Decoding Social Influence and the Wisdom of the Crowd in Financial Trading Network
Open accessAbstract
In this paper, we study roles of social mechanisms in a financial system. Our data come from a novel on-line foreign exchange trading brokerage for individual investors, which also allows investors to form social network ties between each other and copy others' trades. From the dataset, we analyze the dynamics of this connected social influence systems in decision making processes. We discover that generally social trades outperform individual trades, but the social reputation of the top traders is not completely determined by their performance due to social feedback even when users are betting their own money. We also find that social influence plays a significant role in users' trades, especially decisions during periods of uncertainty. We report evidences suggesting that the dynamics of social influence contribute to market overreaction.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Pan et al. (2012) find that followed traders are often but not always the most successful.
“Using data from the copy trading platform eToro , Pan et al. (2012) find that followed traders are, often but not always, the most successful.”
From Copy Trading (2020) · cites this paper as Pan et al. (2012)
Pan et al. (2012) show that users of the trading platform tend to increase the trading strategy volatility and market overreaction.
“In addition, they show that users of the trading platform tend to increase the trading strategy volatility and market overreaction.”
From Copy Trading (2020) · cites this paper as Pan et al. (2012)
Cites
Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market
Salganik, M. J., Dodds, P. S., & Watts, D. J. (2006) · Science · doi:10.1126/science.1121066 · Cited by 2,219 · cited 1× · see citing works
“While people do recognize the power of human computing in certain tasks that machines are simply not able to do, it is still unclear if a crowd can provide better predictions and decisions about future events [10].”
BackgroundComplex Contagions and the Weakness of Long Ties
Centola, D., & Macy, M. (2007) · American Journal of Sociology · doi:10.1086/521848 · Cited by 1,878 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Prospect Theory for Online Financial Trading
Liu, Y.-Y., Nacher, J. C., Ochiai, T., Martino, M., & Altshuler, Y. (2014) · PLoS ONE · doi:10.1371/journal.pone.0109458 · Cited by 76 · see citing works
Self-citationApesteguia, J., Oechssler, J., & Weidenholzer, S. (2020) · Management Science · doi:10.1287/mnsc.2019.3508 · Cited by 54 · see citing works
The forms of financial literacy overconfidence and their role in financial well‐being
Vörös, Z., Szabó, Z., Kehl, D., Kovács, O. B., Papp, T., & Schepp, Z. (2021) · International Journal of Consumer Studies · doi:10.1111/ijcs.12734 · Cited by 44 · see citing works
- Authors
- Pan, W., Altshuler, Y., & Pentland, A.
- Affiliations
- MIT Media Lab (US)
- Venue
- 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing
- Publisher
- IEEE
- Published
- 2012-09
- Pages
- 203-209
- OpenAlex
- W2024453793
- Cited by
- 101
- References
- 2
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 6 · 2023: 11 · 2022: 7 · 2021: 13 · 2020: 10 · 2019: 9 · 2018: 7 · 2017: 15 · 2016: 7 · 2015: 5 · 2014: 4
- FWCI
- 19.68
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- cc-by-nc-sa
- Locations
- International Conference on Social Computing (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Markets and Investment Strategies
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Complex Systems and Time Series Analysis
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- social influence, wisdom of crowds, retail investors, social learning, social reputation, social feedback, market overreaction, uncertainty, investor decision-making
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
