IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2311.09148.html
   My bibliography  Save this paper

Predicting risk/reward ratio in financial markets for asset management using machine learning

Author

Listed:
  • Reza Yarbakhsh
  • Mahdieh Soleymani Baghshah
  • Hamidreza Karimaghaie

Abstract

Financial market forecasting remains a formidable challenge despite the surge in computational capabilities and machine learning advancements. While numerous studies have underscored the precision of computer-generated market predictions, many of these forecasts fail to yield profitable trading outcomes. This discrepancy often arises from the unpredictable nature of profit and loss ratios in the event of successful and unsuccessful predictions. In this study, we introduce a novel algorithm specifically designed for forecasting the profit and loss outcomes of trading activities. This is further augmented by an innovative approach for integrating these forecasts with previous predictions of market trends. This approach is designed for algorithmic trading, enabling traders to assess the profitability of each trade and calibrate the optimal trade size. Our findings indicate that this method significantly improves the performance of traditional trading strategies as well as algorithmic trading systems, offering a promising avenue for enhancing trading decisions.

Suggested Citation

  • Reza Yarbakhsh & Mahdieh Soleymani Baghshah & Hamidreza Karimaghaie, 2023. "Predicting risk/reward ratio in financial markets for asset management using machine learning," Papers 2311.09148, arXiv.org.
    • Handle: RePEc:arx:papers:2311.09148
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2311.09148
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cheol‐Ho Park & Scott H. Irwin, 2007. "What Do We Know About The Profitability Of Technical Analysis?," Journal of Economic Surveys, Wiley Blackwell, vol. 21(4), pages 786-826, September.
    2. Abhyankar, A & Copeland, L S & Wong, W, 1997. "Uncovering Nonlinear Structure in Real-Time Stock-Market Indexes: The S&P 500, the DAX, the Nikkei 225, and the FTSE-100," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 1-14, January.
    3. Ozkan, Oktay, 2021. "Impact of COVID-19 on stock market efficiency: Evidence from developed countries," Research in International Business and Finance, Elsevier, vol. 58(C).
    4. Broadstock, David C. & Zhang, Dayong, 2019. "Social-media and intraday stock returns: The pricing power of sentiment," Finance Research Letters, Elsevier, vol. 30(C), pages 116-123.
    5. Hillier, David & Loncan, Tiago, 2019. "Political uncertainty and Stock returns: Evidence from the Brazilian Political Crisis," Pacific-Basin Finance Journal, Elsevier, vol. 54(C), pages 1-12.
    6. Matkovskyy, Roman & Jalan, Akanksha, 2019. "From financial markets to Bitcoin markets: A fresh look at the contagion effect," Finance Research Letters, Elsevier, vol. 31(C), pages 93-97.
    7. Rose D. Baker & Ian G. McHale, 2013. "Optimal Betting Under Parameter Uncertainty: Improving the Kelly Criterion," Decision Analysis, INFORMS, vol. 10(3), pages 189-199, September.
    8. Zexin Hu & Yiqi Zhao & Matloob Khushi, 2021. "A Survey of Forex and Stock Price Prediction Using Deep Learning," Papers 2103.09750, arXiv.org.
    9. Rui Dias & Nuno Teixeira & Veronika Machova & Pedro Pardal & Jakub Horak & Marek Vochozka, 2020. "Random walks and market efficiency tests: evidence on US, Chinese and European capital markets within the context of the global Covid-19 pandemic," Oeconomia Copernicana, Institute of Economic Research, vol. 11(4), pages 585-608, December.
    10. Maasoumi, Esfandiar & Racine, Jeff, 2002. "Entropy and predictability of stock market returns," Journal of Econometrics, Elsevier, vol. 107(1-2), pages 291-312, March.
    11. Dixon, Matthew & Klabjan, Diego & Bang, Jin Hoon, 2017. "Classification-based financial markets prediction using deep neural networks," Algorithmic Finance, IOS Press, vol. 6(3-4), pages 67-77.
    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. Bock, J. & Geissel, S., 2024. "Evolution of stock market efficiency in Europe: Evidence from measuring periods of inefficiency," Finance Research Letters, Elsevier, vol. 62(PA).
