Return prediction for healthcare sector stocks by using Long Short-Term Memory Algorithm

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. © 2022 SPIE.