Using Artificial Intelligence for Stock Price Prediction and Profitable Trading

Abstract

Investing in the stock market is generally considered a safe and reliable way to protect and grow your savings over the long term. However, stock investments are risky and investing in the wrong companies can lead to financial loss. This research paper aims to see how the emerging field of Artificial Intelligence (AI) can be used to predict a stock’s future movement and capitalize on it by anticipating the movement and automatically buying or selling the stock, in an attempt to safely generate profit while minimizing risk. In this paper, a Multi-Layer Perceptron regressor model was trained to predict a stock’s future price using the stock’s previous prices, two industry competitors’ previous stock prices, and previous daily crude oil prices. The model was evaluated based on its average accuracy to the actual price in percent, and it was found that the model had an average 1.7% error on AAPL (Apple Inc) stock. An automated trading bot was then created and used the model’s daily output to perform a simulated backtest, where it was given a simulated $100,000 portfolio. Its performance was measured in multiple different trading scenarios. It was found that the model can reliably outperform stocks in fairly flat or downward market conditions and still profit on upward-moving stocks, but can still end up failing to profit if the underlying stock it was given drops in price significantly over the testing period.