Prediction of Stock Price Volatility Using the Long Short Term Memory (LSTM) Model for Investment Portfolio Selection Strategy
Journal Title: International Journal of Current Science Research and Review - Year 2024, Vol 7, Issue 05
Abstract
Volatility is an important variable in financial data models. Predicting volatility in financial data is helpful for investors to make good decisions to reduce risk and to gain investment returns. In predicting volatility, many researchers have conducted research in building prediction models using data mining. This research uses a deep learning algorithm, namely Long Short Term Memory (LSTM) which has high accuracy compared to other models. The research aims to predict stock price volatility and for investment portfolio selection. The object of this study is the historical stock price of PT. Unilever Indonesia Tbk. (UNVR), PT. Fast Food Indonesia Tbk. (FAST) which manages KFC and PT. MAP Boga Adiperkasa Tbk. (MAPB) which manages Starbucks in the period 2023 to 2024, when there was a boycott caused by the war between countries that occurred in the Middle East. The data is analysed using the LSTM model where stock price volatility was determined by the variance of the return and log return on the next seven days, then using LSTM the stock price volatility data was predicted. The results show that the MSE and RMSE values are very small, which means that the volatility prediction results are almost the same as the actual data. And the average volatility prediction results in UNVR stock of 0.00841, MAPB stock of 0.01717, and FAST stock of 0.01323. From these results can be used as a reference for the selection of investment portfolios.
Authors and Affiliations
Ana Khofifahturizqi, Farikhin . , Ratna Herdiana, Yola Ulrike Sihombing,
Keywords
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- EP ID EP735835
- DOI 10.47191/ijcsrr/V7-i5-84
- Views 57
- Downloads 0
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