Prediksi Indeks Bursa Efek Indonesia 2023 Pendekatan ARIMA, Machine Learning dengan R Programming
Abstract
The Indonesian Stock Exchange Index (BEI) is the main indicator of the Indonesian stock market. The problem in this research is a specific issue to solve the practical problem of predicting the future movement of the BEI index which has strategic value for investors, traders and companies in Indonesia. The aim of this research is to be able to predict the BEI index until the end of 2023, because the stock exchange plays a big role for the Indonesian economy as an economic and financial function. Traditional approaches such as ARIMA (Autoregressive Integrated Moving Average) have been used to predict the movement of the BEI index. However, in recent years, machine learning and data mining techniques have become popular as more sophisticated alternative approaches. This research uses a combined approach between ARIMA and machine learning with R Programming. Daily IDX index data from January 2012 to December 2022 will be taken from Yahoo Finance. This data will then be cleaned and processed using R Programming. The ARIMA approach will be used as a baseline to compare machine learning performance. The results focus on the estimated closing stock prices for the next 365 days or the average until the end of 2023. The Time Series value of the possible minimum/maximum predicted value for IDX shares in 2023 is a minimum predicted value of 6786,212 - 6849,559, a maximum predicted value of 6850,093 - 7086,012. Trends represent a good approach in predicting the future direction of closing prices.
References
Q. Chen, T. Guan, L. Yun, R. Li, and F. Recknagel, “Online forecasting chlorophyll a concentrations by an auto-regressive integrated moving average model: Feasibilities and potentials,” Harmful Algae, vol. 43, pp. 58–65, Mar. 2015.
R. C. Cruz, P. R. Costa, S. Vinga, L. Krippahl, and M. B. Lopes, “A review of recent machine learning advances for forecasting harmful algal blooms and shellfish contamination,” Journal of Marine Science and Engineering, vol. 9, no. 3. MDPI AG, 01-Mar-2021.
C. Anand, “Comparison of Stock Price Prediction Models using Pre-trained Neural Networks,” J. Ubiquitous Comput. Commun. Technol., vol. 3, no. 2, pp. 122–134, Jul. 2021.
D. S. Kumar, B. C. Thiruvarangan, C. Sai Nikhil Reddy, A. Vishnu, A. S. Devi, and D. Kavitha, “Analysis and Prediction of Stock Price Using Hybridization of SARIMA and XGBoost,” 2022 Int. Conf. Commun. Comput. Internet Things, IC3IoT 2022 - Proc., 2022.
W. Shen, X. Guo, C. Wu, and D. Wu, “Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm,” Knowledge-Based Syst., vol. 24, no. 3, pp. 378–385, Apr. 2011.
M. W. Libbrecht and W. S. Noble, “Machine learning applications in genetics and genomics,” Nat. Rev. Genet., vol. 16, no. 6, pp. 321–332, May 2015.
X. Cao and Z. Zhao, “Research on stock index forecasting based on ARIMA-GARCH and SVM mixed model,” Highlights Sci. Eng. Technol., vol. 4, pp. 40–46, Jul. 2022.
A. H. Bukhari, M. A. Z. Raja, M. Sulaiman, S. Islam, M. Shoaib, and P. Kumam, “Fractional neuro-sequential ARFIMA-LSTM for financial market forecasting,” IEEE Access, vol. 8, pp. 71326–71338, 2020.
G. L. Ramadhan, D. A. R., and H. Sussanto, “Peramalan Inflasi Indonesia Dengan Seasonal Auto Regressive Integrated Moving Average,” SISTEMASI, vol. 10, no. 3, p. undefined-undefined, Sep. 2021.
J. Bollen, H. Mao, and X. Zeng, “Twitter mood predicts the stock market,” J. Comput. Sci., vol. 2, no. 1, pp. 1–8, Mar. 2011.
H. Parikh, N. Panchal, and A. Sharma, “Cryptocurrency Price Prediction Using Machine Learning,” Lect. Notes Networks Syst., vol. 428, pp. 275–285, 2023.
E. Millikan, P. Subramanian, and M. H. Joseph, “Bitcoin Vision: Using Machine Learning and Data Mining to Predict the Short-Term and Long-Term Price of Bitcoin,” Webology, vol. 18, no. Special Issue, pp. 751–760, 2021.
C. M. Abraham, M. S. Elayidom, and T. Santhanakrishnan, “Big Data Analysis for Trend Recognition Using Machine Learning Techniques,” Int. J. Sensors, Wirel. Commun. Control, vol. 10, no. 4, pp. 540–550, Mar. 2020.
A. R. Banjanagari and B. Vijaykumar, “Retail giant sales forecasting using machine learning,” Int. J. Recent Technol. Eng., vol. 8, no. 2 Special Issue 11, pp. 2408–2411, Sep. 2019.
M. Hirey, J. Unagar, K. Prabhu, and R. Desai, “Analysis of Stock Price Prediction using Machine Learning Algorithms,” 2022 Int. Conf. Adv. Technol. ICONAT 2022, 2022.
L. C. Jiang and P. Subramanian, “Forecasting of stock price using autoregressive integrated moving average model,” J. Comput. Theor. Nanosci., vol. 16, no. 8, pp. 3519–3524, 2019.
S. Devi and V. Devaser, “Stock market price prediction using SAP predictive service,” Commun. Comput. Inf. Sci., vol. 955, pp. 135–148, 2019.
D. Borkin, M. Nemeth, and A. Nemethova, “Using Autoregressive Integrated Moving Average (ARIMA) for Prediction of Time Series Data,” Adv. Intell. Syst. Comput., vol. 1046, pp. 470–476, 2019.
B. K. Gupta, M. K. Mallick, and S. Hota, “Survey on stock price forecasting using regression analysis,” Smart Innov. Syst. Technol., vol. 153, pp. 147–156, 2021.
S. M. Idrees, M. A. Alam, P. Agarwal, and L. Ansari, “Effective Predictive Analytics and Modeling Based on Historical Data,” Commun. Comput. Inf. Sci., vol. 1046, pp. 552–564, 2019
