Ambiguity aversion and rational herd behaviour

Abstract

This article reviews the literature on herd behaviour in financial markets in the context of the sequential trading model and points out the importance of incorporating ambiguity into the framework. Although Ford et al. (2005) have applied the Choquet-expected-utility theory to analyse the relationship between ambiguity and herd behaviour, their model does not allow for the separation between ambiguity and ambiguity aversion, therefore how ambiguity and ambiguity aversion affect herd behaviour cannot be analysed by comparative statistics. This article adopts the smooth model suggested by Klibanoff et al. (2005), and applies Gollier's (2006) value function to describe decision makers' welfare under ambiguity. Using very general assumptions, we prove that if the value functions of market makers and traders are homogeneous, herd behaviour will never happen even if ambiguity exists; if some types of traders have different attitudes towards ambiguity from market makers, then herd behaviour will happen with a positive probability. Our numerical simulation suggests that herd behaviour is one of the reasons behind stock price bubbles, and the probability of herd behaviour is positively correlated with the ambiguity of the distribution of stock returns as well as the disparity between traders and market makers' attitudes towards this ambiguity.

On booms that never bust: Ambiguity in experimental asset markets with bubbles

“…Because traders may change their beliefs upwards as prices rise, we expect asset prices not to exhibit the same type of dramatic crashes in the ambiguity, as in the risk, treatment. This prediction also relates to the models assessing the impact of ambiguity on herding in financial markets (e.g., Dong et al, 2010;Ford et al, 2013). These works stress that the conditions for the formation of bubbles in a herding model à la Avery and Zemsky (1998) are less stringent in the case in which ambiguity in the fundamentals is introduced.…”

“…Their general finding is that, in the presence of ambiguity, herding in financial markets can occur under a broader set of information conditions than envisioned by Avery and Zemsky (1998). Dong et al, (2010) has also shown that herding may be more likely in the presence of traders who have heterogenous attitudes towards ambiguity.…”

An experimental investigation of the “follow own signal” decision rule under increased information uncertainty

“…Here, we do not specify whether the follow-own-signal behavior will increase or decrease with uncertainty as one could argue in either direction based on previous findings: the sequential trading model of Dong et al (2010) suggests that the tendency to herd increases under increased uncertainty, which could suggest a decrease in follow-own-signal behavior under increased uncertainty. However, the findings of Celen and Kariv (2004) and Kubler and Weizsacker (2004) may suggest the opposite.…”

“…This study follows the definition of uncertainty in Knight (1921). Dong et al (2010) also follow the concept of Knightian uncertainty in their sequential trading model, where they define uncertainty of a stock's returns as characterized by the uncertainty about its distributions, such that a stock with three possible distributions about its returns has a higher uncertainty than a stock with two distributions. This is also the definition we use to implement uncertainty.…”

Analisis Faktor Faktor Determinan Dan Perilaku Herding Di Pasar Saham

“…Hasil penelitian menunjukkan bahwa, selain risiko pasar dan ketidakpastian tingkat perusahaan, kecenderungan analis untuk herding meningkat dengan ketidakpastian agregat (M. C. Lin, 2018). Secara khusus, herding tampaknya didorong terutama oleh ketidakpastian pasar, kondisi pasar yang ekstrem dan periode arus informasi yang tinggi (Arjoon & Bhatnagar, 2017;Bernales et al, 2016;Bhaduri & Mahapatra, 2013;Bouri, Gupta, & Roubaud, 2019;Cajueiro & Tabak, 2009;Dong, Gu, & Han, 2010;Indārs et al, 2019;Lan, 2014;Lao & Singh, 2011;Lindhe, 2012;Vo & Phan, 2016). Investor cenderung melakukan herding lebih dekat ketika mereka menghadapi risiko volatilitas tinggi (Al-Shboul, 2012;Balcilar et al, 2013;Bernales et al, 2016;Humayun Kabir & Shakur, 2018;Vo & Phan, 2016).…”