Multi-factor Sentiment Analysis for Gauging Investors Fear Open Access
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The Chicago Board Options Exchange Volatility Index (VIX) is widely used to gauge investor fear and measures the “insurance premiums” of the stock market. Traditionally, VIX prediction was done using time-series analysis models (e.g., GARCH), and some attempt was made to predict it using sentiment analysis approach. However, traditional sentiment analysis is focused on authors’ sentiment (sentiment expressed in news authors’ word choices), and this Praxis will demonstrate that adding other factors (e.g., similarity, readability) into the traditional authors’ sentiment model will improve VIX prediction results. This Praxis research leverages natural language processing (NLP) and machine learning (ML) techniques to build a VIX prediction model.