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Applications of genetic programming to finance and economics: past, present, future

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Abstract

While the origins of genetic programming (GP) stretch back over 50 years, the field of GP was invigorated by John Koza’s popularisation of the methodology in the 1990s. A particular feature of the GP literature since then has been a strong interest in the application of GP to real-world problem domains. One application domain which has attracted significant attention is that of finance and economics, with several hundred papers from this subfield being listed in the GP bibliography. In this article we outline why finance and economics has been a popular application area for GP and briefly indicate the wide span of this work. However, despite this research effort there is relatively scant evidence of the usage of GP by the mainstream finance community in academia or industry. We speculate why this may be the case, describe what is needed to make this research more relevant from a finance perspective, and suggest some future directions for the application of GP in finance and economics.

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Notes

  1. Of course, industry participants have a good reason to keep successful applications of new technologies secret and this could explain the relative lack of industry practitioners that discuss the use of GP and other advanced methodologies. There are a few notable exceptions, such as Sentient Technologies, which has used evolutionary and deep learning for areas such as e-commerce and trading.

  2. A more detailed discussion on the importance of the appropriate selection of fitness functions takes place in Sect. 3.5.

  3. http://www.coursera.org. Last Accessed: 26 September 2018.

  4. http://www.udacity.com. Last accessed: 26 September 2018.

  5. There are some exceptions, e.g. high-frequency trading hedgefunds, where black box models are becoming more acceptable, especially due to the good performance of algorithms such as deep learning. Nevertheless, the problem remains that there are many other areas in economics and finance that black (or grey) box models are impractical to implement.

  6. http://blogs.wsj.com/marketbeat/2010/05/11/nasdaq-heres-our-timeline-of-the-flash-crash/ Last access: 10 October 2018.

  7. Of course, the significance of the problem should have been vetted by the scientific community; it shouldn’t be left only to the authors of the paper to argue this.

  8. http://www.human-competitive.org/call-for-entries.

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  1. University College Dublin, Dublin, Ireland

    Anthony Brabazon & Michael O’Neill

  2. University of Kent, Medway, UK

    Michael Kampouridis

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Brabazon, A., Kampouridis, M. & O’Neill, M. Applications of genetic programming to finance and economics: past, present, future. Genet Program Evolvable Mach 21, 33–53 (2020). https://doi.org/10.1007/s10710-019-09359-z

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