Does a scopic regime produce conformism? Herding behavior among trade leaders on social trading platforms

Abstract

Social trading platforms (STPs) are transparent online markets governed by a scopic regime, where order flow is publicly disclosed and participants are subject to constant reciprocal scrutiny. Participants on STPs can be categorized into trade leaders and copiers, where the former execute unique trades and manage the funds allocated to them by the latter in return for compensation. Given limited individual capacity and the competition to attract copiers, we investigate whether the scopic regime produces excess and perpetual conformism among trade leaders. Using data from a popular STP, and from an anonymous traditional foreign exchange broker, we show that the scopic regime produces excess levels of herding. Under the scopic environment, we find that herding is high when market information is scarce, which is evidence of herding due to informational cascades. We find herding to be relatively low among risk-seeking trade leaders, which may be a sign of overconfidence. Herding is high for larger trades, suggesting that traders herd to avoid the disappointment associated with underperforming on large positions. Finally, we show that herding in the scopic environment persists at much higher levels compared to traditional environments. Our findings indicate that exposure to a scopic information-rich environment augments the limitations and personal biases of individual traders, thus producing excess and perpetual herding.

Does a scopic regime erode the disposition effect? Evidence from a social trading platform

“…This occurs as traders choose to close losing positions, which reduces the risk associated with holding on to large unrealized losses. The rationale that the scopic regime induces risk-averse behavior among trade leaders as a mechanism to maintain status quo is also supported by evidence presented by Gemayel and Preda (2018). Specifically, the authors show that trade leaders under a scopic regime exhibit excess intentional herding as a risk-mitigation tool when 1) market information is scarce, 2) the risk (proxied by leverage) of a publicly-disclosed strategy is low, and 3) when they have more to lose by underperforming on large positions.…”

“…Note that the test for significance in this case counts each realized gain, paper gain, realized loss, and paper loss as a separate independent observation, which are then aggregated across all traders. This independence assumption may not hold perfectly in the context of social trading, where individuals may be tempted to imitate each other's trading activities (Gemayel and Preda, 2018).…”

Herding and feedback trading in cryptocurrency markets

“…One important reason being that it is difficult to establish whether traders trade based on imitation strategies, disregarding any private information, or simply trade based on the same shared information set (Cipriani and Guarino 2009 , 2014 ). Thus far, herding behavior has been shown to exist in a number of financial market settings, such as in stock markets (Caparrelli et al 2004 ), bond markets (Galariotis et al 2016 ), amongst financial analysts (Welch 2000 ; Bernhardt et al 2006 ) and on social trading platforms (Gemayel and Preda 2018 ).…”

Groups, social processes and decision making in finance

“…Constant coordination with peers seems to play an important role. One can speculate here whether online trading increases the pressure for conformity, and whether conformistic decision-making in this environment impacts price movements more than in offline environments (Gemayel and Preda, 2017).…”