Published in Working Paper, 2024
The fundamental problem of causal inference lies in the absence of counterfactual. In recent advances, researchers modelling the entire data generating process (DGP) to impute the missing counterfactual explicitly. This paper expands the interactive fixed effect (IFE) model by instrumenting covariates into factor loadings adding additional robustness.
Published in Working Paper, 2023
In recent years, unanticipated climate change risks have propelled green portfolios to achieve superior returns compared to their brown counterparts. Paradoxically, both empirical and theoretical evidence underscore a perplexing phenomenon: brown firms, characterized by higher carbon emissions or lower ESG (Environmental, Social, and Governance) scores, tend to yield greater expected stock returns.
Published in Financial Innovation, 2023
In the field of empirical asset pricing, the challenges of high dimensionality, non-linear relationships, and interaction effects have led to the increasing popularity of machine learning (ML) methods. This study investigates the performance of ML methods when predicting different measures of stock returns from various factor models and investigates the feature importance and interaction effects among firm-specific variables and macroeconomic factors in this context.
Published in work in progress, 2022
The study analyzes a comprehensive dataset across various companies, industries, and countries, employing rigorous econometric techniques. The findings reveal a statistically significant and positive relationship between lagged ESG scores and financial performance, persisting over time and across diverse contexts.