Algorithmic Trading in Experimental Markets with Human Traders: A Literature Survey

Bao, Te; Некрасова, Е. В.; Neugebauer, Tibor; Riyanto, Yohanes E.

doi:10.2139/ssrn.3908065

Cited by 6 publications

(4 citation statements)

References 43 publications

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“…Connecting our findings with related efforts such as focusing on complete versus incomplete information (Kimbrough and Smyth, 2018) or on what information traders actively seek (Kirchsteiger et al., 2005) is useful to inform optimal information design in DAs, especially as availability of information may also determine what kinds of algorithms may be used by nonhuman traders. These kinds of questions are becoming increasingly relevant as the digitization and automation of markets proceeds further (Bao et al., 2021).…”

Section: Discussionmentioning

confidence: 99%

Competitive Market Behavior: Convergence and Asymmetry in the Experimental Double Auction

Ikica

Jantschgi

Nax

et al. 2023

Int Economic Review

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We conducted a large number of controlled continuous double auction experiments to reproduce and stress‐test the phenomenon of convergence to competitive equilibrium under private information with decentralized trading feedback. Our main finding is that across a total of 104 markets (involving over 1,700 subjects), convergence occurs after a handful of trading periods. Initially, however, there is an inherent asymmetry that favors buyers, typically resulting in prices below equilibrium levels. Analysis of over 80,000 observations of individual bids and asks helps identify empirical ingredients contributing to the observed phenomena including higher levels of aggressiveness initially among buyers than sellers.

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Section: Discussionmentioning

confidence: 99%

Competitive Market Behavior: Convergence and Asymmetry in the Experimental Double Auction

Ikica

Jantschgi

Nax

et al. 2023

Int Economic Review

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“…Ref. [6] found that whether algorithm traders earn more profit than human traders depends on the market environment and the fundamental value of a financial asset. Ref.…”

Section: Introductionmentioning

confidence: 99%

Algorithmic Trading and Financial Forecasting Using Advanced Artificial Intelligence Methodologies

Cohen

2022

Mathematics

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Artificial Intelligence (AI) has been recently recognized as an essential aid for human traders. The advantages of the AI systems over human traders are that they can analyze an extensive data set from different sources in a fraction of a second and perform actual high-frequency trading (HFT) that can take advantage of market anomalies and price differences. This paper reviews the most important papers published in recent years that use the most advanced techniques to forecast financial asset trends and answer the question of whether those techniques can be used to successfully trade the complex financial markets. All systems use deep learning (DL) and machine learning (ML) protocols to explore nonobvious correlations and phenomena that influence the probability of trading success. Their predictions are based on linear or nonlinear models often combined with social media investors’ sentiment derivations or pattern recognitions. Most of the reviewed papers have proven the successful ability of their developed system to trade the financial markets.

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Section: Introductionmentioning

confidence: 99%

“…An increasing fraction of trading volumes today is executed by algorithms (Bao et al, 2021;The Economist, 2019), with about 40% of volume executed by high-frequency trading funds (Brogaard et al, 2014). Algorithms are now involved in much of the trading that happens on financial markets through large (institutional) investors (Bao et al, 2021). They also augment smaller (retail) investors through robo-advisory services (D'Acunto et al, 2019;Tao et al, 2021), that suggest (or straight-out make) investment decisions on behalf of laypeople.…”

Section: Introductionmentioning

confidence: 99%

Will the use of machines to identify negative activist investments backfire? Experimental evidence

Niszczota¹,

Kaszás²

2022

Preprint

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Current research does not resolve how people will judge harmful activist investments if machines (machine learning algorithms) are involved in the investment process as advisors, and not the ones “pulling the trigger”. On the one hand, machines might diffuse responsibility for making a socially responsible, but harmful investment. On the other hand, machines could exacerbate the blame that is assigned to the investment fund, which can be penalized for outsourcing part of the decision process to an algorithm. We attempted to resolve this issue experimentally. In our experiment (N = 956), participants judge either a human research team or a machine learning algorithm as the source of advice to the investment team to short-sell a company that they suspect of committing financial fraud. Results suggest that investment funds will be similarly blameworthy for an error regardless of using human or machine intelligence to support their decision to short-sell a company. This finding highlights a novel and relevant circumstance in which reliance on algorithms does not backfire by making the final decision-maker (e.g., an investment fund) more blameworthy. Nor does it lessen the perceived blameworthiness of the final decision-maker, by making algorithms into “electronic scapegoats” for providing well-intended but harmful advice.

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Algorithmic Trading in Experimental Markets with Human Traders: A Literature Survey

Cited by 6 publications

References 43 publications

Competitive Market Behavior: Convergence and Asymmetry in the Experimental Double Auction

Competitive Market Behavior: Convergence and Asymmetry in the Experimental Double Auction

Algorithmic Trading and Financial Forecasting Using Advanced Artificial Intelligence Methodologies

Will the use of machines to identify negative activist investments backfire? Experimental evidence

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