Return prediction for healthcare sector stocks by using long short-term memory algorithm

Zeyi Chen; Chengyuan Liu; Eran Wo

doi:10.1117/12.2646886

23 August 2022 Return prediction for healthcare sector stocks by using long short-term memory algorithm

Zeyi Chen, Chengyuan Liu, Eran Wo

Proceedings Volume 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022); 123301Y (2022) https://doi.org/10.1117/12.2646886
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 2022, Huzhou, China

Abstract

The research is conducted during the rage of COVID-19 throughout the world. The world meets new challenges from COVID-19 from every dimension, especially the economical world. In the economic world, the most related part for the influence that springs from COVID-19 is the stocks belonging to the healthcare sector. Aiming at doing the return prediction for healthcare sector stocks, the study chooses Long Short-Term Memory (LSTM) Algorithm to introduce machines to adapt the pattern and make predictions. The study selects 6 less volatile while keeping high average trading volume stocks from the healthcare sector. Using the LSTM learning model to learn the past 5 years’ data and make the prediction to the future 5 days. The data consist of 65% of the company's data from five years ago as the training set, and the last 35% of the data as the test set. The study compares the actual data to the predicted data and sees the error by calculating root mean square error (RMSE). The result draws the conclusion that the model will perform more precise prediction when the picked stock has a clear price trend and less fluctuation. The application for this study is to provide a short-term trading strategy and manage the risk for short-term stock investment by using the LSTM model.

Citation Download Citation

Zeyi Chen, Chengyuan Liu, and Eran Wo "Return prediction for healthcare sector stocks by using long short-term memory algorithm", Proc. SPIE 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 123301Y (23 August 2022); https://doi.org/10.1117/12.2646886

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available