NLP-Driven Sentiment Analysis of Earnings Calls and Its Impact on Stock Volatility

Abstract

Among the most significant mediums where companies report to investors, analysts and regulating bodies are the earnings calls where companies report their performance in the financial realm, their strategy and future prospects. Unlike regulatory filings which are usually stagnant and very formal, earnings calls are two-way communications, and they contain undertones, language choices, and feelings that might cause serious impacts to the market perception. Recent advances in the field of natural language processing (NLP) have assisted researchers to quantitatively measure such sentiments, offering predictive data as to how the markets would react. The paper will analyze the extent to which the sentiment as it is determined by the earnings call transcripts can predict and explain short-term stock volatility. We train both lexicon-based and transformer-based deep learning models, including FinBERT, to learn sentiment dimensions, including positivity, negativity, uncertainty, and litigious tone. Volatility is measured by realized volatility realized through intraday prices per event of earnings calls. Regression based models and machine learning classifiers are then employed to find predictive relationships. The findings point to the fact that the more sophisticated NLP models are more effective than the methods, which rely on dictionaries, and that uncertainty and negative tones are very closely connected with the volatility. The work has resulted in the area of financial text analytics as it has served to address the gaps in studying the interaction between the analysis of narrative disclosure and the model of market risk and its practical and theoretical implications on the investors, analysts, and policymakers.