LQ45 Index Stock Market Prediction: A Deep Learning Approach using LSTM with Bayesian Optimization

Padma Nyoman Crisnapati; Putu Devi Novayanti; Dian Pramana

doi:10.30865/json.v7i2.8925

Authors

Padma Nyoman Crisnapati ITB STIKOM Bali
Putu Devi Novayanti ITB STIKOM Bali
Dian Pramana ITB STIKOM Bali

DOI:

https://doi.org/10.30865/json.v7i2.8925

Keywords:

Long Short-Term Memory (LSTM), Bayesian Optimization, Stock Price Prediction, Machine Learning, Financial Technology

Abstract

This study investigates the application of Long Short-Term Memory (LSTM) models with Bayesian Optimization for predicting stock price movements in the LQ45 Index, a collection of the 45 most liquid stocks on the Indonesia Stock Exchange. The primary objective is to enhance prediction accuracy by addressing the challenges of volatile stock markets and inefficient hyperparameter tuning. Historical data, including daily closing prices from January 2020 to October 2024, was processed using Min-Max Scaling and transformed into time-series input features with a 60-time-step window. Bayesian Optimization was employed to fine-tune key hyperparameters such as LSTM units, dropout rate, and learning rate, optimizing the model's performance. The results revealed that the LSTM model accurately captured trends for stocks with stable price patterns, such as ACES, ASII, and MTEL, achieving low Mean Absolute Percentage Error (MAPE) and Root Mean Square Percentage Error (RMSPE). However, stocks with high volatility, like AMMN and ITMG, exhibited higher prediction errors, indicating limitations in modeling complex patterns. The study highlights that while LSTM with Bayesian Optimization is highly effective for stable stocks, additional preprocessing and advanced modeling techniques are required for volatile stocks. This research demonstrates the potential of machine learning in supporting stock market decision-making, contributing to the development of more robust and efficient financial prediction tools for investors navigating dynamic markets.

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