IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i8p1231-d789775.html
   My bibliography  Save this article

A Correlation-Embedded Attention Module to Mitigate Multicollinearity: An Algorithmic Trading Application

Author

Listed:
  • Jireh Yi-Le Chan

    (Faculty of Business and Finance, University Tunku Abdul Rahman, Perak 31900, Malaysia
    These authors contributed equally to this work.)

  • Steven Mun Hong Leow

    (Faculty of Business and Finance, University Tunku Abdul Rahman, Perak 31900, Malaysia
    These authors contributed equally to this work.)

  • Khean Thye Bea

    (Faculty of Business and Finance, University Tunku Abdul Rahman, Perak 31900, Malaysia)

  • Wai Khuen Cheng

    (Faculty of Information and Communication Technology, University Tunku Abdul Rahman, Perak 31900, Malaysia)

  • Seuk Wai Phoong

    (Department of Operation and Management Information System, Faculty of Business and Accountancy, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Zeng-Wei Hong

    (Department of Information Engineering and Computer Science, Feng Chia University, Taichung 40724, Taiwan)

  • Jim-Min Lin

    (Department of Information Engineering and Computer Science, Feng Chia University, Taichung 40724, Taiwan)

  • Yen-Lin Chen

    (Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 106344, Taiwan)

Abstract

Algorithmic trading is a common topic researched in the neural network due to the abundance of data available. It is a phenomenon where an approximately linear relationship exists between two or more independent variables. It is especially prevalent in financial data due to the interrelated nature of the data. The existing feature selection methods are not efficient enough in solving such a problem due to the potential loss of essential and relevant information. These methods are also not able to consider the interaction between features. Therefore, we proposed two improvements to apply to the Long Short-Term Memory neural network (LSTM) in this study. It is the Multicollinearity Reduction Module (MRM) based on correlation-embedded attention to mitigate multicollinearity without removing features. The motivation of the improvements is to allow the model to predict using the relevance and redundancy within the data. The first contribution of the paper is allowing a neural network to mitigate the effects of multicollinearity without removing any variables. The second contribution is improving trading returns when our proposed mechanisms are applied to an LSTM. This study compared the classification performance between LSTM models with and without the correlation-embedded attention module. The experimental result reveals that a neural network that can learn the relevance and redundancy of the financial data to improve the desired classification performance. Furthermore, the trading returns of our proposed module are 46.82% higher without sacrificing training time. Moreover, the MRM is designed to be a standalone module and is interoperable with existing models.

Suggested Citation

  • Jireh Yi-Le Chan & Steven Mun Hong Leow & Khean Thye Bea & Wai Khuen Cheng & Seuk Wai Phoong & Zeng-Wei Hong & Jim-Min Lin & Yen-Lin Chen, 2022. "A Correlation-Embedded Attention Module to Mitigate Multicollinearity: An Algorithmic Trading Application," Mathematics, MDPI, vol. 10(8), pages 1-13, April.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:8:p:1231-:d:789775
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/8/1231/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/8/1231/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lucey, Brian M. & Muckley, Cal, 2011. "Robust global stock market interdependencies," International Review of Financial Analysis, Elsevier, vol. 20(4), pages 215-224, August.
    2. Sima Siami-Namini & Akbar Siami Namin, 2018. "Forecasting Economics and Financial Time Series: ARIMA vs. LSTM," Papers 1803.06386, arXiv.org.
    3. Jacinta Chan Phooi M'ng & Azmin Azliza Aziz, 2016. "Using Neural Networks to Enhance Technical Trading Rule Returns: A Case with KLCI," Athens Journal of Business & Economics, Athens Institute for Education and Research (ATINER), vol. 2(1), pages 63-70, January.
    4. O. B. Sezer & M. Ozbayoglu & E. Dogdu, 2017. "An Artificial Neural Network-based Stock Trading System Using Technical Analysis and Big Data Framework," Papers 1712.09592, arXiv.org.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jireh Yi-Le Chan & Seuk Wai Phoong & Wai Khuen Cheng & Yen-Lin Chen, 2022. "Support Resistance Levels towards Profitability in Intelligent Algorithmic Trading Models," Mathematics, MDPI, vol. 10(20), pages 1-17, October.
    2. Wai Khuen Cheng & Khean Thye Bea & Steven Mun Hong Leow & Jireh Yi-Le Chan & Zeng-Wei Hong & Yen-Lin Chen, 2022. "A Review of Sentiment, Semantic and Event-Extraction-Based Approaches in Stock Forecasting," Mathematics, MDPI, vol. 10(14), pages 1-20, July.

