Abstract   Common ownership has increased substantially in recent decades. This paper investigates whether common owners engage in greenwashing and how this affects corporate carbon emissions. Our baseline and difference-indifferences tests show a positive relationship between corporate carbon emissions and common ownership. Common ownership is also positively correlated with firms' revenue, employment, as well as general and green innovation, which indicates technological spillovers via common ownership. However, commonly owned firms' higher green innovation does not result in higher carbon efficiency. In addition, common owners tend to vote against pro-environmental proposals both before and after becoming signatories of the Principles for Responsible Investment (PRI), which suggests a more pronounced greenwashing tendency among common owners than other types of institutional investors. Our tests indicate that common owners may increase portfolio firms' carbon emissions levels. This arises from common owners prioritizing profit maximization over emissions reductions and facilitating technological spillovers that could increase production.

Abstract   This paper investigates ESG risk disclosures by mutual funds when investors learn from their disclosures in addition to past performance. Using a novel natural language processing method to identify ESG-risk disclosure in mutual fund prospectuses, I find that funds with higher ESG risk are more likely to disclose ESG risk than equivalent funds with lower ESG risk. To understand this, I develop a theoretical model which illustrates how ESG risk disclosure reduces investor reliance on past returns, thereby moderating flow performance sensitivity and smoothing fund fee income. I also show that the key predictions of the model hold in practice when I empirically test the model using U.S. mutual fund data. My results suggest that ESG risk disclosure can be used for risk management purposes to mitigate the adverse effects of high ESG risk exposure.

Abstract   We argue that highly complex funds’ prospectuses limit the ability of investors to effectively use available information and make informed investment decisions. Measuring textual complexity with the Fog Index, our evidence suggests that low-quality funds manipulate their prospectuses, making them more complex, possibly targeting less sophisticated investors. These investors, in turn, use a less sophisticated asset pricing model to evaluate fund performance, react more aggressively to past winners, and are more likely to be attracted by funds with high marketing costs. Our results suggest that funds with low-complexity prospectuses are more trustworthy, and that funds with high-complexity prospectuses are possibly subject to more severe agency issues.

Abstract   In this paper, I examine the impact of “correlation neglect” in a financial market, where behavioural traders neglect the correlation between signal errors. I develop a rational expectation equilibrium model including both behavioural and rational traders. I find that the impact of behavioural traders on market quality, measured by liquidity and mispricing risk, depends on whether the information is costly or not. If information acquisition is free of charge and correlation between signal errors is relatively low, mispricing risk decreases in the mass of behavioural traders; but when correlation is large enough, mispricing risk is U-shaped. Conversely, when information acquisition is costly, market liquidity deteriorates and mispricing risk increases in the mass of behavioural traders given that their mass is not too large to drive all informed rational traders out of the market; but market quality can improve afterwards after informed rational traders are entirely crowded out, depending on the correlation and the mass of behavioural traders.

Information Diffusion and Volatility Spillover: Evidence from High-Frequency News

• with Mihai Cucuringu and Chao Zhang

Comprehensive Analysis of Pricing Climate Risk: A United Textual Approach 

• with Z. Tan and Yi Zhang

Decrypting the Financial and Ecological Real Effects of Biodiversity Conservation (with L. Chen and Y. Zhang)

• with L. Chen and Y. Zhang