Exploring the effects of diverse historical stock price data on the accuracy of stock price prediction models

Abstract

In the recent years, a majority of Americans have moved to investing in stocks through algorithmic trading. Not only does this underline the public’s interest in the stock market, but it also shows the important role that artificial intelligence plays when it comes to predicting and trading stocks. These prediction models can benefit investors by improving their investment decisions and potentially increasing financial gains. In this research, we aimed to explore the impact of incorporating historical stock price data types, such as the opening prices, closing prices and highest prices, on the accuracy of stock price prediction models. The primary objective was to optimize the performance of our models. To carry out this research, we developed three supervised learning artificial intelligence models using Python: linear regression, neural network, and multiplicative weight update. Our models predicted the stock prices for Microsoft, Amazon, Google, Tesla, and Apple. First, the models used the opening prices for the past three days to predict the opening price on the fourth day. However, to enhance each model’s performance, we evaluated whether adding extra information, such as closing and highest prices, would be beneficial. We hypothesized that incorporating the opening, closing, and highest prices would yield the highest accuracy as it would provide the models with the most information and help them better predict patterns in the stock market. The results supported our hypothesis as the models’ average percent errors significantly decreased when they were given all three of these data types.