IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i5p1134-d1079426.html
   My bibliography  Save this article

A New Dual Normalization for Enhancing the Bitcoin Pricing Capability of an Optimized Low Complexity Neural Net with TOPSIS Evaluation

Author

Listed:
  • Samuka Mohanty

    (Department of Computer Science and Engineering, Siksha ‘O’ Anusandhan (Deemed to be) University, Bhubaneswar 751030, Odisha, India)

  • Rajashree Dash

    (Department of Computer Science and Engineering, Siksha ‘O’ Anusandhan (Deemed to be) University, Bhubaneswar 751030, Odisha, India)

Abstract

Bitcoin, the largest cryptocurrency, is extremely volatile and hence needs a better model for its pricing. In the literature, many researchers have studied the effect of data normalization on regression analysis for stock price prediction. How has data normalization affected Bitcoin price prediction? To answer this question, this study analyzed the prediction accuracy of a Legendre polynomial-based neural network optimized by the mutated climb monkey algorithm using nine existing data normalization techniques. A new dual normalization technique was proposed to improve the efficiency of this model. The 10 normalization techniques were evaluated using 15 error metrics using a multi-criteria decision-making (MCDM) approach called technique for order performance by similarity to ideal solution (TOPSIS). The effect of the top three normalization techniques along with the min–max normalization was further studied for Chebyshev, Laguerre, and trigonometric polynomial-based neural networks in three different datasets. The prediction accuracy of the 16 models (each of the four polynomial-based neural networks with four different normalization techniques) was calculated using 15 error metrics. A 16 × 15 TOPSIS analysis was conducted to rank the models. The convergence plot and the ranking of the models indicated that data normalization plays a significant role in the prediction capability of a Bitcoin price predictor. This paper can significantly contribute to the research with a new normalization technique for utilization in varied fields of research. It can also contribute to international finance as a decision-making tool for different investors as well as stakeholders for Bitcoin pricing.

