Deep Reinforcement Learning for Stock Behavior Prediction

Abstract

Stock market forecasting has long been both the most sought-after and the most dreaded occupation due to the high stakes involved. Since investors could lose a lot of money if they are wrong, a precise forecast is required. Once performed by trained stock behaviourists, this task is now as easy as clicking a button. Stock prediction is one of the many human abilities that technology has supplanted. This work proposes a combination of machine learning algorithms that, when trained on historical stock data, can identify patterns in market behaviour and make more accurate predictions about stock behaviour than a human expert with a wealth of knowledge in the field. In order to forecast even the most erratic stock price spikes, the suggested system employs Reinforcement Learning algorithms like DQN, Double DQN, and Duelling Double DQN. These algorithms have independently demonstrated to be the most effective, efficient, and productive in their respective fields. An investor in the stock market can relax now that