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Seeing like the market exploring the mutual rise of transparency and accounting in transnational economic and market governance: Exploring the mutual rise of transparency and accounting in transnational economic and market governance
No full textMobilising the literature on global governance, governmentality and accounting regulation, we trace the historical deployment of transparency and the associated assemblages of actors and technologies in transnational economic and market governance. Starting with the first uses of the term “transparency” in the European Common Market (ECM) after World War II, we show how transparency came to inform and frame the imagined rational individual as the central economic (customer, central to price discovery) and later political (citizen, central to the market's public accountability) participant. We then show how in the 1990s, with the rise of the New Financial Architecture (NFA), the role of transparency in economic/market governance was fundamentally transformed. Beginning with their good governance programs, the International Monetary Fund (IMF) and the World Bank gradually adopted “standardised transparency” (in the form of financial accounting, as well as standardised statistics, state budgets, corporate governance, etc.) to govern market participants through financial market discipline. This disciplining program worked in concert with a program of moral persuasion enacted through an intensifying performance measurement apparatus. We elaborate on the implications of this transformation for the political economy of accounting, by reflecting on how the reliance on standardised transparency in neoliberal governmentality has been about: a reconfiguration of the sites of problems (focused on the national level) and solutions (focalised at the global), a liquidation of transnational market governance (that is increased reach, flexiblisation and self-organisation of both the disciplining and moralising/subjectivising governance processes), and a reconfiguration of the topology of actorhood (away from states and individuals both as enablers and beneficiaries, and towards financial investors and private standard bodies)
Motifs de l'intention: Oberkampf et Knoll comme entrepreneurs schumpétéens. Choix stratégiques et transformations majeures des champs et des pratiques.
No full textInternational audiencePresented here is an analysis of Schumpeter's interest in political economy , as it relates to his use of history to investigate economic change and capitalism. This aspect of Schumpeter's work-referring to style and involving a range of moral and aesthetic considerations-is largely neglected in entrepreneurship studies despite his influence on the discipline. This paper argues these considerations are essential to understand Schumpeter's entrepreneur and the role of creative destruction in rejuvenating capitalism. However, his theory also involves political inclinations and choices, such as elitism and a fear of declinism, both of which are more typical to conservative not destructive worldviews. To illustrate my argument I examine and describe two cases, those of Oberkampf and Knoll, the latter a rough contemporary of Schumpeter. The findings point to the central role of political economy in past and present debates about the political role of entrepreneurship in society, suggesting a need for further attention to the zeitgeist (spirit of the time) in future research
Les dynamiques d’intégration du numérique dans les écoles de création françaises
No full textInternational audienc
A Theoretical and Empirical Comparison of Systemic Risk Measures
No full textWe derive several popular systemic risk measures in a common framework and show that they can be expressed as transformations of market risk measures (e.g. beta). We also derive conditions under which the different measures lead to similar rankings of systemically important financial institutions (SIFIs). In an empirical analysis of US financial institutions, we show that (1) different systemic risk measures identify different SIFIs and that (2) firm rankings based on systemic risk estimates mirror rankings obtained by sorting firms on market risk or liabilities. One-factor linear models explain most of the variability of the systemic risk estimates, which indicates that systemic risk measures fall short in capturing the multiple facets of systemic risk
Updating Confidence in Beliefs
No full textThis paper develops a belief update rule under ambiguity, motivated by the maxim: in the face of new information, retain those conditional beliefs in which you are more confident, and relinquish only those in which you have less confidence. We provide a preference-based axiomatisation, drawing on the account of confidence in beliefs developed in Hill (2013). The proposed rule constitutes a general framework of which several existing rules for multiple priors (Full Bayesian, Maximum Likelihood) are special cases, but avoids the problems that these rules have with updating on complete ignorance. Moreover, it can handle surprising and null events, such as crises or reasoning in games, recovering traditional approaches, such as conditional probability systems, as special cases
Machine Learning et nouvelles sources de données pour le scoring de crédit
No full textIn this article, we discuss the contribution of Machine Learning techniques and new data sources (New Data) to credit-risk modelling. Credit scoring was historically one of the first fields of application of Machine Learning techniques. Today, these techniques permit to exploit new sources of data made available by the digitalization of customer relationships and social networks. The combination of the emergence of new methodologies and new data has structurally changed the credit industry and favored the emergence of new players. First, we analyse the incremental contribution of Machine Learning techniques per se. We show that they lead to significant productivity gains but that the forecasting improvement remains modest. Second, we quantify the contribution of the "datadiversity", whether or not these new data are exploited through Machine Learning. It appears that some of these data contain weak signals that significantly improve the quality of the assessment of borrowers' creditworthiness. At the microeconomic level, these new approaches promote financial inclusion and access to credit for the most vulnerable borrowers. However, Machine Learning applied to these data can also lead to severe biases and discrimination.Dans cet article, nous proposons une réflexion sur l’apport des techniques d’apprentissage automatique (Machine Learning) et des nouvelles sources de données (New Data) pour la modélisation du risque de crédit. Le scoring de crédit fut historiquement l’un des premiers champs d’application des techniques de Machine Learning. Aujourd’hui, ces techniques permettent d’exploiter de « nouvelles » données rendues disponibles par la digitalisation de la relation clientèle et les réseaux sociaux. La conjonction de l’émergence de nouvelles méthodologies et de nouvelles données a ainsi modifié de façon structurelle l’industrie du crédit et favorisé l’émergence de nouveaux acteurs. Premièrement, nous analysons l’apport des algorithmes de Machine Learning à ensemble d’information constant. Nous montrons qu’il existe des gains de productivité liés à ces nouvelles approches mais que les gains de prévision du risque de crédit restent en revanche modestes. Deuxièmement, nous évaluons l’apport de cette « datadiversité », que ces nouvelles données soient exploitées ou non par des techniques de Machine Learning. Il s’avère que certaines de ces données permettent de révéler des signaux faibles qui améliorent sensiblement la qualité de l’évaluation de la solvabilité des emprunteurs. Au niveau microéconomique, ces nouvelles approches favorisent l’inclusion financière et l’accès au crédit des emprunteurs les plus fragiles. Cependant, le Machine Learning appliqué à ces données peut aussi conduire à des biais et à des phénomènes de discrimination
Explore First, Exploit Next: The True Shape of Regret in Bandit Problems
No full textInternational audienceWe revisit lower bounds on the regret in the case of multi-armed bandit problems. We obtain non-asymptotic, distribution-dependent bounds and provide straightforward proofs based only on well-known properties of Kullback-Leibler divergences. These bounds show in particular that in an initial phase the regret grows almost linearly, and that the well-known logarithmic growth of the regret only holds in a final phase. The proof techniques come to the essence of the information-theoretic arguments used and they are deprived of all unnecessary complications
