Mong Shan Ee

We investigate how judicially imposed restrictions on managerial labor mobility shape firms' real operating policies, using inventory policy as the empirical setting. Specifically, we exploit the staggered judicial adoption of the Inevitable Disclosure Doctrine (IDD), a legal doctrine that restricts managers' mobility across competing firms, as a quasi‐natural experiment. Our analysis reveals that the IDD adoption leads to a significant reduction in firms' inventory holdings. This effect is more pronounced among managers with greater ability and longer tenure, consistent with the view that mobility restrictions encourage managers with stronger long‐term career incentives to adopt leaner inventory policies. We further show that the decline in inventory holdings moves firms closer to their optimal inventory levels, improves inventory efficiency, and enhances subsequent firm performance. Overall, our findings suggest that legal restrictions on labor mobility have important real effects on corporate operating policies by reshaping managerial career incentives.

This paper investigates the impact of extreme climate shocks on the conduct of monetary policy in China. Leveraging annual prefecture-level city panel data and employing local projection methods, we find that such shocks consistently suppress economic growth and exert upward pressure on inflation. To uncover the underlying transmission mechanisms, we explore both supply- and demand-side channels, including short-term supply disruptions, persistent increases in energy prices, and long-term demand suppression. These dynamics suggest that extreme climate events generate acute policy trade-offs for central banks. Our further analyses reveal significant state-dependent in monetary policy responses: during expansions, climate shocks amplify deviations from inflation targets, whereas in recessions, output gap deviations dominate but with lower impacts. Stability phases show minimal policy deviations. Among various climate events, extreme rainfall causes the largest policy deviations. Moreover, we find that a flexible inflation targeting regime exhibits the greatest resilience, demonstrating the smallest climate-induced deviations across monetary policy frameworks. These findings underscore the critical importance of explicitly incorporating climate-related macroeconomic risks into the formulation and implementation of monetary policy.

This study, partly motivated by the re-emergence of Trump in the U.S., the increase in tariffs, and the role of labor utilization in corporate finance and accounting, examines the impact of import competition on corporate labor investment efficiency and labor reallocation. Regression analyses using a sample of 22,311 firm-year observations across 28 years, reveals that, contrary to common assumptions, import competition can be a powerful catalyst for positive change in corporate labor investment. By forcing a Darwinian selection process and mitigating agency problems, import competition drives firms to correct labor inefficiencies, leading to optimal hiring patterns and a more efficient reallocation of labor across industries. This effect is strongest in sectors with lower barriers to entry and for companies with "soft" competitive strategies.

Data are increasingly recognised as critical assets, yet their absence from financial reports limits research on their impact. Exploiting the staggered adoption of US Data Breach Notification (DBN) laws, we examine how these mandatory data breach disclosure regulations affect firms’ debt maturity structures by altering the ex ante information environment and anticipated costs associated with potential future breach disclosures. We find that DBN laws lead firms to rely more on short-term debt financing, particularly among those with higher credit risk, limited financial flexibility and greater information asymmetry. Further analysis reveals that DBN laws influence firms’ debt instrument choices and are associated with improved financial reporting quality, investment efficiency and cash holdings.

Understanding the relationships between cryptocurrencies is important for making informed investment decisions in this financial market. Our study utilises Bayesian networks to examine the causal interrelationships among six major cryptocurrencies: Bitcoin, Binance Coin, Ethereum, Litecoin, Ripple, and Tether. Beyond understanding the connectedness, we also investigate whether these relationships evolve over time. This understanding is crucial for developing profitable investment strategies and forecasting methods. Therefore, we introduce an approach to investigate the dynamic nature of these relationships. Our observations reveal that Tether, a stablecoin, behaves distinctly compared to mining-based cryptocurrencies and stands isolated from the others. Furthermore, our findings indicate that Bitcoin and Ethereum significantly influence the price fluctuations of the other coins, except for Tether. This highlights their key roles in the cryptocurrency ecosystem. Additionally, we conduct diagnostic analyses on constructed Bayesian networks, emphasising that cryptocurrencies generally follow the same market direction as extra evidence for interconnectedness. Moreover, our approach reveals the dynamic and evolving nature of these relationships over time, offering insights into the ever-changing dynamics of the cryptocurrency market.

Despite advances in artificial intelligence-enhanced trading methods, developing a profitable automated trading system remains challenging in the rapidly evolving cryptocurrency market. This study aims to address these challenges by developing a reinforcement learning-based automated trading system for five popular altcoins~(cryptocurrencies other than Bitcoin): Binance Coin, Ethereum, Litecoin, Ripple, and Tether. To this end, we present CausalReinforceNet, a framework framed as a decision support system. Designed as the foundational architecture of the trading system, the CausalReinforceNet framework enhances the capabilities of the reinforcement learning agent through causal analysis. Within this framework, we use Bayesian networks in the feature engineering process to identify the most relevant features with causal relationships that influence cryptocurrency price movements. Additionally, we incorporate probabilistic price direction signals from dynamic Bayesian networks to enhance our reinforcement learning agent's decision-making. Due to the high volatility of the cryptocurrency market, we design our framework to adopt a conservative approach that limits sell and buy position sizes to manage risk. We develop two agents using the CausalReinforceNet framework, each based on distinct reinforcement learning algorithms. The results indicate that our framework substantially surpasses the Buy-and-Hold benchmark strategy in profitability. Additionally, both agents generated notable returns on investment for Binance Coin and Ethereum.