Financial Risk Forecast Using Machine Learning and Sentiment Analysis

Abstract

There is a widespread need for effective forecasting of financial risk using readily available financial measures, but the complicated environment facing financial practitioners and business institutions makes this very challenging. The concept of financial volatility, a required parameter for pricing many kinds of financial assets and derivatives, is critical, because it is widely expected that financial volatility implies financial risk. Therefore, accurate prediction of financial volatility is extremely important. Efficient prediction of financial volatility has been an extremely difficult task, but we can now offer a scalable and customizable mathematical model to achieve this goal, employing two approaches to forecast the volatility using financial information available online. First, we carry out a comparative study between two different machine-learning techniques — artificial neural networks (ANN) and support vector machines (SVM) — to forecast trading volume volatility. Second, we utilize semantic techniques to probe correlations between information sentiment and asset price volatility.