Executive risk incentives,product market competition,and R&D

Prior studies have examined the relation between product market competition (PMC) and research and development (R&D) investments, while the impact of executive risk incentives on this relation remains unexplored. In this study, we find that Vega (the sensitivity of executives’ wealth to stock return volatility) weakens the negative relation between PMC and R&D. We also find that Vega strengthens the negative relation between PMC and firm performance when R&D investments grow higher. In sum, our results suggest that high‐Vega compensation portfolios in competitive environments may induce executives to overinvest in R&D projects, therefore hurting firm performance.     

Product market competition and productivity shocks

I investigate the interaction effects of competition and productivity shocks on stocks’ earnings and returns. I find that the sensitivities of earnings and returns to productivity shocks are negatively associated with competition intensity. I also find that the excess returns of productivity shocks-sorted portfolios are lower when competition intensity is high, even after controlling for known return predictors. Overall, the empirical evidence shows firms are less exposed to productivity shocks when competition is high. As such, this study provides a possible mechanism through which the structure of product markets affects stock returns.     

The impact of COVID-19 pandemic on the dynamic correlations between gold and U.S. equities: evidence from multifractal cross-correlation analysis

This study exploits multifractal cross-correlation analysis (MFCCA) to investigate the impact of the COVID-19 pandemic on the cross-correlations between gold and U.S. equity markets using 1-min high-frequency data from January 1, 2019, to December 29, 2020. The MFCCA method shows that the pandemic caused an increase of multifractality in cross-correlations between the two markets. Specifically, the cross-correlations of small fluctuations became more persistent while those of large fluctuations became less persistent, explaining the source of multifractality. The findings of this study carry significant implications for investors, academicians, and policymakers. For example, the increase of multifractality of cross-correlation means that the non-linear relationship between gold and U.S. equity returns prevails more during economic downturns. Therefore, academicians may resort to non-linear techniques to evaluate the relationship between gold and U.S. equity markets during the health pandemic. Moreover, investors can know the value of hedging benefits over different investment time horizons during the pandemic. Finally, policymakers can better assess the economic downturns (i.e., those caused by health pandemics) over the dynamics of cross-correlation between gold and equity markets to make sound financial policies.