This paper examines how firm innovation strategies—specifically the degree of exploration versus exploitation in patenting activity—mediate monetary policy transmission to firm-level outcomes. By employing event study methods and local projection regressions, we explore the differential effects of unexpected changes in monetary policy on firm stock returns, capital expenditures, and R&D. We find that firms engaged in more explorative innovation are significantly more sensitive to monetary shocks, particularly in the days following an FOMC announcement. Notably, firms with lower liquidity exhibit greater susceptibility to these shocks, underscoring the importance of financial constraints in moderating the innovation-monetary policy. Moreover, we note an asymmetry in responses to contractionary versus expansionary shocks.
This paper applies machine learning methods to predict the Canadian yield curve using a comprehensive set of macroeconomic variables. Lagged values of the yield curve and a wide array of Canadian and international macroeconomic variables are utilized across various machine-learning models. Hyperparameters are estimated to minimize mispricing across government bonds with different maturities. The Stochastic Gradient Descent algorithm outperforms other models studied, followed by XGBoost. In addition, half of the models outperform the Random Walk benchmark. The feature importance analysis reveals that US bond yields, labor market conditions, banks' balance sheets, and manufacturing-related factors significantly drive yield curve predictions. This study is one of the few that uses such a broad array of macroeconomic variables to examine Canadian macro-level outcomes. It provides valuable insights for policymakers and market participants, with its feature importance analysis highlighting key drivers of the yield curve.
This paper studies the effect of work-from-home on employee performance and teamwork in the context of the sell-side analyst industry. Using a difference-in-difference setting utilizing the exogenous adoption of work-from-home following the start of the Coronavirus pandemic, I find that the performance of analysts working individually and in teams is negatively affected following the adoption of work-from-home. Moreover, contrary to prior literature documenting a positive effect of team size on team performance, I find that following the adoption of pandemic-induced work-from-home, team size becomes negatively related to team performance, supporting the hypothesis that in the work-from-home settings, as team size grows, team coordination becomes more costly. I also find a negative impact of work-from-home on the performances of teams led by female analysts. These findings are in line with the findings of prior literature which document an asymmetric negative impact of work-from-home and remote work on female employees and attribute this effect to the unequal burden of household and caregiving responsibilities on female employees.
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Monetary Policy and Firm Innovation
I addressed a gap in the literature by analyzing the interplay between monetary policy and firm-level innovation in an event study and panel data local projection framework. The study reveals that equity prices and investments of highly innovative firms demonstrate lower sensitivity to monetary shocks, suggesting that firm innovation can act as a stabilizing factor in the transmission of monetary policy.