Network Environment and Financial Risk Using Machine Learning and Sentiment Analysis

Abstract

ABSTRACT Under the network environment, the trading volume and asset price of a financial commodity or instrument are affected by various complicated factors. Machine learning and sentiment analysis provide powerful tools to collect a great deal of data from the website and retrieve useful information for effectively forecasting financial risk of associated companies. This article studies trading volume and asset price risk when sentimental financial information data are available using both sentiment analysis and popular machine learning approaches: artificial neural network (ANN) and support vector machine (SVM). Nonlinear GARCH-based mining models are developed by integrating GARCH (generalized autoregressive conditional heteroskedasticity) theory and ANN and SVM. Empirical studies in the U.S. stock market show that the proposed approach achieves favorable forecast performances. GARCH-based SVM outperforms GARCH-based ANN for volatility forecast, whereas GARCH-based ANN achieves a better forecast result for the volatility trend. Results also indicate a strong correlation between information sentiment and both trading volume and asset price volatility.