LSTM Algorithm Analysis in IHSG Prediction

Oct 13, 2025 by talynt.org 47 views

Prediction of Stock Prices in the Composite Stock Price Index (CSPI) uses the Long Short-Term Memory algorithm

The Composite Stock Price Index (CSPI) is an index that reflects all shares listed on the main board and the Indonesia Stock Exchange (IDX). Every day, the movement of shares listed on the IDX can experience a variety of trends, ranging from up, down, to stagnant. When the CSPI experienced an increase, usually this shows that most shares also feel an increase. Conversely, if the JCI declines, the stocks listed on the IDX tend to decrease. This study was conducted to predict the price of the JCI's stocks based on historical data and external data using the Long Short-Term Memory (LSTM) algorithm. The implementation results show the root of the root mean square error (RMSE) of 0.121 for training data (train scores) and 0.133 for test scores (test scores), indicating that the LSTM algorithm succeeded in providing quite accurate predictions.

Understanding the Composite Stock Price Index (CSPI)

The CSPI is a widely used index in Indonesia to measure the performance of the stock market. It is calculated based on the prices of shares listed on the IDX and is considered a reliable indicator of the overall market trend. The CSPI is influenced by various factors, including economic news, government policies, and global stock indexes. Understanding the CSPI is crucial for investors and analysts to make informed decisions about their investments.

The Long Short-Term Memory (LSTM) Algorithm

The Long Short-Term Memory (LSTM) algorithm is a type of recurrent neural network (RNN) designed to work with sequential data. The advantage of LSTM lies in its ability to remember information in the long run, which is very important to predict the movement of stock prices that are influenced by various market factors. Through this approach, LSTM can capture hidden patterns in historical data which can then be used to predict future price movements.

LSTM Algorithm Analysis in IHSG Prediction

In this study, the analysis was carried out by combining the Historical data of the JCI and external factors that affected the market, such as economic news, government policies, and global stock indexes. The use of richer and diverse data can increase prediction accuracy. The results of the relatively low RMSE value, namely 0.121 and 0.133, showed that this algorithm succeeded in making predictions well, indicating the consistency and reliability of the method in various conditions.

Advantages and Challenges in Using LSTM

Although LSTM offers many advantages, its use in stock price predictions cannot be separated from challenges. One of them is the need for large and high quality data. LSTM is very dependent on past data to make accurate predictions. Inaccuracy of data or lack of length time data can significantly affect prediction results. In addition, the stock market is also influenced by random factors that are difficult to predict, such as political events or natural disasters.

The Importance of Data Quality in LSTM

Data quality is a critical factor in the success of LSTM in predicting stock prices. The accuracy of the data used can significantly affect the prediction results. Inaccurate or incomplete data can lead to poor predictions, which can result in significant losses for investors. Therefore, it is essential to ensure that the data used is accurate, complete, and relevant to the market.

The Role of External Factors in Stock Price Prediction

External factors, such as economic news, government policies, and global stock indexes, play a significant role in stock price prediction. These factors can influence the market trend and affect the performance of individual stocks. Understanding these factors is crucial for investors and analysts to make informed decisions about their investments.

Conclusion

In conclusion, the use of the Long Short-Term Memory algorithm in predicting stock prices on the Composite Stock Price Index (CSPI) shows promising results. By understanding the pattern of movement of historical data and external factors, this algorithm can provide valuable guidelines for investors in making investment decisions. However, it is essential for investors to consider various aspects and risks that exist before making decisions based on prediction results. Stock investment always involves risks, and careful analysis will help in minimizing these risks.

Future Directions

The use of LSTM in predicting stock prices is a promising area of research. Future studies can focus on improving the accuracy of the algorithm by using more advanced techniques, such as deep learning and ensemble methods. Additionally, researchers can explore the use of LSTM in other areas, such as predicting stock prices in other markets or predicting other financial variables, such as interest rates and exchange rates.

Conclusion

Recommendations

Based on the findings of this study, the following recommendations can be made:

Investors should use the Long Short-Term Memory algorithm as a tool to make informed investment decisions.
Investors should consider various aspects and risks that exist before making decisions based on prediction results.
Researchers should continue to explore the use of LSTM in predicting stock prices and other financial variables.
Policymakers should consider the use of LSTM in predicting stock prices and other financial variables to inform their policy decisions.

Limitations

This study has several limitations that should be noted. Firstly, the study only used historical data to train the LSTM algorithm, which may not reflect the current market conditions. Secondly, the study only used a single algorithm, LSTM, to predict stock prices, which may not be the best approach. Finally, the study only focused on predicting stock prices in the Composite Stock Price Index (CSPI), which may not be representative of other markets.

