Exploring Investor Sentiment Patterns in Bear Markets through Behavioral Finance Lenses

Authors

  • Dhiraj Mudvari Department of Management & Business Studies Dr. K.N. Modi University Newai, Rajasthan https://orcid.org/0009-0004-6741-2592 Author
  • Dr. Kirti Depatment of Management & Business Studies Dr. K.N. Modi University Newai, Rajasthan Author

DOI:

https://doi.org/10.62647/IJITCE2025V13I3PP248-254

Keywords:

Behavioral Finance, Financial Forecasting, Investor Sentiment, Market Volatility, Sentiment Analysis, Stock Market,

Abstract

Investor attitude is an important factor in driving the behavior of the stock market, usually dwarfing conventional financial markets. Although previous study has investigated the mental side of investing, this has been concentrated mostly on the developed world and has had no real-time behavioral incorporation. This study fulfills this requirement by investigating the roles played by investor mentality, media reporting, and macroeconomic triggers in determining stock market volatility during bear markets with a special emphasis on Kathmandu. The goal is to measure the effect of sentiment on market performance based on both structured survey data and unstructured social media and news text. A hybrid methodological design is used, combining statistical modeling (Chi-Square test) and a machine learning-based sentiment classification approach utilizing a fine-tuned BERT (Bidirectional Encoder Representations from Transformers) model. Social media and financial news information are both drawn from a 2022–2025 Kaggle Stock Market Sentiment dataset, and NVivo provides thematic analysis of investor interviews. The following BERT model, coded through Python's Hugging Face Transformers package, resulted in a 9.2% improvement on accuracy compared to standard sentiment analysis models such as logistic regression and SVM, providing richer contextual knowledge of investor emotion. Empirical findings indicate a statistically significant relationship between sentiment and investment behavior (χ² = 23.250, p = 0.006), validating market change behavior. This study not only emphasizes sentiment as an important predictive variable but also proposes a scalable, real-time predictive framework for risk-sensitive investment strategy. The application of this model can improve the early identification of volatility and guide sentiment-based trading systems for investors, analysts, and policymakers in developing markets.

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Published

28-07-2025

How to Cite

Exploring Investor Sentiment Patterns in Bear Markets through Behavioral Finance Lenses. (2025). International Journal of Information Technology and Computer Engineering, 13(3), 248-254. https://doi.org/10.62647/IJITCE2025V13I3PP248-254