Suggested Citation

  • Samuka Mohanty & Rajashree Dash, 2023. "A New Dual Normalization for Enhancing the Bitcoin Pricing Capability of an Optimized Low Complexity Neural Net with TOPSIS Evaluation," Mathematics, MDPI, vol. 11(5), pages 1-28, February.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:5:p:1134-:d:1079426
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/5/1134/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/5/1134/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Liu, Mingxi & Li, Guowen & Li, Jianping & Zhu, Xiaoqian & Yao, Yinhong, 2021. "Forecasting the price of Bitcoin using deep learning," Finance Research Letters, Elsevier, vol. 40(C).
    2. Ivan Izonin & Roman Tkachenko & Nataliya Shakhovska & Bohdan Ilchyshyn & Krishna Kant Singh, 2022. "A Two-Step Data Normalization Approach for Improving Classification Accuracy in the Medical Diagnosis Domain," Mathematics, MDPI, vol. 10(11), pages 1-18, June.
    3. Aggarwal, Divya & Chandrasekaran, Shabana & Annamalai, Balamurugan, 2020. "A complete empirical ensemble mode decomposition and support vector machine-based approach to predict Bitcoin prices," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    4. Shanker, M. & Hu, M. Y. & Hung, M. S., 1996. "Effect of data standardization on neural network training," Omega, Elsevier, vol. 24(4), pages 385-397, August.
    5. Andreas-Daniel Cocis & Larissa Batrancea & Horia Tulai, 2021. "The Link between Corporate Reputation and Financial Performance and Equilibrium within the Airline Industry," Mathematics, MDPI, vol. 9(17), pages 1-12, September.
    6. Subhranginee Das & Sarat Chandra Nayak & Biswajit Sahoo, 2022. "Towards Crafting Optimal Functional Link Artificial Neural Networks with Rao Algorithms for Stock Closing Prices Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 60(1), pages 1-23, June.
    7. Larissa Batrancea, 2021. "The Influence of Liquidity and Solvency on Performance within the Healthcare Industry: Evidence from Publicly Listed Companies," Mathematics, MDPI, vol. 9(18), pages 1-15, September.
    8. Steinmetz, Fred & von Meduna, Marc & Ante, Lennart & Fiedler, Ingo, 2021. "Ownership, uses and perceptions of cryptocurrency: Results from a population survey," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    9. Larissa M. Batrancea & Anca Nichita & Andreas-Daniel Cocis, 2022. "Financial Performance and Sustainable Corporate Reputation: Empirical Evidence from the Airline Business," Sustainability, MDPI, vol. 14(20), pages 1-15, October.
    10. Syed Abul, Basher & Perry, Sadorsky, 2022. "Forecasting Bitcoin price direction with random forests: How important are interest rates, inflation, and market volatility?," MPRA Paper 113293, University Library of Munich, Germany.
    11. Samuka Mohanty & Rajashree Dash, 2022. "Neural Network-Based Bitcoin Pricing Using a New Mutated Climb Monkey Algorithm with TOPSIS Analysis for Sustainable Development," Mathematics, MDPI, vol. 10(22), pages 1-23, November.
    12. Esra’a Alshdaifat & Doa’a Alshdaifat & Ayoub Alsarhan & Fairouz Hussein & Subhieh Moh’d Faraj S. El-Salhi, 2021. "The Effect of Preprocessing Techniques, Applied to Numeric Features, on Classification Algorithms’ Performance," Data, MDPI, vol. 6(2), pages 1-23, January.
    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. Samuka Mohanty & Rajashree Dash, 2022. "Neural Network-Based Bitcoin Pricing Using a New Mutated Climb Monkey Algorithm with TOPSIS Analysis for Sustainable Development," Mathematics, MDPI, vol. 10(22), pages 1-23, November.
    2. Goodell, John W. & Ben Jabeur, Sami & Saâdaoui, Foued & Nasir, Muhammad Ali, 2023. "Explainable artificial intelligence modeling to forecast bitcoin prices," International Review of Financial Analysis, Elsevier, vol. 88(C).
    3. Hajek, Petr & Hikkerova, Lubica & Sahut, Jean-Michel, 2023. "How well do investor sentiment and ensemble learning predict Bitcoin prices?," Research in International Business and Finance, Elsevier, vol. 64(C).
    4. Lin, Chien-An & Bates, Timothy C., 2022. "Smart people know how the economy works: Cognitive ability, economic knowledge and financial literacy," Intelligence, Elsevier, vol. 93(C).
    5. Matteo Picozzi & Antonio Giovanni Iaccarino, 2021. "Forecasting the Preparatory Phase of Induced Earthquakes by Recurrent Neural Network," Forecasting, MDPI, vol. 3(1), pages 1-20, January.
    6. Luis Alberto Geraldo-Campos & Juan J. Soria & Tamara Pando-Ezcurra, 2022. "Machine Learning for Credit Risk in the Reactive Peru Program: A Comparison of the Lasso and Ridge Regression Models," Economies, MDPI, vol. 10(8), pages 1-21, July.
    7. Namryoung Lee, 2023. "The Relationship between a Company’s Cryptocurrency Holdings and Its Sustainable Performance—With a Focus on External and Internal Financial Issues and Cash," Sustainability, MDPI, vol. 15(23), pages 1-15, November.
    8. Pawan Kumar Singh & Alok Kumar Pandey & S. C. Bose, 2023. "A new grey system approach to forecast closing price of Bitcoin, Bionic, Cardano, Dogecoin, Ethereum, XRP Cryptocurrencies," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(3), pages 2429-2446, June.
    9. Peter Brusov & Tatiana Filatova & Natali Orekhova, 2023. "Influence of Method and Frequency of Profit Tax Payments on Company Financial Indicators," Springer Books, in: The Brusov–Filatova–Orekhova Theory of Capital Structure, chapter 0, pages 241-264, Springer.
    10. António Portugal Duarte & Fátima Sol Murta & Nuno Baetas da Silva & Beatriz Rodrigues Vieira, 2023. "Flip the Coin: Heads, Tails or Cryptocurrencies?," Scientific Annals of Economics and Business (continues Analele Stiintifice), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 70(SI), pages 1-18, February.
    11. Jingjing Liu & Jing Wang & Tianlin Zhai & Zehui Li, 2022. "The Response of Ecologically Functional Land to Changes in Urban Economic Growth and Transportation Construction in China," IJERPH, MDPI, vol. 19(21), pages 1-17, November.
    12. Lili Pan & Lin Wang & Qianqian Feng, 2022. "A Bibliometric Analysis of Risk Management in Foreign Direct Investment: Insights and Implications," Sustainability, MDPI, vol. 14(12), pages 1-18, June.
    13. Naseh Majidi & Mahdi Shamsi & Farokh Marvasti, 2022. "Algorithmic Trading Using Continuous Action Space Deep Reinforcement Learning," Papers 2210.03469, arXiv.org.
    14. Dong-Her Shih & Ting-Wei Wu & Po-Yuan Shih & Nai-An Lu & Ming-Hung Shih, 2022. "A Framework of Global Credit-Scoring Modeling Using Outlier Detection and Machine Learning in a P2P Lending Platform," Mathematics, MDPI, vol. 10(13), pages 1-13, June.
    15. Jinghua Wang & Geoffrey M. Ngene & Yan Shi & Ann Nduati Mungai, 2023. "An Investigation of the Predictability of Uncertainty Indices on Bitcoin Returns," JRFM, MDPI, vol. 16(10), pages 1-12, October.
    16. Goodell, John W. & Kumar, Satish & Lim, Weng Marc & Pattnaik, Debidutta, 2021. "Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    17. Joana Dias & Humberto Rocha & Brígida Ferreira & Maria Lopes, 2014. "A genetic algorithm with neural network fitness function evaluation for IMRT beam angle optimization," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 22(3), pages 431-455, September.
    18. Yasmeen Idilbi-Bayaa & Mahmoud Qadan, 2022. "Tell Me Why I Do Not Like Mondays," Mathematics, MDPI, vol. 10(11), pages 1-22, May.
    19. Dhoha Mellouli Ellouz Siwar, 2023. "Dynamical Linkages and Frequency Spillovers between Crude Oil and Stock Markets in BRICS During Turbulent and Tranquil Times," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(3), pages 77-96.
    20. Hao, Jun & Feng, Qianqian & Yuan, Jiaxin & Sun, Xiaolei & Li, Jianping, 2022. "A dynamic ensemble learning with multi-objective optimization for oil prices prediction," Resources Policy, Elsevier, vol. 79(C).

    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:11:y:2023:i:5:p:1134-:d:1079426. 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.