Prospect Theory for Online Financial Trading
Open accessAbstract
Prospect theory is widely viewed as the best available descriptive model of how people evaluate risk in experimental settings. According to prospect theory, people are typically risk-averse with respect to gains and risk-seeking with respect to losses, known as the "reflection effect". People are much more sensitive to losses than to gains of the same magnitude, a phenomenon called "loss aversion". Despite of the fact that prospect theory has been well developed in behavioral economics at the theoretical level, there exist very few large-scale empirical studies and most of the previous studies have been undertaken with micro-panel data. Here we analyze over 28.5 million trades made by 81.3 thousand traders of an online financial trading community over 28 months, aiming to explore the large-scale empirical aspect of prospect theory. By analyzing and comparing the behavior of winning and losing trades and traders, we find clear evidence of the reflection effect and the loss aversion phenomenon, which are essential in prospect theory. This work hence demonstrates an unprecedented large-scale empirical evidence of prospect theory, which has immediate implication in financial trading, e.g., developing new trading strategies by minimizing the impact of the reflection effect and the loss aversion phenomenon. Moreover, we introduce three novel behavioral metrics to differentiate winning and losing traders based on their historical trading behavior. This offers us potential opportunities to augment online social trading where traders are allowed to watch and follow the trading activities of others, by predicting potential winners based on their historical trading behavior.
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In case of negative returns, losses are typically higher for copied trades.
“Further, in case of negative returns, losses are typically higher for copied trades.”
From Copy Trading (2020) · cites this paper as Liu et al. (2014)
In Results > Social vs. Non-social trade (page not available), the paper says copy trades have a more negative overall ROI than non-social trades. That does not establish that losses are typically higher specifically among trades with negative returns; for losing trades, the paper instead identifies mirror trades as having significantly more negative ROI than copy trades. (LLM, conf=0.83)
Experimental — accuracy not yet measuredLiu et al. (2014) show that copied trades have a larger probability of positive returns than standard trades, but the return on investment of successful copy trades is smaller than the return of standard successful trades.
“Further, Liu et al. (2014) show that copied trades have a larger probability of positive returns than standard trades, but the return on investment of successful copy trades is smaller than the return of standard successful trades.”
From Copy Trading (2020) · cites this paper as Liu et al. (2014)
In Results > Social vs. Non-social trade (page unavailable), the paper says social trades win more frequently on average than non-social trades, and its duration analysis describes a slight winning advantage for copy trades. But the lower ROI for successful trades is reported for mirror trades compared with both copy and single trades; the text does not establish that successful copy trades have lower ROI than successful single trades. (LLM, conf=0.82)
Experimental — accuracy not yet measured
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“…risk preferences without making a self-other distinction; demonstrating the reflection effect of prospect theory: people seek risks in a loss situation to reduce possible losses and avoid risks in a gain situation to obtain certain gains (e.g., Liu et al., 2014; Tversky & kahneman, 1981).”
BackgroundUnderstanding market functionality and trading success
Schmitz, J., & Rothschild, D. M. (2019) · PLoS ONE · doi:10.1371/journal.pone.0219606 · Cited by 2 · see citing works
Deep Reinforcement Learning for Asset Allocation: Reward Clipping
Kim, J., Kang, M.-J., Lee, K. I., Moon, H., & Jeon, B.-K. (2023) · arXiv.org · doi:10.48550/arxiv.2301.05300 · Cited by 1
An Assessment of the Level of Adoption of Financial Technology (Fintech) by Nigerian Banks
Konto, A., & Akinwale, Y. (2019) · Conference proceedings · doi:10.69798/03425939 · Cited by 1
“Social trading involves a social network in which an investor can see, deliberate and copy investment collections of other participants in a social network (Liu et al ., 2014).”
