Someone is Mimicking You: Investigating Copycats in Social Trading
Abstract:
Social trading platforms are gaining popularity due to their high level of information transparency. While greater information transparency can benefit investors, it also enables strategic investors (copycats) to mimic original traders' trading strategies with little cost, pretend to be original traders, and compete with the original traders. The business model of social trading platforms would collapse if the welfare of original traders, particularly good traders, becomes impaired due to this high level of information transparency. Using data from a leading social trading platform, we investigate the effect of copycats on original traders. We propose an algorithm to detect copycats and establish a copycat-original trader network that represents the mimicking relationship. We then model the dynamic change of the network and traders' attributes (the number of followers) using the coevolution model. We find that traders who are mimicked by copycats will have fewer followers over time than traders who are not mimicked by copycats. The high level of information transparency can hurt original traders, particularly those with top financial performance. We further validate our results with additional robustness checks. Our study identifies several managerial implications for social trading platforms.
Bio:
Mingwen Yang is an Assistant Professor in Information Systems at Michael G. Foster School of Business, University of Washington. Her current research interests are social trading, fintech, and cybersecurity. She received her PhD from Jindal School of Management, University of Texas at Dallas, and received Nunamaker-Chen Dissertation Award (Runner-up) from INFORMS Information Systems Society in 2019. Her work has been published at Management Science, Information Systems Research, MIS Quarterly, and Production and Operations Management.