Abstract: We study the real effects of environmental activist investing. Using plant-level data in a quasi-experimental setting, we find that firms targeted by environmental activist investors reduce their toxic releases, greenhouse-gas emissions, and cancer-causing pollution through preventative efforts. Improvements in air quality within a one-mile of targeted plants suggest potentially important externalities to local economies. We provide evidence supporting the external validity of environmental activism while also ruling out reporting biases, forms of selection, and other alternative hypotheses. Overall, our study suggests that engagements are an effective tool for long-term shareholders to address climate change risks.
Conferences: EFA*; Econometric Society’s World Congress*; FMA Annual Meeting*; UBC Winter Finance Conference^; Texas Finance Festival' ; Finance on Cloud; Asia-Pacific Corporate Finance Online Workshop; World Symposium on Investment Research'; Annual Mid-Atlantic Research Conference (MARC)' ; FMA Europe'; European Winter Finance Conference; Wellington Finance Summit;
Co-author: Ishita Sen (Harvard Business School)
Abstract: Using corporate bond holdings of U.S. life insurers, we show that life insurers used internal models to over-report the value of a large fraction of corporate bond assets during the financial crisis. Reported credit spreads of bonds valued using internal models were substantially lower by 220 bps, as compared to bonds that are otherwise similar but valued using external sources. Misreporting is higher for bonds that are likely to be impaired and negatively affect regulatory ratios and for insurers that are constrained by regulatory capital. We document significant heterogeneity in misreporting across U.S. states and show that it correlates with the strictness of the state regulator during the crisis. Consequently, we show that there is greater misreporting in positions that are held by fewer insurers, as concentrated holdings helps to minimize regulatory scrutiny. Our findings have implications for the ex-ante portfolio choice of insurers, which impacts the micro-structure of a segment of the corporate bond market, and for properly assessing the fragility of financial institutions in bad times.
Conferences: NBER Insurance Workshop^; TADC'; AFA 2020 (Poster Session);
Abstract: The effect of economy-wide political uncertainty on stock market returns is well documented in the literature. However, in order to take a stand on the relation between firm-specific political risk and the cross-section of stock returns, we need a measure independent of those returns. Using a machine-learning based firm-specific measure of political risk, we show that political risk is priced in the cross-section of stock returns. On average, a one standard deviation increase in a firm's political risk is associated with a 0.5% to 1.0% increase in their annual returns. Using a related non-price measure that captures the mean of a firm's political-shocks, we disentangle whether the asset pricing implications of political risk stem from news about the discount rate or future cash flows. We further show that political risk is priced only for firms that do not actively manage political risk. Finally, using a natural language processing (NLP) enabled measure of risk associated with political topics, we examine how (and to what extent) sub-components of political risk are priced.
^ Presentations by co-author
' Cancelled due to COVID-19