Squaring the speed of light? Regulating market access in algorithmic finance

Lenglet, Marc; Mol, Joeri M.

doi:10.1080/03085147.2016.1213080

Cited by 18 publications

(9 citation statements)

References 31 publications

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“…In contemporary financial markets, scholars have shown that traders cannot be seen as ‘being in charge’ of their algorithms: in many cases, the algorithmic tool escapes the trader, whether for purely technical reasons (Lenglet, 2011; Ma and McGroarty, 2017), or more institutionalized justifications (Coeckelbergh, 2015; Lenglet and Mol, 2016). This of course does not suggest that algorithms should be represented as ‘living bacteria endowed with their own soul’ (Beunza, 2012), but rather to acknowledge that as a device, algorithms reconfigure the relation between users and their tools (Borch and Lange, 2016) by building a strong mediation (Latour, 1994), and to question this point thoroughly.…”

Section: Algorithms As ‘Quasi-objects’: Bringing Serres To Ethnographymentioning

confidence: 99%

“…It loses its status as normal subject and rational agent. Whether in the context of accessing markets (Lenglet and Mol, 2016), in the daily workings of algorithmic trading (Lenglet, 2011; Seyfert, 2018) or in HFT firms (Lange, 2016; Seyfert, 2016), interviewees have encountered many situations that would best be described as ‘abnormal’ within the dominant regulatory paradigm based on impersonal efficiency and a pure model of perfect operability.…”

Section: Interpreting Algorithms As Ethnographic Objectsmentioning

confidence: 99%

“…Having extensively surveyed the field of algorithmic and HFT, we have learnt from our interviewees and the data we gathered (Beverungen and Lange, 2018;Borch et al, 2015;Borch and Lange, 2016;Lange, 2016;Lenglet, 2011Lenglet, , 2014Lenglet and Mol, 2016;Lenglet and Riva, 2013;Seyfert, 2016Seyfert, , 2018 that most of the time, however, neither the human trader nor the algorithmic machine is in full control: metaphorically, we can describe their relation as a 'dance' with one another (Pickering, 2010). Surely, a less festive term for this dance is related to the organizational politics we mentioned earlier: as we will see, the idea of a dance allows for making sense of the exchange of roles and agencies within the relation entertained between traders and algorithms.…”

Section: Introductionmentioning

confidence: 99%

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On studying algorithms ethnographically: Making sense of objects of ignorance

2018

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In this article, we make sense of financial algorithms as new objects of concern for organizational ethnography. We conceive of algorithms as ‘objects of ignorance’ jeopardizing traditional ethnography from the perspective of its categories and methods. We investigate the organizational politics taking place within high-frequency trading – a sub-field of algorithmic trading where automated decision-making without human direction has reached a peak, and show that financial algorithms raise particular epistemic and methodological challenges for practitioners and ethnographers alike. Consequently, we develop a typology for various interpretations of algorithms as ethnographic objects, accounting for their structural ignorance and shedding light on a continuum of the changing human-machine/trader-algorithm relation. To this end, we use the concepts of ‘quasi-object’ and ‘quasi-subject’ as developed by Michel Serres, and make the point that in order to study financial algorithms ethnographically, we need to think anew the dynamic relationship they embody, and acknowledge their constitutive heterogeneity.

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Section: Algorithms As ‘Quasi-objects’: Bringing Serres To Ethnographymentioning

confidence: 99%

Section: Interpreting Algorithms As Ethnographic Objectsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On studying algorithms ethnographically: Making sense of objects of ignorance

2018

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“…A financial market flash crash is a very rapid and deep fall in a security price (Schinckus 2017). They cause concerns in the way markets operate, as they have systemically altered the nature of access to financial markets (Lenglet and Joeri 2016), and they remain unanticipated, surprising and unmanageable events causing domino effects in the economy (Lange, Lenglet, and Seyfert 2016). At the moment, there are no viable solutions (Senior Supervisors Group 2015) or proposals on how to prevent them, or how to manage these events (Borch 2016).…”

Section: Introductionmentioning

confidence: 99%

Thinking outside the Box to Get inside the Black Box: Alternative Epistemology for Dealing with Financial Innovation

