This article presents an augmented deep factor model that generates latent factors for cross-sectional asset pricing. The conventional security sorting on firm characteristics for constructing long–short factor portfolio weights is nonlinear modeling, while factors are treated as inputs in linear models. We provide a structural deep-learning framework to generalize the complete mechanism for fitting cross-sectional returns by firm characteristics through generating risk factors (hidden layers). Our model has an economic-guided objective function that minimizes aggregated realized pricing errors. Empirical results on high-dimensional characteristics demonstrate robust asset pricing performance and strong investment improvements by identifying important raw characteristic sources.

We document fragile demand for socially responsible investments (SRIs) by retail mutual fund investors. Using COVID-19 as an economic shock, we show funds with higher sustainability ratings experienced sharper declines in retail flows during the pandemic, controlling for fund characteristics. The decline in retail SRI fund flows is sharper than that of institutional flows, more pronounced when economies are hit harder by COVID-19, and unlikely to be driven by fund performance, past flows and size, or shifting investor attention. Corroborated by out-of-sample survey evidence, our findings highlight the high sensitivity of SRI demand by retail investors with respect to income shocks.

This article provides a survey of China’s initial public offering (IPO) market, focusing on IPO pricing, bids and allocation, and aftermarket trading. We show that strict regulations result in suppressed IPO offer prices and high initial returns, causing a high cost of going public. Investors treat IPOs as lotteries with extremely high short-term returns, with little attention to the long term. The auction selling method, however, works in the way it is supposed to. Mutual funds bid in a more informative way than other investors, and their advantages are unlikely to be due to underwriters’ preferential treatment. We also discuss the latest registration-system reform.

We document the unique structure of the peer-to-peer lending market. Originally designed as decentralized, the market has become highly, but not fully, reintermediated. The platforms’ software now performs essentially all tasks related to loan evaluation, whereas most lenders are passive and automatically fund most applications on offer. Yet unlike banks, and in contrast to theories predicting full reintermediation, the platforms provide detailed loan information, and some active loan pickers coexist with passive investors. We argue that while intermediation attracts unsophisticated passive investors, transparency in the presence of active investors resolves the lending platform’s moral hazard problem inherent in intermediated markets.

In business research, firm size is both ubiquitous and readily measured. Complexity, another firm-related construct, is also relevant, but difficult to measure and not well-defined. As a result, complexity is less frequently incorporated in empirical designs. We argue that most extant measures of complexity are one-dimensional, have limited availability, and/or are frequently misspecified. Using both machine learning and an application-specific lexicon, we develop a text solution that uses widely available data and provides an omnibus measure of complexity. Our proposed measure, used in tandem with 10-K file size, provides a useful proxy that dominates traditional measures.

We analyze a model in which an anomaly is unknown to arbitrageurs until its discovery, and test the model implications on both asset prices and arbitrageurs’ trading activities. Using data on 99 anomalies documented in the existing literature, we find that the discovery of an anomaly reduces the correlation between the returns of its decile-1 and decile-10 portfolios. This discovery effect is stronger if the aggregate wealth of hedge funds is more volatile. Finally, hedge funds increase (reverse) their positions in exploiting anomalies when their aggregate wealth increases (decreases), further suggesting that these discovery effects operate through arbitrage trading.

This article introduces a new holding horizon measure of active management and examines its relation to future risk-adjusted fund performance (alpha). Our measure reveals a wide cross-sectional dispersion in mutual fund investment horizons, and shows that long-horizon funds exhibit positive future long-term alphas by holding stocks with superior long-term fundamentals. Further, stocks largely held by long-horizon funds outperform stocks largely held by short-horizon funds by more than $ 3\% $ annually, adjusted for risk, over the following 5-year period. We also find a clientele effect: to reduce liquidity costs, long-horizon funds attract more long-term investors through share classes that carry load fees.

Sophisticated investors frequently choose to publicly disclose private information, a phenomenon inconsistent with most theories of speculation. We propose and test a model to bridge this gap. We show that when a speculator cares about both short-term portfolio value and long-term profit, a disclosure mixing asset fundamentals and her holdings is optimal by inducing competitive dealership to revise prices toward those holdings while alleviating adverse selection. We find that when mutual fund managers have stronger short-term incentives, the frequency of strategic non-anonymous disclosures about their stocks by market-worthy newspaper articles increases and those stocks’ liquidity improves, consistent with our model.

We study the valuation of collateral by comparing spreads on loans by the same bank, to the same borrower, at the same origination date, but backed by different types of collateral. Pledging collateral reduces borrowing costs by 23 BPS on average. The effect varies across different types of collateral, with marketable securities being most valuable, and real estate and accounts receivables and inventory being more valuable than fixed assets and a blanket lien. Further, the rate reduction from pledging collateral is sensitive to the value of the underlying collateral, and collateral tends to be more valuable for smaller and private firms and for loans with longer maturity.

What are the implications of an aging population for financial stability? To examine this question, we exploit geographic variation in aging across U.S. counties. We establish that banks with higher exposure to aging counties increase loan-to-income ratios. Laxer lending standards lead to higher nonperforming loans during downturns, suggesting higher credit risk. Inspecting the mechanism shows that aging drives risk-taking through two contemporaneous channels: deposit inflows due to seniors’ propensity to save in deposits; and depressed local investment opportunities due to seniors’ lower credit demand. Banks thus look for riskier clients, especially in counties where they operate no branches.

