Estimating the Likelihood of Financial Behaviours Using Nearest Neighbors

Tiago Mendes-Neves (), Diogo Seca, Ricardo Sousa, Cláudia Ribeiro and João Mendes-Moreira
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Tiago Mendes-Neves: LIAAD-INESC TEC - Laboratory of Artificial Intelligence and Decision Support
Diogo Seca: LIAAD-INESC TEC - Laboratory of Artificial Intelligence and Decision Support
Ricardo Sousa: LIAAD-INESC TEC - Laboratory of Artificial Intelligence and Decision Support
Cláudia Ribeiro: cefUP - Centro de Economias e Finanças da UP
João Mendes-Moreira: LIAAD-INESC TEC - Laboratory of Artificial Intelligence and Decision Support

Computational Economics, 2024, vol. 63, issue 4, No 7, 1477-1491

Abstract: Abstract As many automated algorithms find their way into the IT systems of the banking sector, having a way to validate and interpret the results from these algorithms can lead to a substantial reduction in the risks associated with automation. Usually, validating these pricing mechanisms requires human resources to manually analyze and validate large quantities of data. There is a lack of effective methods that analyze the time series and understand if what is currently happening is plausible based on previous data, without information about the variables used to calculate the price of the asset. This paper describes an implementation of a process that allows us to validate many data points automatically. We explore the K-Nearest Neighbors algorithm to find coincident patterns in financial time series, allowing us to detect anomalies, outliers, and data points that do not follow normal behavior. This system allows quicker detection of defective calculations that would otherwise result in the incorrect pricing of financial assets. Furthermore, our method does not require knowledge about the variables used to calculate the time series being analyzed. Our proposal uses pattern matching and can validate more than 58% of instances, substantially improving human risk analysts’ efficiency. The proposal is completely transparent, allowing analysts to understand how the algorithm made its decision, increasing the trustworthiness of the method.

Keywords: Automatic pricing validation; Nearest neighbors; Machine learning; Interpretable machine learning (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10614-023-10370-x

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