Document Type

Poster

Location

Ellensburg

Event Website

http://digitalcommons.cwu.edu/source/

Start Date

18-5-2016

End Date

19-5-2016

Keywords

Machine Learning, Stock Prediction, Forcasting

Abstract

The purpose of this study was to compare machine learning techniques for short term stock prediction and evaluate their effectiveness. Stock value analysis is an important element of modern economies. The ability to predict future stock prices from historical price values is of tremendous interest to investors. The prediction of stock performance is still an unsolved problem with a variety of techniques being proposed. Real stock values are affected by many elements, some of which cannot be measured. In this study, we limit our analysis to stock closing prices. We use these prices to predict the future stock value using regression and machine learning methods and compare their accuracy and effectiveness over a 3 year period.

Poster Number

12

Faculty Mentor(s)

Dr. Donald Davendra

Department/Program

Computational Science

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May 18th, 12:00 AM May 19th, 12:00 AM

Applying Machine Learning to Predict Stock Value

Ellensburg

The purpose of this study was to compare machine learning techniques for short term stock prediction and evaluate their effectiveness. Stock value analysis is an important element of modern economies. The ability to predict future stock prices from historical price values is of tremendous interest to investors. The prediction of stock performance is still an unsolved problem with a variety of techniques being proposed. Real stock values are affected by many elements, some of which cannot be measured. In this study, we limit our analysis to stock closing prices. We use these prices to predict the future stock value using regression and machine learning methods and compare their accuracy and effectiveness over a 3 year period.

https://digitalcommons.cwu.edu/source/2016/cos/5