EXPRESS: Coevolution of Trader Similarity Networks and Follower Dynamics in Social Trading

Ye Liu, Ming-Wen Yang, Yong Tan

Abstract

Social trading platforms have been gaining popularity against traditional brokerages over the past few years. Compared with traders on traditional financial markets, those on social trading platforms often exhibit more similar trading strategies. In this paper, we examine the impact of strategy similarity patterns on traders' popularity. We identify two competing effects of similarity: a substitution effect, whereby traders with similar strategies become interchangeable and compete for followers; and a social proof effect, whereby similarity signals credibility in an environment characterized by high uncertainty and limited verifiable credentials, thereby attracting followers. To empirically examine these effects, we construct a dynamic similarity network and develop a coevolution model that captures the joint evolution of network structure and follower dynamics. Using data from a leading social trading platform, we find that, on average, greater similarity leads to a decline in a trader’s followers over time, indicating that the substitution effect dominates. Importantly, this relationship is heterogeneous. For more established traders, the substitution effect is stronger, such that greater similarity reduces their popularity. In contrast, for less established traders, the social proof effect dominates, and similarity increases their popularity. Using counterfactual simulations, we show that reducing the density of the similarity network and promoting a more balanced distribution of followers can mitigate the negative substitution effect, thereby increasing the total number of followers on the platform and, consequently, the platform revenue. Our study identifies several managerial and operational implications for the social trading platforms.

Source: semanticscholar · PDF

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