Journal of Empirical Finance最新文献

We examine the relationship between stock price synchronicity and stock liquidity using a comprehensive data set across 40 countries. Our local (within-country) empirical results reveal a positive relationship between local synchronicity and stock liquidity. The strength of this positive relationship depends on the quality of country-level institutions; the weaker the institutional environment, the stronger the synchronicity-liquidity relationship. Importantly, our global (across-country) findings mirror those at the local level. Overall, our study provides a comprehensive analysis of the synchronicity-liquidity relationship at both the local and global levels. In addition, our cross-sectional analyses provide new evidence on the institutional determinants of this relationship.

Huang and Kilic (2019) demonstrate that gold to platinum price ratio (GP), which proxies for tail risk in the economy, is a priced risk factor in the cross-section of stock returns. We document that GP negatively affects the mutual fund flows of the active equity funds. In cross-sectional regressions, we find that funds with high betas with respect to the change in GP (${\beta }_{\Delta GP}$) have larger future fund flows, as such funds provide a hedge against economic distress. Further, ${\beta }_{\Delta GP}$ helps predict the future performance of the fund in the next few quarters. ${\beta }_{\Delta GP}$ also relates negatively to the downside risk of the fund, implying that funds could potentially reduce their left-tail risk by tilting towards securities with above average ${\beta }_{\Delta GP}$. We also examine the flows to active corporate bond funds and passive funds. While these effects of GP are largely observable for passive funds, they are not as strongly observable for corporate bond funds.

We evaluate the performance of eleven asset pricing models in the Chinese A-share market using a variety of test portfolios and statistical methodologies. To compile the test portfolios, we construct 105 anomalies and use the 23 significant anomalies as test assets for model comparison. The results indicate that, in time-series test and anomaly explanations, the Hou et al. (2019) five-factor q model demonstrates the best overall performance. The pairwise cross-sectional $R^2$ tests and multiple model comparison tests further affirm that the Hou et al. (2019) five-factor q model, the Fama and French (2018) six-factor (FF6) model, and the Kelly et al. (2019) five-factor Instrumented Principal Component Analysis (IPCA5) model are the top performers. Notably, the performance of the five-factor q model remains robust across various experimental designs.

We investigate how shareholder-debtholder conflict of interest affects the corporate tax avoidance using a unique setting of the affiliated and unaffiliated commercial bankers’ board representation. Consistent with the notion that board representation grants lenders’ access to private information that helps monitor and influence firms’ tax practice, we find that appointments of affiliated banker directors significantly reduce firms’ tax avoidance behavior, while appointing unaffiliated banker directors shows no such effect. The impact of affiliated banker directors on alleviating tax avoidance is stronger among firms with severer conflict of interest between shareholders and debtholders, specifically among firms with weaker corporate governance, higher financial leverage and higher CEO stock ownership.

We evaluate US market return predictability using a novel data set of several hundred ag- gregated firm-level characteristics. We apply LASSO, Elastic Net, Random Forest, Neural Net, Extreme Gradient Boosting, and Light Gradient Boosting Machine methods and find these models experience large prediction errors that lead to forecast failures. However, winsorizing and pooling machine learning model forecasts provides consistent out-of-sample predictability. To assess robustness, we apply machine learning methods to high-dimensional data for Canada, China, Germany and the UK as well as the Goyal–Welch data. All machine learning models we consider, except for the ensemble pooled methods, fail to significantly predict returns across our samples, highlighting the importance of pooling, evaluating additional economies, and the fragility of individual machine learning methods. Our results shed light on the sparsity versus density debate as the degree of sparsity and variable importance evolves over time.