IDEAS home Printed from https://ideas.repec.org/a/rnd/arimbr/v16y2024i2p104-114.html
   My bibliography  Save this article

Forecasting Short-Term FTSE Bursa Malaysia Using WEKA

Author

Listed:
  • Nurhuda Nizar
  • Ahmad Danial Zainudin
  • Ali Albada
  • Chua Mei Shan

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.

Suggested Citation

  • Nurhuda Nizar & Ahmad Danial Zainudin & Ali Albada & Chua Mei Shan, 2024. "Forecasting Short-Term FTSE Bursa Malaysia Using WEKA," Information Management and Business Review, AMH International, vol. 16(2), pages 104-114.
    • Handle: RePEc:rnd:arimbr:v:16:y:2024:i:2:p:104-114
    DOI: 10.22610/imbr.v16i2(I)S.3773
    as

    Download full text from publisher

    File URL: https://ojs.amhinternational.com/index.php/imbr/article/view/3773/2471
    Download Restriction: no
    File URL: https://ojs.amhinternational.com/index.php/imbr/article/view/3773
    Download Restriction: no
    File URL: https://libkey.io/10.22610/imbr.v16i2(I)S.3773?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Lihki Rubio & Keyla Alba, 2022. "Forecasting Selected Colombian Shares Using a Hybrid ARIMA-SVR Model," Mathematics, MDPI, vol. 10(13), pages 1-21, June.
    2. Taufiq Choudhry & Mohammad Hasan & Yuanyuan Zhang, 2019. "Forecasting the daily dynamic hedge ratios in emerging European stock futures markets: evidence from GARCH models," International Journal of Banking, Accounting and Finance, Inderscience Enterprises Ltd, vol. 10(1), pages 67-100.
    3. Huijian Dong & Xiaomin Guo & Han Reichgelt & Ruizhi Hu, 2020. "Predictive power of ARIMA models in forecasting equity returns: a sliding window method," Journal of Asset Management, Palgrave Macmillan, vol. 21(6), pages 549-566, October.
    4. Berislav Žmuk & Hrvoje Jošiæ, 2020. "Forecasting stock market indices using machine learning algorithms," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 18(4), pages 471-489.
    5. Ahmed, Moiz Uddin & Hussain, Iqbal, 2022. "Prediction of Wheat Production Using Machine Learning Algorithms in northern areas of Pakistan," Telecommunications Policy, Elsevier, vol. 46(6).
    6. Peng, Kang-Lin & Wu, Chih-Hung & Lin, Pearl M.C. & Kou, IokTeng Esther, 2023. "Investor sentiment in the tourism stock market," Journal of Behavioral and Experimental Finance, Elsevier, vol. 37(C).
    7. Subhamitra Patra & Gourishankar S. Hiremath, 2022. "An Entropy Approach to Measure the Dynamic Stock Market Efficiency," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 20(2), pages 337-377, June.
    8. Jinan Zou & Qingying Zhao & Yang Jiao & Haiyao Cao & Yanxi Liu & Qingsen Yan & Ehsan Abbasnejad & Lingqiao Liu & Javen Qinfeng Shi, 2022. "Stock Market Prediction via Deep Learning Techniques: A Survey," Papers 2212.12717, arXiv.org, revised Feb 2023.
    9. Khudhayr A. Rashedi & Mohd Tahir Ismail & Sadam Al Wadi & Abdeslam Serroukh & Tariq S. Alshammari & Jamil J. Jaber, 2024. "Multi-Layer Perceptron-Based Classification with Application to Outlier Detection in Saudi Arabia Stock Returns," JRFM, MDPI, vol. 17(2), pages 1-13, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Uddin, Ajim & Tao, Xinyuan & Yu, Dantong, 2023. "Attention based dynamic graph neural network for asset pricing," Global Finance Journal, Elsevier, vol. 58(C).
    2. Luzia, Ruan & Rubio, Lihki & Velasquez, Carlos E., 2023. "Sensitivity analysis for forecasting Brazilian electricity demand using artificial neural networks and hybrid models based on Autoregressive Integrated Moving Average," Energy, Elsevier, vol. 274(C).