Future Research Directions

Future research can focus on improving the accuracy of the LSTM algorithm by using more advanced techniques, such as deep learning and ensemble methods. Additionally, researchers can explore the use of LSTM in other areas, such as predicting stock prices in other markets or predicting other financial variables, such as interest rates and exchange rates.

Conclusion

In our previous article, we discussed the use of the Long Short-Term Memory (LSTM) algorithm in predicting stock prices on the Composite Stock Price Index (CSPI). In this article, we will answer some of the most frequently asked questions about the use of LSTM in stock price prediction.

Q: What is the Long Short-Term Memory (LSTM) algorithm?

A: The Long Short-Term Memory (LSTM) algorithm is a type of recurrent neural network (RNN) designed to work with sequential data. It is particularly useful for predicting time series data, such as stock prices, because it can capture long-term dependencies in the data.

Q: How does the LSTM algorithm work?

A: The LSTM algorithm works by using a network of interconnected nodes, or neurons, to process the input data. Each neuron has a set of weights that are adjusted during training to optimize the performance of the network. The LSTM algorithm uses a technique called backpropagation to adjust the weights and improve the performance of the network.

Q: What are the advantages of using the LSTM algorithm in stock price prediction?

A: The LSTM algorithm has several advantages in stock price prediction, including:

Improved accuracy: The LSTM algorithm can capture long-term dependencies in the data, which can lead to more accurate predictions.
Flexibility: The LSTM algorithm can be used with a wide range of input data, including historical prices, economic indicators, and other market data.
Robustness: The LSTM algorithm is robust to noise and outliers in the data, which can make it more reliable than other prediction methods.

Q: What are the challenges of using the LSTM algorithm in stock price prediction?

A: The LSTM algorithm has several challenges in stock price prediction, including:

Data quality: The LSTM algorithm requires high-quality data to produce accurate predictions. Poor data quality can lead to poor predictions.
Overfitting: The LSTM algorithm can overfit the training data, which can lead to poor predictions on new, unseen data.
Computational complexity: The LSTM algorithm can be computationally intensive, which can make it difficult to train and deploy in real-time.

Q: How can I use the LSTM algorithm in stock price prediction?

A: To use the LSTM algorithm in stock price prediction, you will need to:

Collect and preprocess the data: Collect historical prices and other relevant market data, and preprocess it to prepare it for use in the LSTM algorithm.
Train the model: Train the LSTM algorithm using the preprocessed data.
Evaluate the model: Evaluate the performance of the LSTM algorithm using metrics such as mean absolute error (MAE) and mean squared error (MSE).
Deploy the model: Deploy the trained LSTM algorithm in a production environment to make predictions on new, unseen data.

Q: What are some common applications of the LSTM algorithm in stock price prediction?

A: The LSTM algorithm has several common applications in stock price prediction, including:

Predicting stock prices: The LSTM algorithm can be used to predict stock prices based on historical prices and other market data.
Identifying trends: The LSTM algorithm can be used to identify trends in the market, such as uptrends or downtrends.
Detecting anomalies: The LSTM algorithm can be used to detect anomalies in the market, such as sudden changes in price or volume.

Q: What are some common challenges in implementing the LSTM algorithm in stock price prediction?

A: Some common challenges in implementing the LSTM algorithm in stock price prediction include:

Data quality: Poor data quality can lead to poor predictions.
Overfitting: The LSTM algorithm can overfit the training data, which can lead to poor predictions on new, unseen data.
Computational complexity: The LSTM algorithm can be computationally intensive, which can make it difficult to train and deploy in real-time.

Q: How can I improve the performance of the LSTM algorithm in stock price prediction?

A: To improve the performance of the LSTM algorithm in stock price prediction, you can try:

Collecting more data: Collecting more data can help to improve the accuracy of the LSTM algorithm.
Using more advanced techniques: Using more advanced techniques, such as deep learning and ensemble methods, can help to improve the accuracy of the LSTM algorithm.
Tuning the hyperparameters: Tuning the hyperparameters of the LSTM algorithm can help to improve its performance.

Q: What are some common mistakes to avoid when using the LSTM algorithm in stock price prediction?

A: Some common mistakes to avoid when using the LSTM algorithm in stock price prediction include:

Using poor data quality: Using poor data quality can lead to poor predictions.
Overfitting the training data: Overfitting the training data can lead to poor predictions on new, unseen data.
Not tuning the hyperparameters: Not tuning the hyperparameters of the LSTM algorithm can lead to poor performance.