Forecasting Short-Term FTSE Bursa Malaysia Using WEKA
Keywords:
WEKA, Data Mining, stock market indices prediction, FTSE Bursa Malaysia KLCI
Abstract
This study investigates the use of machine learning methods, specifically utilizing the WEKA software, to predict stock prices of the FTSE Bursa Malaysia Kuala Lumpur Composite Index (KLCI). Two algorithms, Sequential Minimal Optimization Regression (SMOreg) and Multilayer perceptron (MLP), were employed for data analysis. Historical data from January 3, 2023, to December 29, 2023, was used to forecast open, high low, and close prices for ten days. Results from both algorithms were compared, with SMOreg proving to be more accurate than MLP for the dataset. However, it's important to note that further exploration of different forecasting algorithms may lead to even more precise results in the future. The findings of this analysis hold significant implications for investors, as they can use the insights gained to inform their investment strategies. By leveraging machine learning techniques like SMOreg within the WEKA framework, investors can potentially make more informed decisions regarding their stock market investments, leading to improved portfolio performance and risk management.
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References
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Choudhry, T., Hasan, M., & Zhang, Y. (2019). Forecasting the daily dynamic hedge ratios in emerging European stock futures markets: evidence from GARCH models. International Journal of Banking, Accounting and Finance, 10(1), 67-100. https://doi.org/10.1504/IJBAAF.2019.099316.
Devi, A. U., Vishal Kumar, R., Priya, P. S., Amzad Basha, M. S., Sucharitha, M. M. and Oveis, P. M. (2023). A Data Mining approach on the Performance of Machine Learning Methods for Share Price Forecasting using the Weka Environment, Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT), Erode, India, 2023, 01-06, doi: 10.1109/ICECCT56650.2023.10179772.
Dong, S., Wang, J., Luo, H., Wang, H., & Wu, F. X. (2021). A dynamic predictor selection algorithm for predicting stock market movement. Expert Systems with Applications, 186(August), 115836. https://doi.org/10.1016/j.eswa.2021.115836.
Dong, H., Guo, X., Reichgelt, H., & Hu, R. (2020). The predictive power of ARIMA models in forecasting equity returns: a sliding window method. Journal of Asset Management, 21, 549-566. https://doi.org/1057/s41260-020-00184-z.
Emami, S. S. (2018). Predicting Trends of Stock Prices by Developing Data Mining Techniques with the Aim of Gaining Profit. Journal of Accounting & Marketing, 07(04). https://doi.org/10.4172/2168-9601.1000304
Farias Nazário, R. T., e Silva, J. L., Sobreiro, V. A., & Kimura, H. (2017). A literature review of technical analysis on stock markets. Quarterly Review of Economics and Finance, 66, 115–126. https://doi.org/10.1016/j.qref.2017.01.014
Frank, E., Hall, M. A., & Witten, I. H. (2017). The WEKA workbench. Data Mining, 553–571. https://doi.org/10.1016/b978-0-12-804291-5.00024-6
Gandhmal, D. P., & Kumar, K. (2019). Systematic analysis and review of stock market prediction techniques. Computer Science Review, 34, 100190. https://doi.org/10.1016/j.cosrev.2019.08.001
Ismail, M. S., Noorani, M. S. M., Ismail, M., Razak, F. A., & Alias, M. A. (2020). Predicting next day direction of stock price movement using machine learning methods with persistent homology: Evidence from Kuala Lumpur Stock Exchange. Applied Soft Computing, 93, 106422. https://doi.org/10.1016/j.asoc.2020.106422.
Iaousse, M., Jouilil, Y., Bouincha, M., & Mentagui, D. (2023). A Comparative Simulation Study of Classical and Machine Learning Techniques for Forecasting Time Series Data. International Journal of Online and Biomedical Engineering, 19(8), 56–65. https://doi.org/10.3991/ijoe.v19i08.39853.
Jiang, W. (2021). Applications of deep learning in stock market prediction: Recent progress. Expert Systems with Applications, 184(June), 115537. https://doi.org/10.1016/j.eswa.2021.115537.
