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Artificial Intelligence (AI) for Financial Markets: A Good AI for Designing Better Financial Markets and a Bad AI for Manipulating Markets

Part of the Evolutionary Economics and Social Complexity Science book series (EESCS,volume 28)


I introduced a good AI for finance, an artificial market (an agent-based model for a financial market) to design a financial market that works well, and a bad AI for finance, an AI trader that discovers how to manipulate markets through learning in an artificial market. First, I describe an artificial market for designing a financial market that works well. Artificial markets have recently been used to develop and examine rules and regulations such as tick size reduction in actual financial markets. Their influence is growing. A good market design is important for developing and maintaining an advanced economy. Second, I show that an AI trader that learns in an artificial market can discover how to manipulate a market and use that capability as an optimal investment strategy even when the developer of the AI has no such intention, and the result suggests the need for regulation. These stories show that there are both good and bad AIs for society. We should not discuss whether AIs on the whole are good or bad, but rather how to manage them so that they are useful tools. To do that, we should discuss rules, ethics, and philosophy for AIs.


  • Agent-based model
  • Artificial market model
  • Multi-agent simulation
  • Market design
  • Market manipulation
  • Genetic algorithm

Note that the opinions contained herein are solely those of the authors and do not necessarily reflect those of SPARX Asset Management Co., Ltd.

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  1. 1.

    Sabzian et al. provide a comprehensive review of agent-based models for complex systems (Sabzian et al. 2018).

  2. 2.

    A stylized fact is a term used in economics to refer to empirical findings that are so consistent (for example, across a wide range of instruments, markets, and time periods) that they are accepted as truth (Sewell 2011).

  3. 3.

  4. 4.

    This enables us to focus on phenomena on short time scales, as the fundamental price remains static.

  5. 5.

    When t < τ j, however, I set the second term in Eq. (13.1) to 0.

  6. 6.

    When t < t c, however, to generate enough waiting orders, the agent places an order to buy one share when \(P_f>P^t_{o,j}\) or to sell one share when \(P_f<P^t_{o,j}\).

  7. 7.

    Darley and Outkin (2007) investigated tick size reduction using an artificial market model when NASDAQ, a stock exchange in the USA, was planning a tick size reduction. They showed, for example, that a market can become unstable when some investment strategies become more prevalent. However, their model had too many parameters and they focused on which investors earned more, which prevented them from examining factors contributing good design of financial markets.

  8. 8. It was originally scheduled to be held in New York but was held virtually.

  9. 9.

    I presented there together with co-author Yagi et al. (2020).

  10. 10.

    A genetic algorithm is a calculation method inspired by evolution and natural selection that searches for an approximate optimal solution. Input values are represented as genes, and a surviving gene that has a higher adaptability (output value) leads to an optimal solution, that is, the input value that results in the highest output value (Goldberg 1989).

  11. 11.

    In reality, the builder always intends certain strategies to be selected when modeling possible strategies. However, it is crucial for this study that the developer has no intention of any strategies including market manipulation. Therefore, I did not intentionally model trading strategies, and my model directly searched for all the optimal trades in an artificial market environment. Because there are no models of trading strategies, my model does not output in an out-sample forecast; thus, no one can test my model in an out-sample forecast. I argue, however, that this study does not need such evaluations because it focuses on whether an AI trader can discover market manipulation strategy through its own learning despite the builder having no intention of market manipulation. This study does not aim to use the model to out-sample forecast.

  12. 12.

    AIA does not take any action before tick time t c to stabilize the simulations. As mentioned in footnote 7, the purpose of the period before t c is to generate enough waiting orders.


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Mizuta, T. (2022). Artificial Intelligence (AI) for Financial Markets: A Good AI for Designing Better Financial Markets and a Bad AI for Manipulating Markets. In: Aruka, Y. (eds) Digital Designs for Money, Markets, and Social Dilemmas. Evolutionary Economics and Social Complexity Science, vol 28. Springer, Singapore.

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