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Machine learning solutions to challenges in finance: An application to the pricing of financial products

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  • Gan, Lirong
  • Wang, Huamao
  • Yang, Zhaojun

Abstract

The recent fast development of machine learning provides new tools to solve challenges in many areas. In finance, average options are popular financial products among corporations, institutional investors, and individual investors for risk management and investment because average options have the advantages of cheap prices and their payoffs are not very sensitive to the changes of the underlying asset prices at the maturity date, avoiding the manipulation of asset prices and option prices. The challenge is that pricing arithmetic average options requires traditional numerical methods with the drawbacks of expensive repetitive computations and non-realistic model assumptions. This paper proposes a machine-learning method to price arithmetic and geometric average options accurately and in particular quickly. The method is model-free and it is verified by empirical applications as well as numerical experiments.

Suggested Citation

  • Gan, Lirong & Wang, Huamao & Yang, Zhaojun, 2020. "Machine learning solutions to challenges in finance: An application to the pricing of financial products," Technological Forecasting and Social Change, Elsevier, vol. 153(C).
    • Handle: RePEc:eee:tefoso:v:153:y:2020:i:c:s0040162519312399
    DOI: 10.1016/j.techfore.2020.119928
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    References listed on IDEAS

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    Cited by:

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    3. Zhang, Hong & Nguyen, Hoang & Vu, Diep-Anh & Bui, Xuan-Nam & Pradhan, Biswajeet, 2021. "Forecasting monthly copper price: A comparative study of various machine learning-based methods," Resources Policy, Elsevier, vol. 73(C).
    4. Meadows, Maureen & Merendino, Alessandro & Dibb, Sally & Garcia-Perez, Alexeis & Hinton, Matthew & Papagiannidis, Savvas & Pappas, Ilias & Wang, Huamao, 2022. "Tension in the data environment: How organisations can meet the challenge," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    5. Jie Fang & Shutao Xia & Jianwu Lin & Yong Jiang, 2019. "Automatic Financial Feature Construction," Papers 1912.06236, arXiv.org, revised Oct 2020.
    6. Caterina De Lucia & Pasquale Pazienza & Mark Bartlett, 2020. "Does Good ESG Lead to Better Financial Performances by Firms? Machine Learning and Logistic Regression Models of Public Enterprises in Europe," Sustainability, MDPI, vol. 12(13), pages 1-29, July.
    7. José Dias Curto, 2022. "Averages: There is Still Something to Learn," Computational Economics, Springer;Society for Computational Economics, vol. 60(2), pages 755-779, August.
    8. Manickavasagam, Jeevananthan & Visalakshmi, S. & Apergis, Nicholas, 2020. "A novel hybrid approach to forecast crude oil futures using intraday data," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    9. Hector O. Zapata & Supratik Mukhopadhyay, 2022. "A Bibliometric Analysis of Machine Learning Econometrics in Asset Pricing," JRFM, MDPI, vol. 15(11), pages 1-17, November.
    10. Wang, Huamao & Yao, Yumei & Salhi, Said, 2020. "Tension in big data using machine learning: Analysis and applications," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    11. Herrera, Rubén & Climent, Francisco & Carmona, Pedro & Momparler, Alexandre, 2022. "The manipulation of Euribor: An analysis with machine learning classification techniques," Technological Forecasting and Social Change, Elsevier, vol. 176(C).

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