Varun Sharma

Finance PhD Student, London Business School (LBS)

Research areas: Climate Finance; Political Economy; Financial Intermediation; Insurance

Co-author: S Lakshmi Naaraayanan (LBS) and Kunal Sachdeva (Rice University)

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 and presentations: ADBI-JBF-SMU Joint Conference on Green and Ethical Finance; Political Economy of Finance Conference 2020 (Chicago Booth)^; 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; London Business School; Jones Graduate School of Business (Rice University)^

Media Coverage: ProMarket (Stigler Center at the University of Chicago Booth School of Business)

Co-author: Ishita Sen (Harvard Business School)

Abstract: Exploiting position-level heterogeneity in regulatory incentives to misreport and novel data on regulators, we document that U.S. life insurers inflate the values of corporate bonds using internal models. We estimate an additional $9-$18 billion decline in regulatory capital during the 2008 crisis, i.e., a 30% greater decline than what was reported. Supervision helps dissuade misreporting, but only when close pricing benchmarks exist. Insurers, in response, strategically shift asset selection toward bonds where price verification is harder, and corner small bonds. Our findings have consequences for assessing the fragility of financial institutions and for understanding the price discovery of corporate bonds.

Conferences and presentations: WFA-CFAR Conference (Washington University in St. Louis)*; AFFECT^; NBER Insurance Workshop^; TADC'; AFA 2020 (Poster Session); Bank of England*; London Business School; Harvard Business School^; Federal Reserve Bank of Boston*; UW Madison^; Duke^; HEC Paris^;

Co-author: Evgenii Gorbatikov (LBS), Laurence van Lent (Frankfurt), Narayan Naik (LBS), and Ahmed Tahoun (LBS)

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.

Conferences and presentations: London Business School^; Duke^; UNC Kenan-Flagler Business School^

* Scheduled

^ Presentations by co-author

' Cancelled due to COVID-19