Joši?, B. Ž. and H. (2020). Forecasting Stock Market Indices Using Machine Learning Algorithms. Interdisciplinary Description of Complex Systems, 18(4), 471–489. https://doi.org/10.7906/indecs.18.4.7.
Khadjeh Nassirtoussi, A., Aghabozorgi, S., Ying Wah, T., & Ngo, D. C. L. (2014). Text mining for market prediction: A systematic review. Expert Systems with Applications, 41(16), 7653–7670. https://doi.org/10.1016/j.eswa.2014.06.009.
Kulkarni, A., & More, A. (2016). Formulation of a Prediction Index with the Help of WEKA Tool for Guiding the Stock Market Investors. Oriental Journal of Computer Science and Technology, 9(3), 212–225. https://doi.org/10.13005/ojcst/09.03.07.
Kumar, B. S., & Ravi, V. (2016). A survey of the applications of text mining in the financial domain. Knowledge-Based Systems, 114, 128–147. https://doi.org/10.1016/j.knosys.2016.10.003.
Kumar, D., Sarangi, P. K., & Verma, R. (2020). A systematic review of stock market prediction using machine learning and statistical techniques. Materials Today: Proceedings, 49, 3187–3191. https://doi.org/10.1016/j.matpr.2020.11.399.
Kumbure, M. M., Lohrmann, C., Luukka, P., & Porras, J. (2022). Machine learning techniques and data for stock market forecasting: A literature review. Expert Systems with Applications, 197(February), 116659. https://doi.org/10.1016/j.eswa.2022.116659.
Mintarya, L. N., Halim, J. N. M., Angie, C., Achmad, S., & Kurniawan, A. (2022). Machine learning approaches in stock market prediction: A systematic literature review. Procedia Computer Science, 216, 96–102. https://doi.org/10.1016/j.procs.2022.12.115.
Md Salim Chowdhury, Norun Nabi, Md Nasir Uddin Rana, Mujiba Shaima, Hammed Esa, Anik Mitra, Md Abu Sufian Mozumder, Irin Akter Liza, Md Murshid Reja Sweet, & Refat Naznin. (2024). Deep Learning Models for Stock Market Forecasting: A Comprehensive Comparative Analysis. Journal of Business and Management Studies, 6(2), 95–99. https://doi.org/10.32996/jbms.2024.6.2.9.
Navarro, M. M., Young, M. N., Prasetyo, Y. T., & Taylar, J. V. (2023). Stock market optimization amidst the COVID-19 pandemic: Technical analysis, K-means algorithm, and mean-variance model (TAKMV) approach. Heliyon, 9(7), e17577. https://doi.org/10.1016/j.heliyon.2023.e17577.
Nirob, F. A., & Hasan, M. M. (2023). Predicting Stock Price from Historical Data using LSTM Technique. Journal of Artificial Intelligence and Data Science (JAIDA), 3(1), 36–49. https://dergipark.org.tr/pub/jaida.
Patil, H., Bolla, B. K., Sabeesh, E., & Bhumireddy, D. R. (2024). Comparative Study of Predicting Stock Index Using Deep Learning Models. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 536, 45–57. https://doi.org/10.1007/978-3-031-48888-7_4.
Patra, S., & Hiremath, G. S. (2022). An Entropy Approach to Measure the Dynamic Stock Market Efficiency. Journal of Quantitative Economics, 20(2), 337-377. https://doi.org/10.1007/s40953-022-00295-x.
Peng, K. L., Wu, C. H., Lin, P. M. C., & Kou, I. T. E. (2023). Investor sentiment in the tourism stock market. Journal of Behavioral and Experimental Finance, 37, 100732. https://doi.org/10.1016/j.jbef.2022.100732.
Rashedi, K. A., Ismail, M. T., Al Wadi, S., Serroukh, A., Alshammari, T. S., & Jaber, J. J. (2024). Multi-Layer Perceptron-Based Classification with Application to Outlier Detection in Saudi Arabia Stock Returns. Journal of Risk and Financial Management, 17(2). https://doi.org/10.3390/jrfm17020069.