BackgroundNwogugu, M. C. I. (2019) · Palgrave Macmillan UK eBooks · doi:10.1057/978-1-137-44704-3_9
Behavioural theories of investor behaviour: Empirical evidence from the limit order book
Chen, R., Tai, C., Lepori, G. M., Sung, M., & Tseng, Y.-H. (2026) · Journal of Banking & Finance · doi:10.1016/j.jbankfin.2026.107797
Evolution of FinTech Literature: A Bibliometric Exploration of Leading Journals
Bhama, A., Mishra, N., Mukherjee, P., Kumar, V., Tiwari, S., & Mathur, M. (2026) · 2026 IEEE 1st International Conference on Intelligent Technologies for a Sustainable and Inclusive Future (ICITSIF) · doi:10.1109/icitsif69060.2026.11609093
“Investment banking platforms, social trading, robo-advisors, and personal financial management are all included in this category [8, 10].”
Chitaunga, P. S., Boley, B. B., & King, E. (2025) · Journal of Sustainable Tourism · doi:10.1080/09669582.2025.2588376
“…development projects like the potential Kilimanjaro cable car because it explains how people have a habit of avoiding ambiguity when it comes to making difficult choices, and they often go for the safer and risk-free option (regret avoidance) (Kahneman & Tversky, 1979; Liu et al., 2014).”
Finance Film on Must-See Lists: A Tale of Positivization
Parvulescu, C. (2022) · Acta Universitatis Sapientiae: Film and Media Studies · doi:10.2478/ausfm-2022-0008
Addressing Sustainable Supply Chain Network Using Stackelberg Game
Mahdizadeh, R., Pourbaba, I., Fozooni, N., & Abraham, A. (2022) · International Conference of Soft Computing and Pattern Recognition · doi:10.1007/978-3-030-96302-6_31
Everything as a Service (XaaS)
Riasanow, T., & Krcmar, H. (2020) · Handbuch Digitale Wirtschaft · doi:10.1007/978-3-658-17345-6_70-1
Nwogugu, M. I. C. (2019) · Complex Systems, Multi-Sided Incentives and Risk Perception in Companies · doi:10.1057/978-1-137-44704-3_4
The Search Complexity of Collaborative Swarms in Expanding $$\mathbf{Z}^{2}$$ Grid Regions
Altshuler, Y., Pentland, A., & Bruckstein, A. (2018) · doi:10.1007/978-3-319-63604-7_5
Self-citation
- Authors
- Liu, Y.-Y., Nacher, J. C., Ochiai, T., Martino, M., & Altshuler, Y.
- Affiliations
- Brigham and Women's Hospital (US)
- Toho University (JP)
- Otsuma Women's University (JP)
- IBM (United States) (US)
- MIT Media Lab (US)
- Corresponding author
- Liu, Yang-Yu
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2014-10-15
- Volume
- 9
- Issue
- 10
- Pages
- e109458
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC4198126
- OpenAlex
- W2162439131
- Cited by
- 76
- References
- 40
- Citations by year
- 2026: 1 · 2025: 10 · 2024: 3 · 2023: 7 · 2022: 5 · 2021: 8 · 2020: 11 · 2019: 8 · 2018: 6 · 2017: 10 · 2016: 3 · 2015: 4
- FWCI
- 5.17
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Defense Advanced Research Projects Agency (W911NF-09-2-0053)
- Defense Threat Reduction Agency (BRBAA08-Per4-C-2-0033, HDTRA1-10-1-0100/BRBAA08-Per4-C-2-0033, HDTRA1)
- Army Research Laboratory (W911NF, No. W911NF-09-2-0053, W911NF-09-2-0053)
- Topics
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Markets and Investment Strategies
- Social Sciences › Business, Management and Accounting › Accounting › Financial Literacy, Pension, Retirement Analysis
- Keywords
- prospect theory, reflection effect, loss aversion, trading behavior, behavioral metrics, risk preferences
- MeSH terms
- Capital Financing, Humans, Models, Theoretical, Risk-Taking, Social Sciences, Economics, Behavioral, Social Media
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