Dispelling ESG Investing Risk Misconceptions

This replication package provides the synthetic data, code, and instructions required to reproduce the main empirical results of the above-mentioned research paper. The package is designed to facilitate transparency and enable independent verification of all statistical findings reported in the study.
The empirical analysis is organized into three main blocks:
1 – Computation of Risk Metrics (R)
Risk measures are computed from daily fund return data obtained from data sources. The daily returns are first processed to construct fund-level risk metrics. These measures are then aggregated and transformed to a semester frequency.
All data processing and risk metric construction for this step are implemented in R. The corresponding scripts and intermediate datasets are provided in the R replication package.
2 – Data Merging and Empirical Analysis (Stata)
The semester-level dataset constructed in Step 1 is merged with fund-level information from Morningstar funds.
The main empirical analysis is conducted in Stata using the merged dataset. All Stata do-files required to replicate the empirical results are included in the Stata replication package.
3 – Random Forest Model Analysis(R)
A Random Forest model is estimated in R using the merged and cleaned dataset.
All scripts necessary to replicate the machine learning analysis are included in the R replication package.