Predictive Analytics in Behavioral Finance: Modeling Investor Sentiment with NLP Techniques

Abstract

Behavioral finance with predictive analytics is a developing discipline that tries to understand how psychological powers and behavioral predispositions impact the budgetary choices of investors. In this research paper, utilizing Natural Language Processing (NLP) techniques for modeling of the investor sentiment is studied, using text data from financial news, social media, blogs and financial statements. Sentiment of the investors (an important variable in behavioral finance) can cause significant volatility in the market as well as the pricing of the assets. Quantifying sentiment is complicated and subjective, though. In this paper, we present a flexible framework with sentiment analysis, topic modeling and deep learning, which explores the output of unstructured financial text data as Actionable Sentiment Scores. The research starts off by investigating the impact of behavioral finance and data science on financial analysis, with a focus on the way that non-rational factors, including emotions and biases, play a role in financial decision-making. It then goes on to explain recent advances in NLP, particularly in transformer-based models such as BERT and FinBERT and their applicability for finance-specific applications. Following a multi-source data aggregation strategy, this study extracts investor opinions and applies a sentiment classification, which is further utilized with a time series regression model to evaluate how sentiment trends affect stock price changes. However, findings from this paper also offer quantitative outcomes that show a statistically significant correlation when it comes to aggregated sentiment scores and abnormal returns in specific market segments. The experimental setup contains data from 2017 to 2023, whereas the key tools utilized are Python, HuggingFace Transformers and APIs related to finances. A hybrid model is used to model sentiment with rule-based approaches such as VADER, Loughran-McDonald lexicon and machine learning such as LSTM and BERT. This shows the added value to both theoretical and applied domains by combining behavioral theories of finance and the latest computational techniques to improve market prediction models and make better decisions. Implications for practice include the development of real-time sentiment dashboards for traders as well as risk managers.  It finally concludes by suggesting future directions of research, like multilingual sentiment analysis and building real-time prediction engines