IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0297641.html
   My bibliography  Save this article

Heteroscedasticity effects as component to future stock market predictions using RNN-based models

Author

Listed:
  • Aida Nabilah Sadon
  • Shuhaida Ismail
  • Azme Khamis
  • Muhammad Usman Tariq

Abstract

Heteroscedasticity effects are useful for forecasting future stock return volatility. Stock volatility forecasting provides business insight into the stock market, making it valuable information for investors and traders. Predicting stock volatility is a crucial task and challenging. This study proposes a hybrid model that predicts future stock volatility values by considering the heteroscedasticity element of the stock price. The proposed model is a combination of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and a well-known Recurrent Neural Network (RNN) algorithm Long Short-Term Memory (LSTM). This proposed model is referred to as GARCH-LSTM model. The proposed model is expected to improve prediction accuracy by considering heteroscedasticity elements. First, the GARCH model is employed to estimate the model parameters. After that, the ARCH effect test is used to test the residuals obtained from the model. Any untrained heteroscedasticity element must be found using this step. The hypothesis of the ARCH test yielded a p-value less than 0.05 indicating there is valuable information remaining in the residual, known as heteroscedasticity element. Next, the dataset with heteroscedasticity is then modelled using an LSTM-based RNN algorithm. Experimental results revealed that hybrid GARCH-LSTM had the lowest MAE (7.961), RMSE (10.466), MAPE (0.516) and HMAE (0.005) values compared with a single LSTM. The accuracy of forecasting was also significantly improved by 15% and 13% with hybrid GARCH-LSTM in comparison to single LSTMs. Furthermore, the results reveal that hybrid GARCH-LSTM fully exploits the heteroscedasticity element, which is not captured by the GARCH model estimation, outperforming GARCH models on their own. This finding from this study confirmed that hybrid GARCH-LSTM models are effective forecasting tools for predicting stock price movements. In addition, the proposed model can assist investors in making informed decisions regarding stock prices since it is capable of closely predicting and imitating the observed pattern and trend of KLSE stock prices.

