Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed

Convoluted Stock Market: Using Convolution Operation for Stock Market Prediction

Version 1 : Received: 15 April 2021 / Approved: 19 April 2021 / Online: 19 April 2021 (20:56:45 CEST)
Version 2 : Received: 19 April 2021 / Approved: 20 April 2021 / Online: 20 April 2021 (21:12:17 CEST)

How to cite: Alparslan, Y.; Moyer, E.; Kim, E. Convoluted Stock Market: Using Convolution Operation for Stock Market Prediction. Preprints 2021, 2021040515 (doi: 10.20944/preprints202104.0515.v2). Alparslan, Y.; Moyer, E.; Kim, E. Convoluted Stock Market: Using Convolution Operation for Stock Market Prediction. Preprints 2021, 2021040515 (doi: 10.20944/preprints202104.0515.v2).

Abstract

Many studies in the current literature annotate patterns in stock prices and use computer vision models to learn and recognize these patterns from stock price-action chart images. Additionally, current literature also use Long Short-Term Memory Networks to predict prices from continuous dollar amount data. In this study, we combine the two techniques. We annotate the consolidation breakouts for a given stock price data, and we use continuous stock price data to predict consolidation breakouts. Unlike computer vision models that look at the image of a stock price action, we explore using the convolution operation on raw dollar values to predict consolidation breakouts under a supervised learning problem setting. Unlike LSTMs that predict stock prices given continuous stock data, we use the continuous stock data to classify a given price window as breakout or not. Finally, we do a regularization study to see the effect of L1, L2, and Elastic Net regularization. We hope that combining regression and classification shed more light on stock market prediction studies.

Keywords

LSTM; convolution; regularization; stock market

Subject

MATHEMATICS & COMPUTER SCIENCE, Algebra & Number Theory

Comments (1)

Comment 1
Received: 20 April 2021
Commenter: Yigit Alparslan
Commenter's Conflict of Interests: Author
Comment: Updated the authors.
+ Respond to this comment

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 1
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.