Gasparin

Schınckus

Green

2019

Social Epistemology

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This paper seeks to ignite debate surrounding the computerization and change in organizing financial markets and, due to the emergence of trading algorithms, investigates those as disruptive innovation and its side effects. First, we frame the computerization of financial markets as disruptive innovation. Second, we analyze how an extension of a disruptive innovation generates latent effects that contradict the original principles. Third, we argue the necessity to explicitly consider the epistemological nature of financial innovations. This leads to our conclusion that there is a necessity to extend the epistemological dimension of financial innovation, in order to think outside the box to get inside the black box of financial innovation. Key words Black box; disruptive innovation; epistemology; financial innovation; financial markets; flash crashes Joeri 2016) and there are no well-defined regulations (Coombs 2016). Heeding MacKenzie's (2003) advice to open up the black box, we analyze the construction of algorithms, conceiving them as a disruptive innovation, reflecting on why their evolution from computerization of the financial market to trading algorithms caused epistemological issues that have not been solved and led to the emergence of the flash crashes. Innovation has been widely researched since the seminal works of Schumpeter (1934). However, the process of how innovations emerge and are managed is a perpetual issue. Innovation can be incremental (it presents minor improvements) or radical (it is new to the market or new to the company) if it refers to a product or service from a company's perspective; it can be disruptive or non-disruptive if it refers to a technology in a given market. Disruptive innovation generally emerges from a technological niche that is underestimated or even ignored by the mainstream companies operating in an industry. In this paper, we define a disruptive innovation, in line with Christensen (1997, 2006), as an innovation that disrupts the current market by serving a market segment that was not initially considered by the established firms, that disrupts the dominant configurations, creates new values that leads to changes to existing markets, and that establishes new working practices. This technology starts as a radical innovation, and then, building on a value network, subsequently captures the market of the mainstream technology and becomes dominant, moving into a phase of incremental innovations. For a large part of the literature, a disruptive innovation is a technology that changes the performance metrics along which it competes (Daneels, 2004; Pinkse, Bohnsack, and Kolk 2014) or it changes the business models of the firms in the market (Habtay 2012). Building on this literature, when they first emerged, financial algorithms, in line with Christensen (1997, 2006, 2009), created new values and led to changes to existing, and the creation of new, financial practice. Like many successful innovations, the financial algorithms and market subsequently evolved incrementally. H...

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“…In the 21st Century, with the rise of the FinTech industry and the integration of machine learning in financial trading, leading to a wave of automation in the financial industry [22,23], the issue of nonlinear market dynamics is a center stage problem for both academia, financial community and regulators [24,25,26].…”

Section: Introductionmentioning

confidence: 99%

Financial Risk and Returns Prediction with Modular Networked Learning

Gonçalves

2018

Preprint

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An artificial agent for financial risk and returns' prediction is built with a modular cognitive system comprised of interconnected recurrent neural networks, such that the agent learns to predict the financial returns, and learns to predict the squared deviation around these predicted returns. These two expectations are used to build a volatility-sensitive interval prediction for financial returns, which is evaluated on three major financial indices and shown to be able to predict financial returns with higher than 80% success rate in interval prediction in both training and testing, raising into question the Efficient Market Hypothesis. The agent is introduced as an example of a class of artificial intelligent systems that are equipped with a Modular Networked Learning cognitive system, defined as an integrated networked system of machine learning modules, where each module constitutes a functional unit that is trained for a given specific task that solves a subproblem of a complex main problem expressed as a network of linked subproblems. In the case of neural networks, these systems function as a form of an "artificial brain", where each module is like a specialized brain region comprised of a neural network with a specific architecture.

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Squaring the speed of light? Regulating market access in algorithmic finance

Cited by 18 publications

References 31 publications

On studying algorithms ethnographically: Making sense of objects of ignorance

On studying algorithms ethnographically: Making sense of objects of ignorance

Thinking outside the Box to Get inside the Black Box: Alternative Epistemology for Dealing with Financial Innovation

Financial Risk and Returns Prediction with Modular Networked Learning

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