We document and explain the sharp performance deterioration of smart beta indexes after the corresponding exchange-traded funds (ETFs) are launched for investment. While smart beta is purported to deliver excess returns through factor exposures, the market-adjusted return of smart beta indexes drops from about 3% “on paper” before ETF listings to about −0.50% to −1% after ETF listings. This performance decline cannot be explained by variation in factor premia, strategic timing, or diminishing returns to scale. Instead, we find strong evidence of data mining in the construction of smart beta indexes, which helps ETFs attract flows, as investors respond positively to backtests.

We test the signaling view of corporate social responsibility (CSR) engagement using two complementary quasi-natural experiments that impose exogenous negative pressure on stock prices. Firms under such adverse price pressure increase CSR activities compared to otherwise similar firms. This effect concentrates among firms with stronger signaling incentives, namely, those facing greater information asymmetry, more product market competition, higher shareholder litigation risk, and higher stock price crash risk. Firms under the exogenous negative price pressure mainly improve CSR strengths, including costly environmental investments. We also find that CSR engagement attracts socially responsible investors and lowers the cost of capital for signaling firms.

We build a novel comprehensive data set of new product trademarks as an output measure of product development innovation. We show that risk-taking incentives in CEO compensation motivate this type of innovation and that this innovation improves firm performance. Using an exogenous shock to executive compensation, we find that reductions in stock option compensation cause reductions in new product development. We also find that firms undertaking new product development experience increases in future cash flow from operations and return on assets. These findings suggest the importance of product development innovation to firms and new trademarks as a novel innovation measure.

We document the causal effects of single-name options trading on the absolute level of information content of prices (stock price informativeness) by exploiting the Penny Pilot Program as an exogenous shock to options trading volume. We find that options trading increases underlying stock price informativeness and information acquisition by both option and stock investors, consistent with the framework of Goldstein and Yang (2015). The findings are driven by firms for which options are more important sources of information and firms with more efficiently priced options. Options market introduction in a sample of 25 other economies also leads to higher price informativeness.

We demonstrate the benefits of merging traditional hypothesis-driven research with new methods from machine learning that enable high-dimensional inference. Because the literature on post-earnings announcement drift (PEAD) is characterized by a “zoo” of explanations, limited academic consensus on model design, and reliance on massive data, it will serve as a leading example to demonstrate the challenges of high-dimensional analysis. We identify a small set of variables associated with momentum, liquidity, and limited arbitrage that explain PEAD directly and consistently, and the framework can be applied broadly in finance.

A short squeeze occurs if borrowed shares are recalled and the short seller is unable to find another source of shares. This forces the short seller to terminate a position early. For most stocks, the probability of a short squeeze is very low. Short squeezes, however, are not unusual for the hardest to borrow stocks. For these stocks, trading costs from squeezes are high and have a significant impact on the returns to short selling. For hard-to-borrow stocks, short sellers also miss out on significant abnormal returns because squeezes force them to close positions.

Big data on job postings reveal multiple facets of the impact of COVID-19 on corporate hiring. Firms disproportionately cut new hiring for high-skill positions, with financially constrained firms reducing skilled hiring the most. Applying machine learning methods to job-ad texts, we find that firms have skewed their hiring toward operationally-core functions. New positions display greater flexibility regarding schedules and tasks. While job posting levels show signs of recovery starting in late-2020, changes to job descriptions and skill profiles persist through early-2022. Financial constraints amplify these changes, with constrained firms’ new hires witnessing greater adjustments to job roles and employment arrangements.

We examine how broad changes in work arrangements and lifestyles brought on by the COVID-19 pandemic have affected households’ location decisions. Using data on over 360,000 residential, interstate moves over the last 5 years, we find that more than 12% of moves were directly influenced by the pandemic. Among pandemic-influenced movers, over 15% of households cite that remote work influenced their move. Lifestyle-related (job-related) migration increased (decreased) significantly, particularly for the set of households who are likely to have access to remote work. We further find that these changes in migration patterns are positively related to post-pandemic economic growth.

Retail order imbalance positively predicts returns, but on average retail investor trades lose money. Why? Order imbalance tests equal-weighted stocks, but retail purchases concentrate on attention-grabbing stocks that subsequently underperform. Long–short strategies based on extreme quintiles of retail order imbalance earn dismal annualized returns of −14.8% among stocks with heavy retail trading but earn 6.6% among other stocks. Our results reconcile the literatures on the performance of retail investors, the predictive content of retail order imbalance, and attention-induced trading and returns. Smaller retail trades concentrate more on attention-grabbing stocks and perform worse.

Announcements of mergers very often discuss the immediate impact of the deal on the acquirer’s earnings per share (EPS). We argue that the focus on EPS reflects the difficulty of evaluating and communicating deal synergy in mergers and acquisitions (M&A) practice and provide supporting evidence. We show that the acquirer’s EPS focus affects how deals are structured, the premium that is paid, and the types of deals that are done. EPS-driven M&A decisions are also associated with costly distortions in the acquirer’s financial and investment policies.