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Ekaterina Svetlova, Financial Models and Society: Villains or Scapegoats?

Ariane Agunsoye
p. 365-371
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Ekaterina Svetlova, Financial Models and Society: Villains or Scapegoats? Cheltenham, UK: Edward Elgar, 2018, 192 pages, 978-178471001-9

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Crédits : Edward Elgar

1Perhaps it is no more timely than now to discuss Ekaterina Svetlova’s book on the impact of financial actors and their use of models on financial stability. We have just witnessed a power play between private investors and hedge fund managers which has resulted in temporarily blocking amateur investors on trading apps (BBC, 2021) and despite living with a major pandemic stock market values are rising, hinting at yet another financial bubble (Egan, 2021). These events have once again highlighted the importance of scrutinizing actions taken by financial institutions and explore whether models are villains, “dangerous and having the power to destroy markets”, or scapegoats, “harmless camouflage which is frequently used to hide risks emerging somewhere else” (7). Originating from a desire to challenge claims made after the Global Financial Crisis that the blind following of models has led to its turmoil, Svetlova argues for a more subtle interpretation where “insufficient models are used as one of many resources in the multifaceted decision-making practices of financial markets” (3). In brief, the argument is that while financial models have grown in importance when making financial decisions, these are not used in isolation but users’ emotions, judgements of the market and/or common institutional practices are part of the decision-making process. Consequently, rather than experiencing a uniform application of models, we observe a differentiated employment of models “contributing to the markets’ stability rather than destroying them” (144). To make this argument, the book is divided into two parts. In the first part, Svetlova undertakes a comprehensive review of the literature (Chapters 2 and 3) while the second part (Chapters 4 and 5) combines extensive empirical data collected in Germany and Switzerland with secondary data.

2The theoretical chapters are remarkable in their ability to synthesise a large, interdisciplinary literature on decision-making processes of actors within financial institutions. Integrating insights from economics, finance and sociology, the author is able to show limitations to the assumptions of substantive rationality and emphasize the importance of judgements and emotions in making financial decisions. Perhaps most importantly to note here is the recognition of radical uncertainty and its distinction from risk which is often marginalized within the economics tradition. Svetlova acknowledges that investment decisions are inherently linked to “non-knowledge” which not only “cannot be conceptualized as missed information or not-yet-knowledge” but also “cannot be fully eliminated … in financial markets” (144). This is argued to be a necessary condition for financial markets to exist because if future values would be known, there would be no potential gains from differential asset values. Svetlova then develops an important connection to one of the limitations of financial models: “self-referentiality” (16) whereby models do not sufficiently take into consideration how present expectations and actions of market actors change future outcomes and through this “produce their own blind spots” (19). This is an intriguing argument as it seems to indicate, while not explicitly stating it, that the employment of models reinforces the creation of non-knowledge.

3The book’s empirical discussion is certainly of importance to the field of economics, calling attention to the need to develop realistic assumptions of economic theoretical viewpoints and challenging its dominant use of quantitative research. Svetlova rightly points out that studies of decision-making would greatly benefit from employing ethnographic research, in particular observations and in-depth interviews. The extensive qualitative research conducted by her, ranging from semi-structured interviews with 8 financial academics and 40 practitioners to a three-months long participant observation in a Swiss investment bank, enabled the author to debunk some of the “ways of making models work” (7) in situations of uncertainty. Rather than ignoring model deficiencies, Svetlova identified three unique strategies of employing inherently insufficient models: 1) qualitative overlay where personal judgements outweigh quantitative results from the model, 2) backing out/implied modelling where judgements are used to find out the underlying expectations in a current market forecast and develop personal deviations from it and 3) employing models as opinion proclaimers to portray one’s own opinion by adjusting its input factors. I applaud the introduction of performance acts here, differentiating between backstage and frontstage performance, or in other words between decision-making and decision-selling—whereas backstage refers here to internal discussions between members of a company, frontstage refers to situations where company members present insights to an external audience such as customers. While in the backstage models are “overlaid by judgement” as seen in the three strategies outlined above, in the frontstage they are used as “instruments for staging precision” (128). Because uncertainty cannot be overcome by “knowledge work” (144)—the process of gaining knowledge about market developments, for instance, through collecting information and/or conducting more analyses—and clients expect clearly identifiable, quantified suggestions, financial actors provide “pseudo-knowledge” (146), that means a forecast of future outcomes originating from an interplay between personal judgement and financial models. Here, it is less important what specific actions the models suggest and more important how the numbers of the model can be used to sell a decision. Models thus act as “communicative tools” (121) to “perform (not to produce!) knowledge” (127).

