Copy Trading

Apesteguía, José; Oechssler, Jörg; Weidenholzer, Simon

doi:10.1287/mnsc.2019.3508

Cited by 34 publications

(15 citation statements)

References 47 publications

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“…Our findings also contribute to wider discussions around the impact of financial technology on financial decision making. We present evidence suggesting that the way financial information is framed can positively affect investment decisions, which adds to ongoing debates around risk taking and investment bias in the areas of robo‐advising (D'Acunto et al, 2019) and copy trading (e.g., Apesteguia et al, 2020). We also contribute to the literature on salience (e.g., Bazley et al, 2021; Frydman & Wang, 2020) and framing (e.g., Liêu & Pelster, 2020), by showing that the way financial platforms display trading data can directly influence investments.…”

Section: Introductionmentioning

confidence: 67%

“…Thus, our findings contribute to the recent literature on salience (e.g., Bazley et al, 2021; Frydman & Wang, 2020) and framing (e.g., Liêu & Pelster, 2020). Since the disposition effect is universally found to be wealth diminishing (e.g., Barber & Odean, 2000; Singal & Xu, 2011), applying these insights in practice can help improve the performance of investment managers, trading platforms and associated businesses (Apesteguia et al, 2020; D'Acunto et al, 2019; Schniter et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

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Investment transparency and the disposition effect

Danbolt

Eshraghi

Lukas

2021

Euro Fin Management

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The disposition effect is lower in a trading environment with salient information on current holdings. Using proprietary data from a European fintech platform for social trading, we analyze variation in trading behavior within and between private and publicly visible portfolios. The disposition effect diminishes by about 35% when trades and holdings become public. We find the level of transparency and the way financial information is illustrated can influence trading decisions. Our results suggests that requiring greater transparency from portfolio managers can reduce trading bias.

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Section: Introductionmentioning

confidence: 67%

Section: Discussionmentioning

confidence: 99%

Investment transparency and the disposition effect

Danbolt

Eshraghi

Lukas

2021

Euro Fin Management

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“…In addition, our investment decision is simple and does not require uncovering the solution to a complex optimization problem. Participants are thus less likely to imitate others' decisions than in a complex investment task with multiple assets (Gortner and van der Weele, 2019;Apesteguia et al, 2020). Social utility is also limited because the experiment is anonymous and no one can identify who has been buying the asset or not.…”

Section: Social Information and Competitionmentioning

confidence: 99%

Risk-Taking and Tail Events Across Trading Institutions

Corgnet

Cornand

Hanaki³

2021

SSRN Journal

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We study the reaction of investors to tail events across trading institutions. We conduct experiments in which investors bid on a financial asset that delivers a small positive reward in more than 99% of the cases and a large loss otherwise. The baseline treatment uses a repeated BDM mechanism whereas the market treatment replaces the uniform draw of the BDM mechanism by a uniform draw over the bids of the other participants. Our design is such that bids should not differ across treatments in normal times while allowing for potential differences to emerge after tail events have occurred. We find that markets tend to exacerbate the reaction of investors to tail losses and we attribute this effect to emotions.

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“…Transferring these micro-heuristics in the toolbox is practically feasible nowadays, as the so-called copy-trading [1] made possible by numerous platforms allows copying the operations of traders willing to make their way of operating visible in exchange for fees. Once properly documented and regimented, these micro-heuristics can then be automated.…”

Section: A Financial Heuristics Toolboxmentioning

confidence: 99%

Integrating Heuristics and Learning in a Computational Architecture for Cognitive Trading

Pareschi¹,

Zappone²

2021

Preprint

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The successes of Artificial Intelligence in recent years in areas such as image analysis, natural language understanding and strategy games have sparked interest from the world of finance. Specifically, there are high expectations, and ongoing engineering projects, regarding the creation of artificial agents, known as robotic traders, capable of juggling the financial markets with the skill of experienced human traders. Obvious economic implications aside, this is certainly an area of great scientific interest, due to the challenges that such a real context poses to the use of AI techniques. Precisely for this reason, we must be aware that artificial agents capable of operating at such levels are not just round the corner, and that there will be no simple answers, but rather a concurrence of various technologies and methods to the success of the effort. In the course of this article, we review the issues inherent in the design of effective robotic traders as well as the consequently applicable solutions, having in view the general objective of bringing the current state of the art of robo-trading up to the next level of intelligence, which we refer to as Cognitive Trading. Key to our approach is the joining of two methodological and technological directions which, although both deeply rooted in the disciplinary field of artificial intelligence, have so far gone their separate ways: heuristics and learning.

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Copy Trading

Cited by 34 publications

References 47 publications

Investment transparency and the disposition effect

Investment transparency and the disposition effect

Risk-Taking and Tail Events Across Trading Institutions

Integrating Heuristics and Learning in a Computational Architecture for Cognitive Trading

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