Mapping borrowers’ and lenders’ interactions according to their dark financial profiles

Olivier Mesly () and Hareesh Mavoori ()
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Olivier Mesly: ICN Business School, CEREFIGE - Centre Européen de Recherche en Economie Financière et Gestion des Entreprises - UL - Université de Lorraine
Hareesh Mavoori: ICN Business School, CEREFIGE - Centre Européen de Recherche en Economie Financière et Gestion des Entreprises - UL - Université de Lorraine

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Abstract: In this interdisciplinary, conceptual article with implications in marketing fnancial products and services, we study real estate and capital markets characterized by a predatory paradigm and economic agents' dark fnancial profles (DFPs). These are estimated by three orthogonal components—disconnection, irrationality, and deceit. We identify the best interactional patterns of borrower-lender profles, ones that expectedly minimize the risk of default. We resort to discretized, predator–prey Lotka–Volterra equations where lenders act as predators and borrowers as prey, incorporating market trends and learning efects. To mathematically operationalize our framework, we use combinatorics with high, medium, and low levels of the three components of DFPs. We fnd 27 salient lender-borrower interactional scenarios and observe three diferent patterns: explosive, conducive, and implosive. Our theoretical fndings indicate that equal (ir)rationality (in fnancial terms) between lenders and borrowers is a necessary but insufcient condition to maintain harmonious, long-term relationships. We use eutectic theory to map the agents' profles by introducing another variable: Expected return [E(Rp)] versus risk [σ], using the Capital Asset Pricing Model (CAPM) as a base. We fnd six market segments: the inactive predators and prey, the loose, the greedy, the vulnerable, and the stable. We identify the optimal combination of borrowers–lenders interaction under risk, given market trends and learning efects. We propose a path for future research that would see the application of analytical tools such as factor analysis, k-means clustering algorithm, χ2 and non-parametric Kruskal–Wallis and Dunn's multiple comparison tests to verify diferences among the hypothesized segments.

Keywords: Borrowers; Combinatorics; Dark fnancial profle; Deceit; Disconnection; Eutectic; Irrationality; Learning; Lenders (search for similar items in EconPapers)
Date: 2023-11
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Published in Journal of Marketing Analytics, 2023, pp.1-15. ⟨10.1057/s41270-023-00263-1⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04350694

DOI: 10.1057/s41270-023-00263-1

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