Working Paper

Abstract: I develop a quantitative dynamic general equilibrium model to explore how climate regulatory risk is reflected in cross-sectional asset pricing. The representative household has preferences for low carbon. Greener environmental preference makes the market price of climate policy risk more positive conditional on a low elasticity of substitution between green and brown capital. The quantitative implications of the model can rationalize the recent empirical evidence on the positive price of carbon transition risk and carbon intensity risk. It also highlights the heterogeneity of carbon premium over energy structure dimension. Finally, using textual analysis to measure transition risk from 10K filings, the paper shows that lower transition risk-exposed firms carry a 8.4% risk premium. Consistent with the model, the price of transition risk tends to be negative.

Presented at SoFiE 2023 Climate Finance (NYU Shanghai), Imperial College Business School

Abstract: We propose a new measure of firm-level climate regulatory exposure based on 10-K filings. Using the 2016 Trump election as an exogenous shock to perceived climate regulatory risks, we identify a positive effect on stock returns for firms with higher climate regulatory exposures; they experience economically and statistically significant higher cumulative returns post-election. In the year following the election, firms with higher climate regulatory exposure experience higher carbon emissions and lower investor attention. Both findings indicate that, post-election, investors become less concerned with climate regulatory risks. Results are robust to physical climate, trade, tax and oil price exposures. 

Presented at 5th Annual Boca-ECGI* Conference 2024, University of Liverpool Finance Seminar, University of Macau, Economic Seminar

Abstract: We investigate the relationship between biodiversity and municipal finance. At the county level, municipalities experience higher borrowing costs due to increased biodiversity exposure, significantly affecting both intensive and extensive margins. A one-standard-deviation increase in biodiversity exposure raises municipal bond yields by 42.4 (63.3) basis points (bps) dependent on the ecological and economic controls, while the extensive margin of bond issuance decreases by 0.61\%. Our analysis highlights pricing heterogeneity based on regional species protection awareness finding that species awareness facilitates to mitigate the financial cost. This economic mechanism is driven by the effect of biodiversity conservation over real estate market, reflected in enriched biodiversity and lower credit ratings. Utilizing the federal Endangered Species Act (ESA) as an exogenous shock, we causally identify that the observed pricing pattern results from stringent species protection policies.

(Presented at AAEA, SWUFE ESG Conference, HKUST(GZ) FinTech Workshop)

Abstract: We develop a unified machine learning framework to extract the specific topic risk from the SEC 10K filings and finish its inference with three-stage regressions. We implement both supervised and unsupervised learning algorithms in Natural Language Processing to accomplish this task.  With application into climate topic, the proposed risk exposure estimator is not only testable within Fama-MacBeth framework by compressing high-dimensional text information, but also has statistical sufficiency property. Empirically, this technique could generate a monthly abnormal return on climate-topic risk from 1.2% to 2.1%, depending on the specification of the benchmark model. The pricing finding is robust at the firm-level for Fama-MacBeth regression. Additionally, we find that the sufficient text-implied risk exposure is consistent with recent empirical literature in Engle et al.(2020), implying the rationality of our method. Moreover, a higher causal stock market reaction over Paris Agreement indicates a consistent finding as Bolton & Kacperczyk(2021).  

Abstract: We conduct a quantitative analysis of the textual content for more than 60 million Chinese court judgments. Focusing on the firm-involved cases, we find that Chinese judicial outcomes have specific characteristics with regards to State-Owned Enterprises (SOEs). In contrast to legal cases initiated by Private-Owned Enterprises (POE), the winning probabilities of SOE-initiated legal cases are 2.9% higher. The judicial slant is of greater economic magnitude when governments initiate cases (27.1%). The results still hold when comparing the local against non-local cases or SOE against POE retrial cases. Finally, we use an event study around 2014 ( Fourth Plenary Session of the 18th Central Committee of the Communist Party of China) and identify that state ownership consolidation increases the judicial slant.

Presented at USC Marshall (China Economy Workshop) 2021, Imperial College Business School