Stock Market Analysis using Machine Learning Algorithms

Document Type


Lead Author Type

CIS Masters Student


Dr. Jared Moore; moorejar@gvsu.edu

Embargo Period



The stock market moves a large amount of wealth between individuals and institutions daily. Forty million transactions, involving 10 billion shares, are exchanged in the US market alone everyday. In the past twenty years, computers have dominated transaction volume, processing information at a rate inconceivable to human traders. Machine learning (ML) algorithms have gained traction for their ability to digest data and formulate predictions in many domains. This project investigates three ML algorithms as applied to publicly available stock market data to predict future prices. The goal of this project is to observe how these algorithms perform on a dataset consisting of historical prices for one company and their accuracy was compared against each other. The closing price of the data for each day was selected as the target variable. The algorithms predicted the close price for the next thirty days based on the input data. A “good” algorithm would predict closing prices close to the actual values. Results show that a Random Forest outperforms Neural Networks and Linear Regression for our dataset. While initially promising, during the project I noticed that the models do not perform as well as would be expected from ML. Investigating further, the dataset was gathered from freely available sources on the Internet. After speaking with veteran stock traders, it appears that publicly available data is not enough to produce an effective stock trading model. Going forward, I would need to acquire data from paid sources as it includes features such as climate models, public sentiment analysis, and forward-looking business climate information. Still, the project was successful in applying ML to stock market price prediction, although it is not a fully functioning trading algorithm.

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