Beyond the Numbers: Causal Effects of Financial Report Sentiment on Bank Profitability
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866912915112591360 |
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| author | Neupane, Krishna Sapkota, Prem Prajapati, Ujjwal |
| author_facet | Neupane, Krishna Sapkota, Prem Prajapati, Ujjwal |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_17851 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Beyond the Numbers: Causal Effects of Financial Report Sentiment on Bank Profitability Neupane, Krishna Sapkota, Prem Prajapati, Ujjwal Computational Finance Statistical Finance 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. |
| title | Beyond the Numbers: Causal Effects of Financial Report Sentiment on Bank Profitability |
| topic | Computational Finance Statistical Finance |
| url | https://arxiv.org/abs/2602.17851 |