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In this study, we examine whether the social capital surrounding the firm's corporate headquarters mitigates managerial self-dealing in the form of opportunistic insider trading. We find strong evidence that the level of social capital in the region surrounding the firm's headquarters is negatively and significantly associated with insider trading profitability. We also find that the negative association between social capital and insider trading profitability is more pronounced when governance is weaker and corporate opacity is higher, instances where insiders have greater opportunities to trade on their private information. Further analyses on the potential mechanisms suggest that the negative association is stronger when the firm's social networks are denser and when the civic norms in the region are stronger. Overall, our article contributes to the growing social capital literature in accounting and finance by providing direct empirical evidence that social capital mitigates managerial self-serving behavior in the form of opportunistic insider trading. [ABSTRACT FROM AUTHOR]

We show theoretically that the usual estimated investment strategies will not achieve the optimal Sharpe ratio when the dimensionality is high relative to sample size, and the $ 1/N $ rule is optimal in a 1-factor model with diversifiable risks as dimensionality increases, which explains why it is difficult to beat the $ 1/N $ rule in practice. We also explore conditions under which it can be beaten, and find that we can outperform it by combining it with the estimated rules when $ N $ is small, and by combining it with anomalies or machine learning portfolios, conditional on the profitability of the latter, when $ N $ is large. [ABSTRACT FROM AUTHOR]

To provide consumers with a channel to interact directly with service retailers, some service platforms have allowed consumers to incentivize service retailers for better service via tipping. There are pros and cons of allowing and not allowing tipping in terms of service levels and costs, which can affect consumer utility and platform profitability. By developing a game-theoretic model, we analyze the tipping strategy choices of platforms under competition. We find that when the service ability of service retailers is high, platforms with service retailer pricing should allow tipping, whereas for at least one platform with platform pricing, they should allow it only when the commission rate is also high. Interestingly, if consumers are concerned about service levels in addition to service prices during tipping, for platforms with platform pricing, only one platform should allow tipping when the service ability is medium. However, when the service ability is low or when the sensitiveness of the service level to tips exceeds the threshold, neither platform should allow tipping, regardless of pricing models. Finally, an all-win outcome for service platforms, service retailers, and consumers may be achieved only when platforms adopt different pricing models. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We propose a loglinear present-value identity in which investment ("scale"), profitability ("yield"), and discount rates determine a firm's market-to-book ratio. Our identity reconciles existing influential market-to-book decompositions and facilitates novel insights from three empirical applications: (1) Both investment and profitability are important contributors to the value spread and stock return news variance. (2) Any cross-sectional return predictability has a mirror image in cash-flow fundamentals, providing asset pricing theories with additional moments to match. (3) The investment spread significantly improves the predictability of time-series variation in the value premium and justifies the poor performance of value in recent years. [ABSTRACT FROM AUTHOR]