Proposal and Evaluation of a Nikkei Average VI Prediction Model Using Newspaper and Bulletin Board Data

In this paper, we propose a novel model that combines two types of textual data, newspaper articles and stockbulletin board data, to predict the rise of the Volatility Index (VI). VI is a crucial indicator of market risk and iswidely used to forecast future price fluctuations. Often referred to as the fear index, VI is closely linked to socialconditions and investor sentiment. Previous studies have primarily focused on either mass media or social mediawhen predicting financial indicators. Mass media provides valuable information about social events that influence theeconomy and financial markets. In contrast, social media reflects the interests, opinions, and sentiments of investorsand contributors, making it an important data source. To comprehensively capture both social conditions and investorsentiment, this study utilizes newspaper articles and stock message board posts to predict VI. Specifically, featurevectors are extracted from these textual data and combined with financial time series data to build a machine learningbased prediction model. The proposed method was evaluated by comparing its prediction accuracy with baselinemodels and conducting trading simulations. The results demonstrate that the proposed model outperforms existingmethods in terms of prediction accuracy and shows the potential to generate profits.