Rehman, I., & Soomro, T. R. (2019). Data Mining for Forecasting OGDCL Share Prices Using WEKA. MACS 2019 - 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics, Proceedings, 1–7. https://doi.org/10.1109/MACS48846.2019.9024780.
Rouf, N., Malik, M. B., Arif, T., Sharma, S., Singh, S., Aich, S., & Kim, H. C. (2021). Stock market prediction using machine learning techniques: A decade survey on methodologies, recent developments, and future directions. Electronics (Switzerland), 10(21). https://doi.org/10.3390/electronics10212717.
Rubio, L., & Alba, K. (2022). Forecasting selected Colombian shares using a hybrid ARIMA-SVR model. Mathematics, 10(13), 2181. https://doi.org/10.3390/math10132181.
Shah, A., Gor, M., Sagar, M., & Shah, M. (2022). A stock market trading framework based on deep learning architectures. Multimedia Tools and Applications, 81(10), 14153–14171. https://doi.org/10.1007/s11042-022-12328-x.
Villavicencio, C. N., Macrohon, J. J. E., Inbaraj, X. A., Jeng, J. H., & Hsieh, J. G. (2021). COVID-19 prediction applying supervised machine learning algorithms with comparative analysis using Weka. Algorithms, 14(7). https://doi.org/10.3390/a14070201.
Žmuk, B., & Jošic, H. (2020). Forecasting Stock Market Indices Using Machine Learning Algorithms. Interdisciplinary Description of Complex Systems, 18(4), 471–489. https://doi.org/10.7906/indecs.18.4.7.
Zou, J., Zhao, Q., Jiao, Y., Cao, H., Liu, Y., Yan, Q., Abbasnejad, E., Liu, L., & Shi, J. Q. (2023). Stock Market Prediction via Deep Learning Techniques: A Survey. 1(1). https://doi.org/10.48550/arXiv.2212.12717.
Ahmed, M. U., & Hussain, I. (2022). Prediction of Wheat Production Using Machine Learning Algorithms in northern areas of Pakistan. Telecommunications Policy, 46(6), 102370. https://doi.org/10.1016/j.telpol.2022.102370.
Al-Radaideh, Q., Assaf, A., & Alnagi, E. (2013). Predicting stock prices using data mining techniques. The International Arab Conference on Information Technology, December 2013, 8.
Choudhry, T., Hasan, M., & Zhang, Y. (2019). Forecasting the daily dynamic hedge ratios in emerging European stock futures markets: evidence from GARCH models. International Journal of Banking, Accounting and Finance, 10(1), 67-100. https://doi.org/10.1504/IJBAAF.2019.099316.
Devi, A. U., Vishal Kumar, R., Priya, P. S., Amzad Basha, M. S., Sucharitha, M. M. and Oveis, P. M. (2023). A Data Mining approach on the Performance of Machine Learning Methods for Share Price Forecasting using the Weka Environment, Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT), Erode, India, 2023, 01-06, doi: 10.1109/ICECCT56650.2023.10179772.
Dong, S., Wang, J., Luo, H., Wang, H., & Wu, F. X. (2021). A dynamic predictor selection algorithm for predicting stock market movement. Expert Systems with Applications, 186(August), 115836. https://doi.org/10.1016/j.eswa.2021.115836.
Dong, H., Guo, X., Reichgelt, H., & Hu, R. (2020). The predictive power of ARIMA models in forecasting equity returns: a sliding window method. Journal of Asset Management, 21, 549-566. https://doi.org/1057/s41260-020-00184-z.
