Beyond the Numbers: Causal Effects of Financial Report Sentiment on Bank Profitability

TL;DR

This paper uses advanced causal machine learning techniques and a novel sentiment analysis approach to uncover how financial report sentiment causally influences bank profitability, accounting for heterogeneity and non-linear effects.

Contribution

It introduces a causal forest methodology combined with FinancialBERT sentiment scores to analyze the causal impact of financial report sentiment on bank profitability, addressing heterogeneity and non-linearities.

Findings

Sentiment causally affects profitability metrics.

Balance sheet variables significantly influence profitability.

Heterogeneous effects depend on asset composition and leverage.

Abstract

This study establishes the causal effects of market sentiment on firm profitability, moving beyond traditional correlational analyses. It leverages a causal forest machine learning methodology to control for numerous confounding variables, enabling systematic analysis of heterogeneity and non-linearities often overlooked. A key innovation is the use of a pre-trained FinancialBERT to generate sentiment scores from quarterly reports, which are then treated as causal interventions impacting profitability dynamics like returns and volatilities. Utilizing a comprehensive dataset from NEPSE, NRB, and individual financial institutions, the research employs SHAP analysis to identify influential profit predictors. A two-pronged causal analysis further explores how sentiment's impact is conditioned by Loan Portfolio/Asset Composition and Balance Sheet Strength/Leverage. Average Treatment Effect analyses, combined with SHAP insights, reveal statistically significant causal associations between certain balance sheet and expense management variables and profitability. This advanced causal machine learning framework significantly extends existing literature, providing a more robust understanding of how financial sentiment truly impacts firm performance.