4These cultures of model use, backstage and frontstage, are argued to show limitations of financial models being responsible for periods of financial instability. Due to the differentiated employment of models and bringing in one’s own judgement, they are not omni-performative and have not led to users blindly following their suggestions but instead model application is inherently creative. “For each culture, different types of unknowns are relevant and there are different ways of recognizing them and coping with unforeseen developments” (147). The process of making insufficient models work is thus never the same and is influenced by individual behaviour as well as institutional norms. It can be performative in the sense of enabling decisions in situations of uncertainty and through this changing reality but it can also “not produce effects, or … produce effects opposite to what they describe” (129). As a consequence, Svetlova asserts that models have been wrongly accused of being villains. Despite being insufficient, models and the concomitant unique practices allow otherwise impossible decisions to be made in a radical uncertain environment.

5Now this leads me to my first point of criticism. Whereas the author rejects claims of “deliberate misuse of models” (110) and contends that “the term pseudo-knowledge is not intended to have any negative connotation” in her analysis (146), a somewhat different conclusion could be drawn based on the provided empirical data. An argument could be made that her examples indicate a rather intentional use of financial models to conceal non-knowledge and influence market developments. Here, I would like to take up the two concepts introduced by Svetlova, cultures of model use and self-referentiality. Practices by financial actors reveal that models are employed to convince others of one’s own “subjective opinions” (93) about market developments and initiate investment decisions. In the backstage, opinion proclaimers manipulate input factors so that the model “proclaims what its users think is right” (93) and in the front stage, models create “illusions of knowledge” (146) where situations of uncertainty are established as manageable situations of risk, creating a false sense of security. If colleagues or clients then act on this judgement, the ensuing investments change market outcomes, be it positive or negative. One could argue, as frequent references in the book suggest, that this is not a deliberate misuse but simply a result of the inherent insufficiencies of models such as not being able to represent reality and deal with radical uncertainty.

6Yet, the author provides evidence which contradicts this argument when discussing model use by credit rating agencies before the Global Financial Crisis. Here, she highlights that by selectively choosing the information they wanted to put more emphasis on, credit rating agencies “used the models to present the risks as lower than they really were” (114) and “maintain any rating they want[ed]” (113). While this example is given to assert that models are not inherently harmful but that financial agents manipulate model outcomes, it reveals how models are being purposefully used in shaping market outcomes. To the reader, it also does not become clear how models could be separated from their users, indicating a similar fallacy as criticised by the author where theory and practise are perceived as separate entities. This contradiction is then arguably overcome by asserting that like using a language “there is no correct way of using models” (108) and by representing staging as a mutual process where the client is aware of the staging process and “acts as if they trust”. Since customers are being part of the creation of pseudo-knowledge and “fake the willingness to trust” (126), manipulating models to one’s own perception is less problematic as it removes the deadlock in making decisions under radical uncertainty and enables financial activities. What this assumption does not explain is why clients would pay an advisor if they are aware that they are receiving knowledge illusions. It also ignores how, despite being incorrect, models, similarly to language, are powerful enablers in influencing decisions by others with even potentially detrimental effects.

7Alongside staging of “mutual non-knowledge as knowledge” (39), Svetlova refutes the concept of herding and its influence in creating financial bubbles based on yet another insufficiency of models, namely the inability to portray other market participants’ actions. Creating and “communicating heterogeneous types of non-knowledge” (147) is asserted to translate into an inability of models driving market behaviour in a coherent way. This conclusion seems rather hastily taken. Again, the author appears to challenge this argument herself by stating that advisors seek to convince clients to choose them through “feigning” knowledge “where there is no reliable knowledge at all” (42) and contrasting their suggestions to the dominant market view. Indeed, in all cultures of model use financial actors compare one’s own opinions to the market views. Independent of how the information is presented within the particular culture of model use, if judgements of the market situation are the same and the models are manipulated to convince others, they become tools in influencing the market and can enhance upward or downward movements of the market. This we have seen in the example of the credit rating agencies who played a dominant role in market developments before the financial crisis and in its aftermath, endangering financial stability in Europe. The distinction between models and the agents using them is thus rather problematic as it underestimates the power of using models in impacting market movements.

8This leads me to my second point of criticism: the missing exploration of underlying power relationships. While some statements hint at an unequal relationship between advisors and investors (“knowledge is known to one party but concealed from another” [146]) and political motivations being conjoined with model use (“compulsory agreement on a house view is … rather politically motivated” [140]), they are quickly dispensed with since the author’s intention is to discuss individual decision-making processes and not systemic changes. I believe this view is rather challenging as we cannot separate individual behaviour from institutional norms and systemic stability is dependent on sector level stability. In other words, micro-, meso- and macro-level dynamics are intertwined and power relationships are shaping these interconnections. The author herself seems to tackle with this inherent conflict between arguing that financial models should not be categorized as villains and their potential detrimental effects on economic outcomes. In the conclusion, Svetlova calls for future studies to pick up the role models play “as tools of staging false certainty and security” (156) and to provide insights into “what is concealed and why it is concealed” (147), only to then downplay the importance of such actions. Models are stated to be used within an environment of incomplete knowledge where “collective outcomes are often not intended” (156) but are byproducts from using inherently insufficient models which themselves produce non-knowledge. The question remains why then use formalized, quantitative models if they cannot solve radical uncertainty and instead reinforce the creation of non-knowledge.

