Artificial Intelligence in the Stock Market: Predicting Prices

This research project focuses on stock price prediction through A.I. models as well as machine learning algorithms to maximize profit potential, improve investments, and eliminate risk.

This research project focuses on stock price prediction through A.I. models as well as machine learning algorithms to maximize profit potential, improve investments, and eliminate risk. Essentially, this project will demonstrate the modern implementation of A.I. in predicting the stock market. Potentially lucrative company stocks and shares have attracted investors as well as general interest in the stock market for decades, leading more people to try to predict the rise or fall of market prices. However, industry volatility and the seemingly unpredictable nature of the stock market have led many buyers to invest impulsively, sell their shares at the wrong time, or purchase stock from the wrong company. To combat these problems, we trained and tested A.I. models on our collected, classified data in order to generate accurate predictions. These models achieved average prediction errors of 0.12% for the stock prices of Amazon, 0.13% for the stock prices of Google, and 0.07% for Microsoft’s stock prices on the testing datasets.

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