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Prospect theory for online financial trading
Yang-Yu Liu1, Jose C Nacher2, Tomoshiro Ochiai3
1Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America; Center for Complex Network Research and Departments of Physics, Computer Science and Biology, Northeastern University, Boston, Massachusetts, United States of America; Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America.
Plos One
|October 21, 2014
Summary
Prospect theory
Area of Science:
- Behavioral economics
- Financial trading
- Decision-making under risk
Background:
- Prospect theory is a leading model for understanding risk evaluation.
- Key concepts include the reflection effect (risk-seeking for losses) and loss aversion (greater sensitivity to losses).
- Existing empirical evidence is limited, primarily using micro-panel data.
Purpose of the Study:
- To provide large-scale empirical validation of prospect theory's core tenets.
- To analyze over 28.5 million trades from 81.3 thousand traders.
- To explore the reflection effect and loss aversion in a real-world financial trading context.
Main Methods:
- Analysis of a large dataset of online financial trades (28.5 million trades, 81.3 thousand traders, 28 months).
- Comparison of trading behaviors between winning and losing trades and traders.
- Development of novel behavioral metrics to distinguish trader performance.
Main Results:
- Confirmation of the reflection effect and loss aversion phenomena in a large-scale trading dataset.
- Demonstration of unprecedented empirical evidence supporting prospect theory.
- Identification of distinct behavioral patterns differentiating winning and losing traders.
Conclusions:
- The study provides robust, large-scale empirical support for prospect theory in financial trading.
- Findings have direct implications for developing trading strategies that mitigate behavioral biases.
- Novel metrics offer potential for enhancing social trading platforms by predicting trader success.
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