    3. Ayse Yavuz Ozalp & Halil Akinci, 2023. "Evaluation of Land Suitability for Olive ( Olea europaea L.) Cultivation Using the Random Forest Algorithm," Agriculture, MDPI, vol. 13(6), pages 1-22, June.
    4. Rupel Nargunam & William W. S. Wei & N. Anuradha, 2021. "Investigating seasonality, policy intervention and forecasting in the Indian gold futures market: a comparison based on modeling non-constant variance using two different methods," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-15, December.
    5. Patryk Hara & Magdalena Piekutowska & Gniewko Niedbała, 2022. "Prediction of Protein Content in Pea ( Pisum sativum L.) Seeds Using Artificial Neural Networks," Agriculture, MDPI, vol. 13(1), pages 1-21, December.
    6. Guo, Yangli & Li, Pan & Wu, Hanlin, 2023. "Jumps in the Chinese crude oil futures volatility forecasting: New evidence," Energy Economics, Elsevier, vol. 126(C).
    7. Naeem, Muhammad Abubakr & Chatziantoniou, Ioannis & Gabauer, David & Karim, Sitara, 2024. "Measuring the G20 stock market return transmission mechanism: Evidence from the R2 connectedness approach," International Review of Financial Analysis, Elsevier, vol. 91(C).
    8. Yuhui Jin, 2024. "GraphCNNpred: A stock market indices prediction using a Graph based deep learning system," Papers 2407.03760, arXiv.org, revised Jul 2024.
    9. Gulay, Emrah & Sen, Mustafa & Akgun, Omer Burak, 2024. "Forecasting electricity production from various energy sources in Türkiye: A predictive analysis of time series, deep learning, and hybrid models," Energy, Elsevier, vol. 286(C).
    10. Boyue Fang & Yutong Feng, 2019. "Design of High-Frequency Trading Algorithm Based on Machine Learning," Papers 1912.10343, arXiv.org.
    11. Samuel Tabot ENOW, 2023. "A Non-linear Dependency Test for Market Efficiency: Evidence from International Stock Markets," Journal of Economics and Financial Analysis, Tripal Publishing House, vol. 7(1), pages 1-12.
    12. Guo, Yangli & He, Feng & Liang, Chao & Ma, Feng, 2022. "Oil price volatility predictability: New evidence from a scaled PCA approach," Energy Economics, Elsevier, vol. 105(C).
    13. Erjiang E & Ming Yu & Xin Tian & Ye Tao, 2022. "Dynamic Model Selection Based on Demand Pattern Classification in Retail Sales Forecasting," Mathematics, MDPI, vol. 10(17), pages 1-16, September.
    14. Radhika Prosad Datta, 2023. "Leveraging Sample Entropy for Enhanced Volatility Measurement and Prediction in International Oil Price Returns," Papers 2312.12788, arXiv.org.
    15. Nida Iqbal & Muhammad Umair Shahzad & El-Sayed M. Sherif & Muhammad Usman Tariq & Javed Rashid & Tuan-Vinh Le & Anwar Ghani, 2024. "Analysis of Wheat-Yield Prediction Using Machine Learning Models under Climate Change Scenarios," Sustainability, MDPI, vol. 16(16), pages 1-26, August.
    16. Tiago E. Pratas & Filipe R. Ramos & Lihki Rubio, 2023. "Forecasting bitcoin volatility: exploring the potential of deep learning," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 13(2), pages 285-305, June.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:rnd:arimbr:v:16:y:2024:i:2:p:104-114. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Muhammad Tayyab (email available below). General contact details of provider: https://ojs.amhinternational.com/index.php/imbr .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.