings management allowed ‐ economically undesirable; and (2) when selecting the optimal accounting standard, valuation concerns may conflict with stewardship concerns. We conclude that earnings management is better understood in a strategic context that involves various economic trade‐offs.

Two complementary sources of information are studied in a multiperiod agency model. One is an accounting source that partially but credibly conveys the agent's private information through accounting recognition. The other is an unverified communication by the agent (i.e., a self‐report). In a simple setting with no communication, alternative labor market frictions lead to alternative optimal recognition policies. When the agent is allowed to communicate his or her private information, accounting signals serve as a veracity check on the agent's self‐report. Finally, such communication sometimes makes delaying the recognition optimal. We see contracting and confirmatory roles of accounting as its comparative advantage. As a source of information, accounting is valuable because accounting reports are credible, comprehensive, and subject to careful and professional judgement. While other information sources may be more timely in providing valuation information about an entity, audited accounting information, when used in explicit or implicit contracts, ensures the accuracy of the reports from nonaccounting sources.

Evidence from the laboratory and the field has uncovered both underreaction and overreaction to new information. We provide new experimental evidence on the underlying mechanisms of under‐ and overreaction by comparing how people make inferences and revise forecasts in the same information environment. Participants underreact to signals when inferring about underlying states, but overreact to the same signals when revising forecasts about future outcomes—a phenomenon we term “the inference‐forecast gap.” We show that this gap is largely driven by different simplifying heuristics used in the two tasks. Additional treatments suggest that the choice of heuristics is affected by the similarity between statistics in the information environment and the statistic elicited by the belief‐updating problem.

We study deep neural networks and their use in semiparametric inference. We establish novel nonasymptotic high probability bounds for deep feedforward neural nets. These deliver rates of convergence that are sufficiently fast (in some cases minimax optimal) to allow us to establish valid second‐step inference after first‐step estimation with deep learning, a result also new to the literature. Our nonasymptotic high probability bounds, and the subsequent semiparametric inference, treat the current standard architecture: fully connected feedforward neural networks (multilayer perceptrons), with the now‐common rectified linear unit activation function, unbounded weights, and a depth explicitly diverging with the sample size. We discuss other architectures as well, including fixed‐width, very deep networks. We establish the nonasymptotic bounds for these deep nets for a general class of nonparametric regression‐type loss functions, which includes as special cases least squares, logistic regression, and other generalized linear models. We then apply our theory to develop semiparametric inference, focusing on causal parameters for concreteness, and demonstrate the effectiveness of deep learning with an empirical application to direct mail marketing.

Quantile factor models (QFM) represent a new class of factor models for high‐dimensional panel data. Unlike approximate factor models (AFM), which only extract mean factors, QFM also allow unobserved factors to shift other relevant parts of the distributions of observables. We propose a quantile regression approach, labeled Quantile Factor Analysis (QFA), to consistently estimate all the quantile‐dependent factors and loadings. Their asymptotic distributions are established using a kernel‐smoothed version of the QFA estimators. Two consistent model selection criteria, based on information criteria and rank minimization, are developed to determine the number of factors at each quantile. QFA estimation remains valid even when the idiosyncratic errors exhibit heavy‐tailed distributions. An empirical application illustrates the usefulness of QFA by highlighting the role of extra factors in the forecasts of U.S. GDP growth and inflation rates using a large set of predictors.

This paper finds that stocks of repurchasers with high sensitivity to investor sentiment are more likely to be mispriced. Thus, such repurchases are followed by superior post-buyback stock performance. This abnormal return associated with sensitivity to sentiment cannot be explained by other undervaluation factors: book-to-market or prior return effects. My results are robust with factor model analysis and controls for contamination effects. I conclude that this sentiment-driven undervaluation may result from the difficulty to value and/or limits to arbitrage rather than investor overreaction.

The outperformance of repurchasing firms with a high book-to-market (B/M) ratio is usually explained by investors’ undervaluation of the firm’s past performance. However, several studies suggest that the underestimation of future intangible value may explain the high return associated with the share repurchase. To better understand the actual information content of repurchases, I decompose the B/M ratio into past tangible information and future intangible information and find that repurchase signals an undervaluation of the intangible return. In addition, I investigate several potential proxies for intangible information—R&D expenses, intangible assets, and future operating performance. My results show that intangible information signals the undervaluation of future operating performance.

IN the Far East, the overall volume of intraregional trade is not as substantial as that of Europe, but much larger than that of Latin America.2 One rather unique and very interesting feature of Far Eastern intraregional trade is that one country, namely, Japan, stands out prominently, from the point of view of both the character and magnitude of its trade. Japan contributed about one-third of the Far Eastern intraregional trade in the immediate prewar years (32.3 per cent for 1935, 34.5 per cent for 1937, and 38.o per cent for I938). Immediately after the cessation of hostilities, its share suffered a sharp reduction, but again became a significant percentage of the total in 1949 (I6.5 per cent). The ratio of Far Eastern intraregional trade to its total export, and Japan's share in intraregional trade showed a tendency to fluctuate together.3 Since Japan's share was about one-third in the prewar period and its intraregional imports were related to the volume of total intraregional exports, several questions may be raised with regard to the future role of Japan in the intraregional trade of the Far East. What are the initial and secondary effects of Japan's imports from the Far East on the intraregional exports of the countries of this region? Does such relationship in the prewar period remain true in the postwar period? If there is a change of preand postwar relationships, what are some of the reasons for the change? How are Japan's imports from the Far East related to the over-all exports of the region to all countries? This paper is a preliminary attempt at an analysis of the above questions.