9The answer to that can be found in the increasing mathematisation of economics and its influence on the financial sector and its regulatory framework. Since the 1980s, we have experienced a substantial change in society with numerous countries deregulating financial markets, culminating in the financial sector growing to unprecedented levels, generating profit opportunities for corporations while increasing income and wealth inequality and destabilizing economic systems. This change was an ideological project driven by the belief that state intervention should be minimal and that wider access to financial markets would generate economic growth. Economists have been largely influential in creating this change through advising governments and producing ‘objective’ knowledge. Yet we can find non-disclosed conflicts of interests (Epstein, 2020) and discriminatory practices in knowledge production and dissemination, preventing methodological and theoretical diversity (Colander, 2005; Hengel, 2017; Mearman et al., 2018). Both forces, the increasing role of economics in shaping regulatory changes and its underlying neoliberal ideology, challenge claims of objective knowledge. It is thus less surprising that even after the Global Financial Crisis, the finance sector was able to keep its hold on society by avoiding major regulatory forces (Epstein, 2020) and despite the increasing mathematisation of economics having been criticised, its formalized models are still perceived as acceptable knowledge in forecasting economic outcomes (Bresser-Pereira, 2012; Fourcade et al., 2015; Lawson, 2009). Moreover, in defiance of their strong influence on financial instability before and after the Global Financial Crisis and their conflicts of interests based on being private institutions who evaluate their own clients, credit rating agencies continue to exert power over economic outcomes, as evidenced in their recent move to consider downgrading low-income countries’ ratings, threatening their economic recovery (Ghosh, 2021).

10Svetlova makes such a convincing case in describing the staging process where signifiers associated with finance are employed to create trust and motivate action, including wearing expensive watches and presenting “hard science” and “objective” knowledge (124), that it is somewhat surprising that she has largely removed the discussion of power relations immanent in financial decision-making processes from her analysis. As highlighted by recent events, where hedge funds, despite being proponents of the free-market ideology, have incited the blocking of private investors and where rising stock market values have led to a substantial rise in wealth of high net-worth individuals while large groups of society struggle financially, a discussion on underlying power relationships should not be moved to the side lines. It is more urgent than ever to reconsider dominant forms of knowledge production and its interplay with everyday practices. A starting point for this agenda could be the truly interdisciplinary findings provided in Svetlova’s book. Her insights into cultures of model use are highly relevant in highlighting the importance of methodological diversity in economic research and put into question the narrative of objective knowledge.

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BBC. 2021. Anger as Trading in Gamstop Shares is Restricted. Available at: (Accessed 1 March 2021).

Bresser-Pereira, Luiz Carlos. 2012. Why Economics Should be a Modest and Reasonable Science. Journal of Economic Issues, 46(2): 291-302.

Colander, David. 2005. The Making of an Economist Redux. Journal of Economic Perspectives, 19(1): 175-198.

Egan, Matt. 2021. US Stocks are in a Bubble, and it’s Unclear When it will Pop, Hedge Fund Manager Says. Available at: (Accessed 1 March 2021).

Epstein, Gerald. 2020. The Banker’s Club and the Power of Finance. In Philip Mader, Daniel Mertens and Natasha Van der Zwan (eds), The Routledge International Handbook of Financialization. New York: Routledge, 437-447.

Fourcade, Marion, Etienne Ollion and Yann Algan. 2015. The Superiority of Economics. Journal of Economic Perspectives, 29(1): 89-114.

Ghosh, Jayati (2021) Credit-Rating Agencies Could Derail Economic Recovery. Project Syndicate. Available at: (Accessed 18 March 2021).

Hengel, Erin. 2017. Publishing while Female. Are Women Held to Higher Standards? Evidence from Peer Review. Cambridge Working Papers in Economics, No. 1753.

Lawson, Tony. 2009. The Current Economic Crisis: its Nature and the Course of Academic Economics. Cambridge Journal of Economics, 33(4): 759-777.

Mearman, Andrew, Danielle Guizzo and Sebastian Berger. 2018. Whither Political Economy? Evaluating the CORE Project as a Response to Calls for Change in Economics Teaching. Review of Political Economy, 30(2): 241-259.

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Ariane Agunsoye, « Ekaterina Svetlova, Financial Models and Society: Villains or Scapegoats? »Œconomia, 11-2 | 2021, 365-371.

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Ariane Agunsoye, « Ekaterina Svetlova, Financial Models and Society: Villains or Scapegoats? »Œconomia [En ligne], 11-2 | 2021, mis en ligne le 01 juin 2021, consulté le 18 avril 2024. URL : ; DOI :

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Ariane Agunsoye

Goldsmiths, University of London.

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Le texte seul est utilisable sous licence CC BY-NC-ND 4.0. Les autres éléments (illustrations, fichiers annexes importés) sont « Tous droits réservés », sauf mention contraire.

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