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Neural network forecasting in prediction Sharpe ratio: Evidence from EU debt market

Author

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  • Vukovic, Darko
  • Vyklyuk, Yaroslav
  • Matsiuk, Natalia
  • Maiti, Moinak

Abstract

This study analyzes a neural networks model that forecast Sharpe ratio. The developed neural networks model is successful to predict the position of the investor who will be rewarded with extra risk premium on debt securities for the same level of portfolio risk or a greater risk premium than proportionate growth risk. The main purpose of the study is to predict highest Sharpe ratio in the future. Study grouped the data on yields of debt instruments in periods before, during and after world crisis. Results shows that neural networks is successful in forecasting nonlinear time lag series with accuracy of 82% on test cases for the prediction of Sharpe-ratio dynamics in future and investor‘s portfolio position.

Suggested Citation

  • Vukovic, Darko & Vyklyuk, Yaroslav & Matsiuk, Natalia & Maiti, Moinak, 2020. "Neural network forecasting in prediction Sharpe ratio: Evidence from EU debt market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    • Handle: RePEc:eee:phsmap:v:542:y:2020:i:c:s0378437119318655
    DOI: 10.1016/j.physa.2019.123331
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    References listed on IDEAS

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    Cited by:

    1. Darko B. Vukovic & Vladislav Ugolnikov & Moinak Maiti, 2021. "Sell‐side analysts' recommendations a value or noise," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 3134-3151, April.
    2. Vukovic, Darko B. & Lapshina, Kseniya A. & Maiti, Moinak, 2021. "Wavelet coherence analysis of returns, volatility and interdependence of the US and the EU money markets: Pre & post crisis," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    3. Varshini, Anu & Kayal, Parthajit & Maiti, Moinak, 2024. "How good are different machine and deep learning models in forecasting the future price of metals? Full sample versus sub-sample," Resources Policy, Elsevier, vol. 92(C).
    4. Park, Hyun Joon & Francisco, Sara Chari & Pang, M. Rosemary & Peng, Lulu & Chi, Guangqing, 2023. "Exposure to anti-Black Lives Matter movement and obesity of the Black population," Social Science & Medicine, Elsevier, vol. 316(C).
    5. Darko Vukovic & Moinak Maiti & Zoran Grubisic & Elena M. Grigorieva & Michael Frömmel, 2021. "COVID-19 Pandemic: Is the Crypto Market a Safe Haven? The Impact of the First Wave," Sustainability, MDPI, vol. 13(15), pages 1-17, July.
    6. Moinak Maiti & Darko Vukovic & Yaroslav Vyklyuk & Zoran Grubisic, 2022. "BRICS Capital Markets Co-Movement Analysis and Forecasting," Risks, MDPI, vol. 10(5), pages 1-13, April.
    7. Li-Chen Cheng & Yu-Hsiang Huang & Ming-Hua Hsieh & Mu-En Wu, 2021. "A Novel Trading Strategy Framework Based on Reinforcement Deep Learning for Financial Market Predictions," Mathematics, MDPI, vol. 9(23), pages 1-16, November.
    8. Darko B. Vukovic & Carlos J. Rincon & Moinak Maiti, 2021. "Price distortions and municipal bonds premiums: evidence from Switzerland," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-21, December.

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    More about this item

    Keywords

    Risk; Returns; Neural networks; Sharpe ratio;
    All these keywords.

    JEL classification:

    • G20 - Financial Economics - - Financial Institutions and Services - - - General
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software

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