Predicting the DJIA with News Headlines and Historic Data Using Hybrid Genetic Algorithm/Support Vector Regression and BERT

Abstract

One important application of Artificial Intelligence (AI) is forecasting stock price in the stock market, as such knowledge is highly useful for investors. We first examine two state-of-the-art AI techniques, hybrid genetic algorithm/support vector regression and bidirectional encoder representations from transformers (BERT). After that, we proposed a new AI model that uses hybrid genetic algorithm/support vector regression and BERT to predict daily closes in the Dow Jones Industrial Average. We found that there is an up to 36.5% performance improvement with root-mean-squared-errors using headline data compared to without headline data based on models we have tested, although further analysis may reveal more significant improvements. The code and data used in our model can be found at: https://github.com/bcwarner/djia-gasvr-bert.