Portfolio underdiversification is one of the most costly losses accumulated over a household’s life cycle. We provide new evidence on the impact of financial inclusion services on households’ portfolio choice and investment efficiency using 2015, 2017, and 2019 survey data for Chinese households. We hypothesize that higher financial inclusion penetration encourages households to participate in the financial market, leading to better portfolio diversification and investment efficiency. The results of the baseline model are consistent with our proposed hypothesis that higher accessibility to financial inclusion encourages households to invest in risky assets and increases investment efficiency. We further estimate a dynamic double machine learning model to quantitatively investigate the non-linear causal effects and track the dynamic change of those effects over time. We observe that the marginal effect increases over time, and those effects are more pronounced among low-asset, less-educated households and those located in non-rural areas, except for investment efficiency for high-asset households.

How does executive authority affect household behavior? I develop a model in which the executive branch of the government is partially constrained. These constraints credibly limit intervention under normal conditions but can be overridden when a sufficiently large fraction of the population is in distress. Households anticipate this and strategically coordinate their financial risks through public markets, creating collective distress that compels government bailouts. Weaker constraints lower the threshold for intervention, making implicit guarantees more likely. The model explains why implicit guarantees are prevalent in China and predicts that such guarantees may discontinuously emerge elsewhere as executive constraints gradually weaken.

This paper investigates whether increased female representation on corporate boards affects firms’ bond financing costs. Exploiting the 2017 Big Three’s campaigns as a plausibly exogenous shock, we document that firms experiencing larger increases in female board representation, induced by the campaigns, experience significant reductions in bond yield spreads and improvements in credit ratings. We identify reduced leverage and enhanced workplace environment as key mechanisms, and show that the effects are stronger among firms with greater tail risk and information asymmetry. An alternative identification strategy based on California’s SB 826 regulatory mandate yields consistent results. Our findings suggest that board gender diversity enhances governance in ways valued by credit markets.

This paper reconciles the debates on carbon return estimation by introducing the concept of equity duration. Our findings reveal that equity duration effectively captures the multifaceted effects of carbon transition risks. Regardless of whether carbon transition risks are measured by emission level or emission intensity, brown firms earn lower returns than green firms when the equity duration is long due to discount rate channel. This relationship reverses for short-duration firms conditional on the near-term cash flow. Our analysis underscores the pivotal role of carbon transitions' multifaceted effects on cash flow structures in understanding the pricing of carbon emissions.

This study reconsiders the timing of the equity risk factors by using the flexible neural networks specified for image recognition to determine the timing weights. The performance of each factor is visualized to be standardized price and volatility charts and `learned' by flexible image recognition methods with timing weights as outputs. The performance of all groups of factors can be significantly improved by using these ``deep learning--based'' timing weights. In addition, visualizing the volatility of factors and using deep learning methods to predict volatility can significantly improve the performance of the volatility-managed portfolio for most categories of factors. Our further investigation reveals that the timing success of our method hinges on its ability in identifying ex ante regime switches such as jumps and crashes of the factors and its predictability on future macroeconomic risk.

Reputation is widely regarded as a stabilizing factor in financial institutions, reducing capital constraints and enhancing firm resilience. However, in the insurance industry, where capital requirements are shaped by solvency regulations and policyholder behavior, the effects of reputation on capital management remain unclear. This paper examines the unintended consequences of reputation in insurance asset-liability management, focusing on its impact on capital allocation. Using a novel reputation risk measure based on large language models (LLMs) and actuarial models, we show that reputation shifts influence surrender rates, altering capital requirements. While higher reputation reduces surrender risk, it increases capital demand for investment-oriented insurance products, whereas protection products remain largely unaffected. These findings challenge the conventional wisdom that reputation always eases capital constraints, highlighting the need for insurers to integrate reputation management with capital planning to avoid unintended capital strain.

This study examines the relationship between industry litigation risk and corporate misconduct using China's A-share listed companies’ data from 2007 to 2022. The findings indicate a significant and negative association, where companies in industries with higher median litigation amounts relative to their assets exhibit reduced incidents of misconduct. This suggests that businesses in high-risk litigation sectors may adopt more cautious practices to mitigate legal challenges and protect their reputations. The robustness of these findings is confirmed through a variety of tests, including a quasi-experimental setting of the chief judges rotation implemented in 2008. Furthermore, the study finds that external monitors including financial analysts’ site visits and local law firms moderate the negative relationship between litigation risk and misconduct. We further show that legal enforcement and moral capital are the two channels through which industry litigation risk impacts corporate misconduct. Our findings underscore the role of litigation risk in shaping peer firms' behavior.

Using a unique dataset from China spanning 2005 to 2023, we investigate how superstitious beliefs influence mutual fund managers’ risk-taking behavior and how this influence evolves over their careers. We find a significant 6.82% reduction in risk-taking during managers’ zodiac years, traditionally considered unlucky in Chinese culture. This effect is particularly pronounced among less experienced managers, those without financial education backgrounds, and those with lower management skills. The impact also intensifies during periods of high market volatility. Our findings challenge the traditional dichotomy between retail and professional investors, showing that even professional fund managers can be influenced by irrational beliefs early in their careers. However, the diminishing effect of superstition with experience and expertise suggests a gradual transition towards more rational decision-making. Our results provide insights into the process by which financial professionals evolve from exhibiting behavior akin to retail investors to becoming the rational actors often assumed in financial theory.

Estimates of canonical affine term structure model parameters are highly sensitive to sample periods. For example, depending on whether the sample starts in 1961 or 1981, the 5-5 forward risk-neutral rate for September 1981 differs by 4.6 percentage points or 98% of the latter. The estimated response of this rate to high-frequency monetary policy shocks differs by a factor of three, even within a fixed sample for the monetary policy transmission regression. We suggest that a shifting endpoint model can mitigate these issues. Additionally, we provide new estimates of the effects of monetary policy shocks on long-term risk-neutral rates.

This paper proposes the concepts of ESG information flows and a predictable framework of ESG flows based on AR process, and studies how ESG information flows are incorporated into and affect a dynamic portfolio with transaction costs. Two methods, called the ESG factor model and the ESG preference model, are considered to embed ESG information flows into a dynamic mean-variance model. The dynamic optimal portfolio can be expressed as a traditional optimal portfolio without ESG information and a dynamic ESG preference portfolio, and the impact of ESG information on optimal trading is explicitly analyzed. The rich numerical results show that ESG information can improve the out-of-sample performance, and ESG preference portfolio has the best out-of-sample performance including the net returns, Sharpe ratio and cumulative return of portfolios, and contribute to reducing risk and transaction costs. Our dynamic trading strategy provides valuable insights for sustainable investment both in theory and practice.