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Informing a Financial Market

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Part of the Studies in Computational Intelligence book series (SCI,volume 990)

Abstract

This article reports on a scheme to exploit human computation and data science for fostering financial markets’ efficiency. The scheme consists in incentivizing market’s agents to assess their own risk of defaulting to financial contracts (that is, human computation) and in an eigenvector model of credit risk (that is, data science) aggregating agents’ assessments of their defaulting risk estimates into a global, or systemic, credit risk rating. The systemic credit risk rating in turn informs the market’s agents what nudges them to decisions beneficial not only to themselves, but also to the market as a whole.

Keywords

  • Credit risk rating
  • Human computation
  • Data science

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  • DOI: 10.1007/978-3-030-75583-6_23
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Notes

  1. 1.

    “Collective intelligence” is understood in the sense of Condorcet’s jury theorem [10] and of Galton’s observation on the “trustworthiness of popular judgments” [11].

  2. 2.

    Derivative contracts like swaps do not specify which contract party will be the debtor and which will be the creditor, hence the phrase “(actual of potential) debtor”.

  3. 3.

    The European Sovereign-Debt Crisis since 2009 can be seen as an evidence of this backflow through a currency of credit risk [13].

  4. 4.

    It is because of these paths that central banks can control the monetary base.

  5. 5.

    The asymmetric relations referred to in this article are those specified by networks’ directed edges.

  6. 6.

    Superscript t denotes time, not matrix transposition which is denoted, as usual, by superscript T.

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Correspondence to François Bry .

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Bry, F. (2021). Informing a Financial Market. In: Bucciarelli, E., Chen, SH., Corchado, J.M., Parra D., J. (eds) Decision Economics: Minds, Machines, and their Society. DECON 2020. Studies in Computational Intelligence, vol 990. Springer, Cham. https://doi.org/10.1007/978-3-030-75583-6_23

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