International Journal of Data Science, Engineering, and Analytics (IJDASEA)
Vol. 4 No. 01 (2024): International Journal of Data Science, Engineering, and Analytics Vol 4, No 1,

Prediction of The Islamic Stock Price Index and Risk of Loss Using The Long Short-Term Memory (LSTM) and Value At Risk (VaR)

Taufik, Ikbar Athallah (Unknown)
Trimono, Trimono (Unknown)
Muhaimin, Amri (Unknown)



Article Info

Publish Date
27 May 2024

Abstract

Investment aims to increase the value of capital or earn additional income through asset growth, dividends or profits. One investment instrument that is in demand, especially among the Muslim community, is Islamic stocks, which are in accordance with Islamic principles that focus on a healthy economy. This research is focused on predicting Islamic stock prices using the Long Short-Term Memory (LSTM) method and measuring risk with Value at Risk (VaR) using the Cornish-Fisher Expansion (ECF) method. Stock price data from the food sector (PT Indofood), technology sector (Telkom Indonesia), and construction sector (Indocement) for the period 2018-2023 were analyzed. The results show that the ADAM model provides the best performance with the lowest prediction error rates for INTP and TLKM stocks (around 1.22%, 1.98%, and 1.41%). In addition, the SGD model shows limitations in accurate predictions with an error rate above 12%. VaR analysis reveals a slightly higher level of risk in INTP stocks, with a VaR value of around 2.85% at the 95% confidence level. Meanwhile, TLKM stock shows a lower level of risk, with a VaR of around 2.25% at the same confidence level. An in-depth understanding of the risk and growth characteristics of each stock, as well as the selection of the optimization model, are key in making wise investment decisions.

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Journal Info

Abbrev

ijdasea

Publisher

Subject

Computer Science & IT Decision Sciences, Operations Research & Management

Description

Focus and Scope The IJDASEA International Journal of Data Science, Engineering, and Analytics publishes original papers in the field of computer science which covers the following scope: 1. Theoretical Foundations: Probabilistic and Statistical Models and Theories Optimization Methods Data ...