    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. Adamantios Ntakaris & Giorgio Mirone & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2019. "Feature Engineering for Mid-Price Prediction with Deep Learning," Papers 1904.05384, arXiv.org, revised Jun 2019.
    2. Anatoly A. Peresetsky & Ruslan I. Yakubov, 2017. "Autocorrelation in an unobservable global trend: does it help to forecast market returns?," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 7(1/2), pages 152-169.
    3. Fuinhas, José Alberto & Marques, António Cardoso & Nogueira, David Coito, 2014. "Análise VAR dos índices bolsistas SP500, FTSE100, PSI20, HSI e IBOVESPA [Integration of the indexes SP500, FTSE100, PSI20, HSI and IBOVESPA: A VAR approach]," MPRA Paper 62092, University Library of Munich, Germany, revised 10 Feb 2015.
    4. Masum, Mohammad & Masud, M.A. & Adnan, Muhaiminul Islam & Shahriar, Hossain & Kim, Sangil, 2022. "Comparative study of a mathematical epidemic model, statistical modeling, and deep learning for COVID-19 forecasting and management," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    5. Sima Siami‐Namini & Darren Hudson & Adao Alexandre Trindade & Conrad Lyford, 2019. "Commodity price volatility and U.S. monetary policy: Commodity price overshooting revisited," Agribusiness, John Wiley & Sons, Ltd., vol. 35(2), pages 200-218, April.
    6. Aniruddha Dutta & Saket Kumar & Meheli Basu, 2020. "A Gated Recurrent Unit Approach to Bitcoin Price Prediction," JRFM, MDPI, vol. 13(2), pages 1-16, February.
    7. Wan, Xiaoli & Yan, Yuruo & Zeng, Zhixiong, 2020. "Exchange rate regimes and market integration: evidence from the dynamic relations between renminbi onshore and offshore markets," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    8. Sima Siami-Namini & Daniel Muhammad & Fahad Fahimullah, 2018. "The Short and Long Run Effects of Selected Variables on Tax Revenue - A Case Study," Applied Economics and Finance, Redfame publishing, vol. 5(5), pages 23-32, September.
    9. Pick-Soon Ling & Ruzita Abdul-Rahim, 2017. "Market Efficiency Based on Unconventional Technical Trading Strategies in Malaysian Stock Market," International Journal of Economics and Financial Issues, Econjournals, vol. 7(3), pages 88-96.
    10. de Lucio, Juan, 2021. "Estimación adelantada del crecimiento regional mediante redes neuronales LSTM," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, issue 49, pages 45-64.
    11. Sadefo Kamdem, Jules & Bandolo Essomba, Rose & Njong Berinyuy, James, 2020. "Deep learning models for forecasting and analyzing the implications of COVID-19 spread on some commodities markets volatilities," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    12. Xiangzhou Chen & Zhi Long, 2023. "E-Commerce Enterprises Financial Risk Prediction Based on FA-PSO-LSTM Neural Network Deep Learning Model," Sustainability, MDPI, vol. 15(7), pages 1-17, March.
    13. Mehmet Sahiner & David G. McMillan & Dimos Kambouroudis, 2023. "Do artificial neural networks provide improved volatility forecasts: Evidence from Asian markets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 47(3), pages 723-762, September.
    14. Guoteng Xu & Shuai Peng & Chengjiang Li & Xia Chen, 2023. "Synergistic Evolution of China’s Green Economy and Digital Economy Based on LSTM-GM and Grey Absolute Correlation," Sustainability, MDPI, vol. 15(19), pages 1-29, September.
    15. Himanshu Gupta & Aditya Jaiswal, 2024. "A Study on Stock Forecasting Using Deep Learning and Statistical Models," Papers 2402.06689, arXiv.org.
    16. Faten Ben Slimane & Mohamed Mehanaoui & Irfan A. Kazi, 2014. "Interdependency and Spillover during the Financial Crisis of 2007 to 2009 – Evidence from High Frequency Intraday Data," Working Papers 2014-126, Department of Research, Ipag Business School.
    17. Pushpendu Ghosh & Ariel Neufeld & Jajati Keshari Sahoo, 2020. "Forecasting directional movements of stock prices for intraday trading using LSTM and random forests," Papers 2004.10178, arXiv.org, revised Jun 2021.
    18. Mourad Mroua & Ahlem Lamine, 2023. "Financial time series prediction under Covid-19 pandemic crisis with Long Short-Term Memory (LSTM) network," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-15, December.
    19. Chen, Mei-Ping & Chen, Pei-Fen & Lee, Chien-Chiang, 2014. "Frontier stock market integration and the global financial crisis," The North American Journal of Economics and Finance, Elsevier, vol. 29(C), pages 84-103.
    20. Abu Bakar, Norhidayah & Masih, Abul Mansur M., 2014. "The Dynamic Linkages between Islamic Index and the Major Stock Markets: New Evidence from Wavelet time-scale decomposition Analysis," MPRA Paper 56977, University Library of Munich, Germany.

    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:gam:jmathe:v:10:y:2022:i:8:p:1231-:d:789775. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.