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.

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Keywords :

investment, Investment Portfolio, LSTM, Stock price., Volatility.

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References :

1. Z. Ouyang, M. Lu, dan Y. Lai, “Forecasting stock index return and volatility based on GAVMD- Carbon-BiLSTM: How important is carbon emission trading?,” Energy Econ., vol. 128, no. October, hal. 107134, 2023, doi: 10.1016/j.eneco.2023.107134.
2. Y. Yu, Y. Lin, X. Hou, dan X. Zhang, “Novel optimization approach for realized volatility forecast of stock price index based on deep reinforcement learning model,” Expert Syst. Appl., vol. 233, no. January, hal. 120880, 2023, doi: 10.1016/j.eswa.2023.120880.
3. Z. De Khoo, K. H. Ng, Y. B. Koh, dan K. H. Ng, “Forecasting volatility of stock indices: Improved GARCH-type models through combined weighted volatility measure and weighted volatility indicators,” North Am. J. Econ. Financ., vol. 71, no. January, 2024, doi: 10.1016/j.najef.2024.102112.
4. H. Markowitz, “Portofolio Selection,” J. Finance, vol. 1, no. 7, hal. 77–91, 1952.
5. H. Y. Kim dan C. H. Won, “Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models,” Expert Syst. Appl., vol. 103, hal. 25–37, 2018, doi: 10.1016/j.eswa.2018.03.002.
6. M. F. Rizkilloh dan S. Widyanesti, “Cryptocurrency Price Prediction Using Long Short Term Memory (LSTM) Algorith,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, no. 1, hal. 25–31, 2022, doi: 10.29207/resti.v6i1.3630.
7. R. Liu, Y. Jiang, dan J. Lin, “Forecasting the Volatility of Specific Risk for Stocks with LSTM,” Procedia Comput. Sci., vol. 202, hal. 111–114, 2022, doi: 10.1016/j.procs.2022.04.015.
8. M. Beniwal, A. Singh, dan N. Kumar, “Forecasting multistep daily stock prices for long-term investment decisions: A study of deep learning models on global indices,” Eng. Appl. Artif. Intell., vol. 129, no. June 2023, hal. 107617, 2024, doi: 10.1016/j.engappai.2023.107617.
9. A. Petrozziello dkk., “Deep learning for volatility forecasting in asset management,” Soft Comput., vol. 26, no. 17, hal. 8553–8574, 2022, doi: 10.1007/s00500-022-07161-1.
10. A. Bash dan K. Alsaifi, “Fear from uncertainty: An event study of Khashoggi and stock market returns,” J. Behav. Exp. Financ., vol. 23, no. October 2018, hal. 54–58, 2019, doi: 10.1016/j.jbef.2019.05.004.
11. S. Shanaev dan B. Ghimire, “Is all politics local? Regional political risk in Russia and the panel of stock returns,” J. Behav. Exp. Financ., vol. 21, no. September 2017, hal. 70–82, 2019, doi: 10.1016/j.jbef.2018.11.002.
12. Y. Xu, T. Liu, dan P. Du, “Volatility forecasting of crude oil futures based on Bi-LSTM-Attention model: The dynamic role of the COVID-19 pandemic and the Russian-Ukrainian conflict,” Resour. Policy, vol. 88, no. November 2023, hal. 104319, 2024, doi: 10.1016/j.resourpol.2023.104319.
13. Y. Maulana, “Modeling The Volatility of Sectoral Stock Price Indices in Indonesia,” Log.  J. Penelit. Univ. Kuningan, vol. 13, no. 1, hal. 53–72, 2022.
14. E. Chotib dan N. Huda, “Analysis of the Effect of Macroeconomic Variables On the Indonesian Sharia Stock Index (ISSI) for the Period 2016 – 2019,” JEBA (Journal Econ. Bus. Aseanomics), vol. 4, no. 1, hal. 174–195, 2020, doi: 10.33476/j.e.b.a.v4i1.1235.
15. A. Atkins, M. Niranjan, dan E. Gerding, “Financial news predicts stock market volatility better than close price,” J. Financ. Data Sci., vol. 4, no. 2, hal. 120–137, 2018, doi: 10.1016/j.jfds.2018.02.002.
16. A. García-Medina dan E. Aguayo-Moreno, “LSTM–GARCH Hybrid Model for the Prediction of Volatility in Cryptocurrency Portfolios,” Comput. Econ., 2023, doi: 10.1007/s10614-023-10373-8.
17. J. Schmidhuber, “Lstm Can Solve Hard Long Time Lag Problems,” vol. 9, hal. 473–479, 1997.
18. T. Roncalli, Introduction to risk parity and budgeting. New York: CRC Press, 2013.

19. Syafitri dan C. Batubara, “Evaluation of Islamic Banks Investment Strategies in the Face Of Financial Market Volatility,” J. Ilmu Komputer, Ekon. dan Manaj., vol. 3, no. 2, hal. 3565–3566, 2023.