Suggested Citation

  • Aida Nabilah Sadon & Shuhaida Ismail & Azme Khamis & Muhammad Usman Tariq, 2024. "Heteroscedasticity effects as component to future stock market predictions using RNN-based models," PLOS ONE, Public Library of Science, vol. 19(5), pages 1-18, May.
    • Handle: RePEc:plo:pone00:0297641
    DOI: 10.1371/journal.pone.0297641
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0297641
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0297641&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0297641?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. M. Mallikarjuna & R. Prabhakara Rao, 2019. "Evaluation of forecasting methods from selected stock market returns," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-16, December.
    2. Yuruixian Zhang & Wei Chong Choo & Yuhanis Abdul Aziz & Choy Leong Yee & Cheong Kin Wan & Jen Sim Ho, 2022. "Effects of Multiple Financial News Shocks on Tourism Demand Volatility Modelling and Forecasting," JRFM, MDPI, vol. 15(7), pages 1-47, June.
    3. Mingyue Qiu & Yu Song, 2016. "Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-11, May.
    4. Vanshu Mahajan & Sunil Thakan & Aashish Malik, 2022. "Modeling and Forecasting the Volatility of NIFTY 50 Using GARCH and RNN Models," Economies, MDPI, vol. 10(5), pages 1-20, April.
    5. Alexander Jakob Dautel & Wolfgang Karl Härdle & Stefan Lessmann & Hsin-Vonn Seow, 2020. "Forex exchange rate forecasting using deep recurrent neural networks," Digital Finance, Springer, vol. 2(1), pages 69-96, September.
    6. U, JuHyok & Lu, PengYu & Kim, ChungSong & Ryu, UnSok & Pak, KyongSok, 2020. "A new LSTM based reversal point prediction method using upward/downward reversal point feature sets," Chaos, Solitons & Fractals, Elsevier, vol. 132(C).
    7. Desa Daba Fufa & Belianeh Legesse Zeleke, 2018. "Forecasting the Volatility of Ethiopian Birr/Euro Exchange Rate Using Garch-Type Models," Annals of Data Science, Springer, vol. 5(4), pages 529-547, December.
    8. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    9. Zahedi, Javad & Rounaghi, Mohammad Mahdi, 2015. "Application of artificial neural network models and principal component analysis method in predicting stock prices on Tehran Stock Exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 178-187.
    10. Mohammad Arashi & Mohammad Mahdi Rounaghi, 2022. "Analysis of market efficiency and fractal feature of NASDAQ stock exchange: Time series modeling and forecasting of stock index using ARMA-GARCH model," Future Business Journal, Springer, vol. 8(1), pages 1-12, December.
    11. Hu, Yan & Ni, Jian & Wen, Liu, 2020. "A hybrid deep learning approach by integrating LSTM-ANN networks with GARCH model for copper price volatility prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    12. Alexandru Badescu & Zhenyu Cui & Juan-Pablo Ortega, 2019. "Closed-form variance swap prices under general affine GARCH models and their continuous-time limits," Annals of Operations Research, Springer, vol. 282(1), pages 27-57, November.
    13. Kizilaslan, Recep & Freund, Steven & Iseri, Ali, 2016. "A data analytic approach to forecasting daily stock returns in an emerging marketAuthor-Name: Oztekin, Asil," European Journal of Operational Research, Elsevier, vol. 253(3), pages 697-710.
    14. Sarika Murty & Vijay Victor & Maria Fekete-Farkas, 2022. "Is Bitcoin a Safe Haven for Indian Investors? A GARCH Volatility Analysis," JRFM, MDPI, vol. 15(7), pages 1-13, July.
    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. Ghaemi Asl, Mahdi & Adekoya, Oluwasegun Babatunde & Rashidi, Muhammad Mahdi & Oliyide, Johnson Ayobami & Rajab, Sahel, 2024. "A new approach to forecasting Islamic and conventional oil and gas stock prices," International Review of Economics & Finance, Elsevier, vol. 96(PA).
    2. Haojun Pan & Yuxiang Tang & Guoqiang Wang, 2024. "A Stock Index Futures Price Prediction Approach Based on the MULTI-GARCH-LSTM Mixed Model," Mathematics, MDPI, vol. 12(11), pages 1-15, May.
    3. Weronika Ormaniec & Marcin Pitera & Sajad Safarveisi & Thorsten Schmidt, 2022. "Estimating value at risk: LSTM vs. GARCH," Papers 2207.10539, arXiv.org.
    4. Shafiqah Azman & Dharini Pathmanathan & Aerambamoorthy Thavaneswaran, 2022. "Forecasting the Volatility of Cryptocurrencies in the Presence of COVID-19 with the State Space Model and Kalman Filter," Mathematics, MDPI, vol. 10(17), pages 1-15, September.
    5. Pegah Eslamieh & Mehdi Shajari & Ahmad Nickabadi, 2023. "User2Vec: A Novel Representation for the Information of the Social Networks for Stock Market Prediction Using Convolutional and Recurrent Neural Networks," Mathematics, MDPI, vol. 11(13), pages 1-26, July.
    6. Heni Boubaker & Giorgio Canarella & Rangan Gupta & Stephen M. Miller, 2023. "A Hybrid ARFIMA Wavelet Artificial Neural Network Model for DJIA Index Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1801-1843, December.
    7. Ehsan Hoseinzade & Saman Haratizadeh & Arash Khoeini, 2019. "U-CNNpred: A Universal CNN-based Predictor for Stock Markets," Papers 1911.12540, arXiv.org.
    8. Daniel Poh & Bryan Lim & Stefan Zohren & Stephen Roberts, 2021. "Enhancing Cross-Sectional Currency Strategies by Context-Aware Learning to Rank with Self-Attention," Papers 2105.10019, arXiv.org, revised Jan 2022.
    9. Pengfei Zhao & Haoren Zhu & Wilfred Siu Hung NG & Dik Lun Lee, 2024. "From GARCH to Neural Network for Volatility Forecast," Papers 2402.06642, arXiv.org.
    10. 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.
    11. Escobar-Anel, Marcos & Spies, Ben & Zagst, Rudi, 2024. "Mean–variance optimization under affine GARCH: A utility-based solution," Finance Research Letters, Elsevier, vol. 59(C).
    12. Rashmi Chaudhary & Priti Bakhshi & Hemendra Gupta, 2020. "Volatility in International Stock Markets: An Empirical Study during COVID-19," JRFM, MDPI, vol. 13(9), pages 1-17, September.
    13. Zhang, Li & Wang, Lu & Nguyen, Thong Trung & Ren, Ruiyi, 2024. "Volatility forecasting of clean energy ETF using GARCH-MIDAS with neural network model," Finance Research Letters, Elsevier, vol. 70(C).
    14. Ao Yang & Qing Ye & Jia Zhai, 2024. "Volatility forecasting with Hybrid‐long short‐term memory models: Evidence from the COVID‐19 period," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(3), pages 2766-2786, July.
    15. Manel Hamdi & Walid Chkili, 2019. "An artificial neural network augmented GARCH model for Islamic stock market volatility: Do asymmetry and long memory matter?," Working Papers 13, Economic Research Forum, revised 21 Aug 2019.
    16. Jia Zhai & Yi Cao & Xuemei Ding, 2018. "Data analytic approach for manipulation detection in stock market," Review of Quantitative Finance and Accounting, Springer, vol. 50(3), pages 897-932, April.
    17. Roy Cerqueti & Mario Maggi & Jessica Riccioni, 2024. "Statistical methods for decision support systems in finance: how Benford’s law predicts financial risk," Annals of Operations Research, Springer, vol. 342(3), pages 1445-1469, November.
    18. Jakub Micha'nk'ow & {L}ukasz Kwiatkowski & Janusz Morajda, 2023. "Combining Deep Learning and GARCH Models for Financial Volatility and Risk Forecasting," Papers 2310.01063, arXiv.org.
    19. Vanshu Mahajan & Sunil Thakan & Aashish Malik, 2022. "Modeling and Forecasting the Volatility of NIFTY 50 Using GARCH and RNN Models," Economies, MDPI, vol. 10(5), pages 1-20, April.
    20. Rayadurgam, Vikram Chandramouli & Mangalagiri, Jayasree, 2023. "Does inclusion of GARCH variance in deep learning models improve financial contagion prediction?," Finance Research Letters, Elsevier, vol. 54(C).

    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:plo:pone00:0297641. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.