Machine Learning in Finance: Using Neural Networks to predict stock returns
Format
Oral Presentation
Faculty Mentor Name
Vusal Eminli
Faculty Mentor Department
Eberhardt School of Business
Abstract/Artist Statement
There have been multiple attempts to predict stock returns using machine learning, which have largely used historical time series data on share prices to make these predictions. Those attempts create networks which only work on one firm's data, and cannot be applied generally. This study uses a neural network to predict stock returns based on financial and economic data. The method that is employed here predicts whether a given stock will beat the S&P 500 index over a future time period. This method has reached prediction accuracy of 64.5%. A method which makes consistently accurate predictions helps to identify additional factors that determine a firm’s value beyond what is generally accepted in the literature.
Location
University of the Pacific, 3601 Pacific Ave., Stockton, CA 95211
Start Date
24-4-2021 10:15 AM
End Date
24-4-2021 10:30 AM
Machine Learning in Finance: Using Neural Networks to predict stock returns
University of the Pacific, 3601 Pacific Ave., Stockton, CA 95211
There have been multiple attempts to predict stock returns using machine learning, which have largely used historical time series data on share prices to make these predictions. Those attempts create networks which only work on one firm's data, and cannot be applied generally. This study uses a neural network to predict stock returns based on financial and economic data. The method that is employed here predicts whether a given stock will beat the S&P 500 index over a future time period. This method has reached prediction accuracy of 64.5%. A method which makes consistently accurate predictions helps to identify additional factors that determine a firm’s value beyond what is generally accepted in the literature.