Emami, S. S. (2018). Predicting Trends of Stock Prices by Developing Data Mining Techniques with the Aim of Gaining Profit. Journal of Accounting & Marketing, 07(04). https://doi.org/10.4172/2168-9601.1000304
Farias Nazário, R. T., e Silva, J. L., Sobreiro, V. A., & Kimura, H. (2017). A literature review of technical analysis on stock markets. Quarterly Review of Economics and Finance, 66, 115–126. https://doi.org/10.1016/j.qref.2017.01.014
Frank, E., Hall, M. A., & Witten, I. H. (2017). The WEKA workbench. Data Mining, 553–571. https://doi.org/10.1016/b978-0-12-804291-5.00024-6
Gandhmal, D. P., & Kumar, K. (2019). Systematic analysis and review of stock market prediction techniques. Computer Science Review, 34, 100190. https://doi.org/10.1016/j.cosrev.2019.08.001
Ismail, M. S., Noorani, M. S. M., Ismail, M., Razak, F. A., & Alias, M. A. (2020). Predicting next day direction of stock price movement using machine learning methods with persistent homology: Evidence from Kuala Lumpur Stock Exchange. Applied Soft Computing, 93, 106422. https://doi.org/10.1016/j.asoc.2020.106422.
Iaousse, M., Jouilil, Y., Bouincha, M., & Mentagui, D. (2023). A Comparative Simulation Study of Classical and Machine Learning Techniques for Forecasting Time Series Data. International Journal of Online and Biomedical Engineering, 19(8), 56–65. https://doi.org/10.3991/ijoe.v19i08.39853.
Jiang, W. (2021). Applications of deep learning in stock market prediction: Recent progress. Expert Systems with Applications, 184(June), 115537. https://doi.org/10.1016/j.eswa.2021.115537.
Joši?, B. Ž. and H. (2020). Forecasting Stock Market Indices Using Machine Learning Algorithms. Interdisciplinary Description of Complex Systems, 18(4), 471–489. https://doi.org/10.7906/indecs.18.4.7.
Khadjeh Nassirtoussi, A., Aghabozorgi, S., Ying Wah, T., & Ngo, D. C. L. (2014). Text mining for market prediction: A systematic review. Expert Systems with Applications, 41(16), 7653–7670. https://doi.org/10.1016/j.eswa.2014.06.009.
Kulkarni, A., & More, A. (2016). Formulation of a Prediction Index with the Help of WEKA Tool for Guiding the Stock Market Investors. Oriental Journal of Computer Science and Technology, 9(3), 212–225. https://doi.org/10.13005/ojcst/09.03.07.
Kumar, B. S., & Ravi, V. (2016). A survey of the applications of text mining in the financial domain. Knowledge-Based Systems, 114, 128–147. https://doi.org/10.1016/j.knosys.2016.10.003.
Kumar, D., Sarangi, P. K., & Verma, R. (2020). A systematic review of stock market prediction using machine learning and statistical techniques. Materials Today: Proceedings, 49, 3187–3191. https://doi.org/10.1016/j.matpr.2020.11.399.
Kumbure, M. M., Lohrmann, C., Luukka, P., & Porras, J. (2022). Machine learning techniques and data for stock market forecasting: A literature review. Expert Systems with Applications, 197(February), 116659. https://doi.org/10.1016/j.eswa.2022.116659.
Mintarya, L. N., Halim, J. N. M., Angie, C., Achmad, S., & Kurniawan, A. (2022). Machine learning approaches in stock market prediction: A systematic literature review. Procedia Computer Science, 216, 96–102. https://doi.org/10.1016/j.procs.2022.12.115.
Md Salim Chowdhury, Norun Nabi, Md Nasir Uddin Rana, Mujiba Shaima, Hammed Esa, Anik Mitra, Md Abu Sufian Mozumder, Irin Akter Liza, Md Murshid Reja Sweet, & Refat Naznin. (2024). Deep Learning Models for Stock Market Forecasting: A Comprehensive Comparative Analysis. Journal of Business and Management Studies, 6(2), 95–99. https://doi.org/10.32996/jbms.2024.6.2.9.
