Chris F. Hua

Date of Award


Degree Type

Honors College Thesis


Finance, Real Estate, and Business Law

First Advisor

Kimberly Goodwin, Ph.D.

Advisor Department

Finance, Real Estate, and Business Law


The concept of beating the stock market is one of the main goals of financial investors and analysts. However, seasoned analysts and investors cannot perfectly predict the movements within the stock market. Machine learning (ML) is one prediction method that has intrigued the financial industry as a means of accurately predicting the market. Though the current prediction algorithms do not grasp every change in the stock markets, the models do provide a means of looking towards stock trends as a means to hedge an investor's or fund's portfolio against market declines by looking at the artificial intelligence (AI) results. This study focused on the accuracy of True Risk Lab's machine learning model for predicting stock price trends. Each of the 11 companies selected for analysis represents significant sectors of the S&P 500, according to the Global Industry Classification Standard (GCIS). By analyzing the price trends from 2013-2018, the True Risk models performed well at forecasting the average stock price and direction for mature companies. However, the models resulted in higher errors for high-growth companies. These results infer that ML models may not adequately forecast the actual price of companies with high volatility due to unprecedented growth.