Analysis of the Strengths and Weaknesses of the ARIMA Method

Oct 13, 2025 by recoveryshake.com 136 views

Introduction

Predicting stock price movements is a crucial aspect of the financial world, especially for investors who want to make informed decisions. One widely used method for forecasting stock prices is the ARIMA (Autoregressive Integrated Moving Average) method. Developed by George Edward Pelham Box and Gwilym Meirion Jenkins, this method is used to analyze time series data. This study aims to evaluate the accuracy of the ARIMA method in predicting the prices of automotive companies and components listed on the Indonesia Stock Exchange (IDX).

Background

The Indonesia Stock Exchange (IDX) is a dynamic market that often experiences volatility. Predicting stock prices in this market is a challenging task, but it is essential for investors to make informed decisions. The ARIMA method is a popular choice for forecasting stock prices due to its ability to capture seasonal patterns and data trends. However, the accuracy of this method depends on the stability of the stock price movement in the past.

Methodology

This study used a sample of 9 automotive companies and components listed on the IDX. The data used was secondary data obtained from the daily reports of each company for one year, starting from September 2018 to September 2019. The researchers used purposive sampling techniques to ensure that the data taken was relevant to the research.

Results

The results showed that the ARIMA method was able to predict future stock prices or actual prices quite accurately, especially if the price of stock in the past shows the same pattern of movement with future prices. However, when there is a significant price movement, whether it goes up or down, the ARIMA method tends to not be able to follow the pattern properly. This shows that the accuracy of predictions is very dependent on the stability of the stock price movement in the past.

Analysis of the Strengths and Weaknesses of the ARIMA Method

One of the advantages of the ARIMA method is its ability to capture seasonal patterns and data trends, which makes it ideal for data time series analysis such as stock prices. ARIMA works by building a model based on existing historical data, so that it can provide an overview of the possibility of future price movements. However, the main weakness of this method is its dependence on stable historical data. If extreme fluctuations occur, this method cannot always provide accurate results.

Application on the Indonesia Stock Exchange

The application of the ARIMA method in the context of the Indonesia Stock Exchange is very relevant, given the dynamic market nature and often experience volatility. With a deep understanding of how ARIMA works and when this method can be used, investors can make better investment decisions and minimize risk. On the other hand, it is important for investors to always combine this analysis with other approaches and still pay attention to external factors that can affect the market.

Conclusion

ARIMA method is an effective tool in predicting stock prices in the automotive sector and components on the Indonesia Stock Exchange, with a note that the pattern of price movements in the past remains consistent. For investors who want to take advantage of this method, it is important to understand both the advantages and restrictions of ARIMA, as well as combining them with other analytical methods for more optimal results. Thus, this research provides valuable insights for stakeholders in the stock market to make smarter investment decisions.

Recommendations

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

Investors should use the ARIMA method in conjunction with other analytical methods to make more informed investment decisions.
The ARIMA method should be used with caution when there are significant price movements in the market.
Investors should pay attention to external factors that can affect the market, such as economic indicators and global events.

Limitations

This study has several limitations that should be noted:

The sample size used in this study was limited to 9 automotive companies and components.
The data used was secondary data obtained from the daily reports of each company for one year.
The study only focused on the ARIMA method and did not compare it with other forecasting methods.

Future Research Directions

Future research should focus on the following areas:

Developing a more robust ARIMA model that can handle extreme fluctuations in the market.
Comparing the ARIMA method with other forecasting methods, such as machine learning algorithms.
Investigating the application of the ARIMA method in other markets, such as the US stock market.

Conclusion

Frequently Asked Questions

Q: What is the ARIMA method and how does it work?

A: The ARIMA method is a statistical technique used to forecast future values of a time series based on past values. It works by building a model based on existing historical data, which can provide an overview of the possibility of future price movements.

Q: What are the advantages of using the ARIMA method?

A: The ARIMA method has several advantages, including its ability to capture seasonal patterns and data trends, which makes it ideal for data time series analysis such as stock prices. It also works by building a model based on existing historical data, which can provide an overview of the possibility of future price movements.

Q: What are the limitations of the ARIMA method?

A: The ARIMA method has several limitations, including its dependence on stable historical data. If extreme fluctuations occur, this method cannot always provide accurate results. Additionally, the sample size used in this study was limited to 9 automotive companies and components.

Q: Can the ARIMA method be used in other markets, such as the US stock market?

A: Yes, the ARIMA method can be used in other markets, such as the US stock market. However, the accuracy of the method may vary depending on the market conditions and the stability of the historical data.

Q: How can investors use the ARIMA method to make better investment decisions?

A: Investors can use the ARIMA method to make better investment decisions by combining it with other analytical methods and paying attention to external factors that can affect the market. They should also use the ARIMA method with caution when there are significant price movements in the market.

Q: What are some potential risks associated with using the ARIMA method?

A: Some potential risks associated with using the ARIMA method include the dependence on stable historical data and the inability to handle extreme fluctuations in the market. Investors should also be aware of the limitations of the method and use it with caution.

Q: Can the ARIMA method be used in conjunction with other forecasting methods?

A: Yes, the ARIMA method can be used in conjunction with other forecasting methods, such as machine learning algorithms. This can help to improve the accuracy of the predictions and provide a more comprehensive view of the market.

Q: What are some potential applications of the ARIMA method in the financial industry?

A: Some potential applications of the ARIMA method in the financial industry include predicting stock prices, forecasting economic indicators, and analyzing market trends. The method can also be used to develop more accurate risk models and improve portfolio management.

Q: How can researchers and investors stay up-to-date with the latest developments in the ARIMA method?

A: Researchers and investors can stay up-to-date with the latest developments in the ARIMA method by attending conferences and workshops, reading academic journals and books, and participating in online forums and discussions.

Q: What are some potential future research directions for the ARIMA method?

A: Some potential future research directions for the ARIMA method include developing a more robust ARIMA model that can handle extreme fluctuations in the market, comparing the ARIMA method with other forecasting methods, and investigating the application of the ARIMA method in other markets.

Conclusion

In conclusion, the ARIMA method is a powerful tool for predicting stock prices in the automotive sector and components on the Indonesia Stock Exchange. However, it should be used with caution and in conjunction with other analytical methods to make more informed investment decisions. Future research should focus on developing a more robust ARIMA model and comparing it with other forecasting methods.