    2. Daouda Lawa Tan Toe & Mamadou Toe & Tibi Didier Zoungrana, 2023. "Investigating the weak and semi-strong forms of Informational Efficiency on the West African Economic and Monetary Union’s Stock Exchange (BRVM) through returns predictability tests," SN Business & Economics, Springer, vol. 3(9), pages 1-27, September.
    3. Yuze Lu & Hailong Zhang & Qiwen Guo, 2023. "Stock and market index prediction using Informer network," Papers 2305.14382, arXiv.org.
    4. Andrew Phiri, 2022. "Changing efficiency of BRICS currency markets during the COVID-19 pandemic," Economic Change and Restructuring, Springer, vol. 55(3), pages 1673-1699, August.
    5. Deniz Erer & Elif Erer & Selim Güngör, 2023. "The aggregate and sectoral time-varying market efficiency during crisis periods in Turkey: a comparative analysis with COVID-19 outbreak and the global financial crisis," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-25, December.
    6. Müller, Fernanda Maria & Santos, Samuel Solgon & Righi, Marcelo Brutti, 2023. "A description of the COVID-19 outbreak role in financial risk forecasting," The North American Journal of Economics and Finance, Elsevier, vol. 66(C).
    7. Shyh-Wei Chen, 2008. "Non-stationarity and Non-linearity in Stock Prices: Evidence from the OECD Countries," Economics Bulletin, AccessEcon, vol. 3(11), pages 1-11.
    8. Gruber, Lutz F. & West, Mike, 2017. "Bayesian online variable selection and scalable multivariate volatility forecasting in simultaneous graphical dynamic linear models," Econometrics and Statistics, Elsevier, vol. 3(C), pages 3-22.
    9. Jitka Veselá & Alžběta Zíková, 2022. "Are the Czech, Polish, German and Dutch markets taking a random walk? [Konají český, polský, německý a nizozemský trh náhodnou procházku?]," Český finanční a účetní časopis, Prague University of Economics and Business, vol. 2022(2), pages 19-38.
    10. Barge-Gil, Andrés & García-Hiernaux, Alfredo, 2019. "Staking plans in sports betting under unknown true probabilities of the event," MPRA Paper 92196, University Library of Munich, Germany.
    11. Goodell, John W. & Goutte, Stephane, 2021. "Co-movement of COVID-19 and Bitcoin: Evidence from wavelet coherence analysis," Finance Research Letters, Elsevier, vol. 38(C).
    12. Parthajit Kayal & Purnima Rohilla, 2021. "Bitcoin in the economics and finance literature: a survey," SN Business & Economics, Springer, vol. 1(7), pages 1-21, July.
    13. Salois, Matthew & Moss, Charles, 2010. "An Information Approach to the Dynamics in Farm Income: Implications for Farmland Markets," MPRA Paper 26850, University Library of Munich, Germany.
    14. Hongcheng Ding & Xuanze Zhao & Zixiao Jiang & Shamsul Nahar Abdullah & Deshinta Arrova Dewi, 2024. "EUR-USD Exchange Rate Forecasting Based on Information Fusion with Large Language Models and Deep Learning Methods," Papers 2408.13214, arXiv.org.
    15. Tsung-Jui Chiang-Lin & Yong-Shiuan Lee & Tzong-Hann Shieh & Chien-Chang Yen & Shang-Yueh Tsai, 2022. "Study of Asian indexes by a newly derived dynamic model," PLOS ONE, Public Library of Science, vol. 17(5), pages 1-19, May.
    16. Dionisio, Andreia & Menezes, Rui & Mendes, Diana & Vidigal Da Silva, Jacinto, 2007. "Nonlinear Dynamics Within Macroeconomic Factors And Stock Market In Portugal, 1993-2003," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 7(2), pages 57-70.
    17. Stefania D'Amico, 2004. "Density Estimation and Combination under Model Ambiguity," Computing in Economics and Finance 2004 273, Society for Computational Economics.
    18. Darren Duxbury & Robert Hudson & Kevin Keasey & Zhishu Yang & Songyao Yao, 2013. "How prior realized outcomes affect portfolio decisions," Review of Quantitative Finance and Accounting, Springer, vol. 41(4), pages 611-629, November.
    19. Pojarliev, Momtchil & Levich, Richard M., 2010. "Trades of the living dead: Style differences, style persistence and performance of currency fund managers," Journal of International Money and Finance, Elsevier, vol. 29(8), pages 1752-1775, December.
    20. Aamir Aijaz Syed & Muhammad Abdul Kamal, 2024. "Do cryptocurrency and commodities markets affect stock market performance in South Asia? An empirical investigation during the COVID-19 pandemic," International Journal of Economics and Business Research, Inderscience Enterprises Ltd, vol. 27(4), pages 673-692.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:2311.09148. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.