Navarro, M. M., Young, M. N., Prasetyo, Y. T., & Taylar, J. V. (2023). Stock market optimization amidst the COVID-19 pandemic: Technical analysis, K-means algorithm, and mean-variance model (TAKMV) approach. Heliyon, 9(7), e17577. https://doi.org/10.1016/j.heliyon.2023.e17577.
Nirob, F. A., & Hasan, M. M. (2023). Predicting Stock Price from Historical Data using LSTM Technique. Journal of Artificial Intelligence and Data Science (JAIDA), 3(1), 36–49. https://dergipark.org.tr/pub/jaida.
Patil, H., Bolla, B. K., Sabeesh, E., & Bhumireddy, D. R. (2024). Comparative Study of Predicting Stock Index Using Deep Learning Models. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 536, 45–57. https://doi.org/10.1007/978-3-031-48888-7_4.
Patra, S., & Hiremath, G. S. (2022). An Entropy Approach to Measure the Dynamic Stock Market Efficiency. Journal of Quantitative Economics, 20(2), 337-377. https://doi.org/10.1007/s40953-022-00295-x.
Peng, K. L., Wu, C. H., Lin, P. M. C., & Kou, I. T. E. (2023). Investor sentiment in the tourism stock market. Journal of Behavioral and Experimental Finance, 37, 100732. https://doi.org/10.1016/j.jbef.2022.100732.
Rashedi, K. A., Ismail, M. T., Al Wadi, S., Serroukh, A., Alshammari, T. S., & Jaber, J. J. (2024). Multi-Layer Perceptron-Based Classification with Application to Outlier Detection in Saudi Arabia Stock Returns. Journal of Risk and Financial Management, 17(2). https://doi.org/10.3390/jrfm17020069.
Rehman, I., & Soomro, T. R. (2019). Data Mining for Forecasting OGDCL Share Prices Using WEKA. MACS 2019 - 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics, Proceedings, 1–7. https://doi.org/10.1109/MACS48846.2019.9024780.
Rouf, N., Malik, M. B., Arif, T., Sharma, S., Singh, S., Aich, S., & Kim, H. C. (2021). Stock market prediction using machine learning techniques: A decade survey on methodologies, recent developments, and future directions. Electronics (Switzerland), 10(21). https://doi.org/10.3390/electronics10212717.
Rubio, L., & Alba, K. (2022). Forecasting selected Colombian shares using a hybrid ARIMA-SVR model. Mathematics, 10(13), 2181. https://doi.org/10.3390/math10132181.
Shah, A., Gor, M., Sagar, M., & Shah, M. (2022). A stock market trading framework based on deep learning architectures. Multimedia Tools and Applications, 81(10), 14153–14171. https://doi.org/10.1007/s11042-022-12328-x.
Villavicencio, C. N., Macrohon, J. J. E., Inbaraj, X. A., Jeng, J. H., & Hsieh, J. G. (2021). COVID-19 prediction applying supervised machine learning algorithms with comparative analysis using Weka. Algorithms, 14(7). https://doi.org/10.3390/a14070201.
Žmuk, B., & Jošic, H. (2020). Forecasting Stock Market Indices Using Machine Learning Algorithms. Interdisciplinary Description of Complex Systems, 18(4), 471–489. https://doi.org/10.7906/indecs.18.4.7.
Zou, J., Zhao, Q., Jiao, Y., Cao, H., Liu, Y., Yan, Q., Abbasnejad, E., Liu, L., & Shi, J. Q. (2023). Stock Market Prediction via Deep Learning Techniques: A Survey. 1(1). https://doi.org/10.48550/arXiv.2212.12717.
Published
2024-05-29
How to Cite
Nizar, N., Zainudin, A. D., Albada, A., & Shan, C. M. (2024). Forecasting Short-Term FTSE Bursa Malaysia Using WEKA. Information Management and Business Review, 16(2(I)S), 104-114. https://doi.org/10.22610/imbr.v16i2(I)S.3773
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Research Paper
Copyright (c) 2024 Nurhuda Nizar, Ahmad Danial Zainudin, Ali Albada, Chua Mei Shan
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