Information efficiency, volatility and herding: evidence from Hong Kong

Abstract

Financial market volatility persists as a dominating characteristic of modem financial markets. This presents a difficulty for finance theory in that research has demonstrated that standard models of price adjustment, such as information efficiency, are extremely limited in their ability to fully explain price adjustment during volatility. In particular, a common research conclusion is that models that are built from explanations based on psychological factors in the market, such as panics, bubbles and fads, may offer superior insight into price behaviour during volatility. One particular set of explanations formalise such factors into what are known within the economics literature as "herding models". These models suggest that investor 'herds' affect security prices inducing the observed excess volatility. This explanation offers a testable hypothesis as under information efficiency, security returns are expected to be statistically independent through time. However, a market driven by herding would be characterised by delayed information assimilation, hence some degree of serial dependence. This research tests for serial dependence on samples of minimum and maximum volatility on the Hong Kong Stock Exchange. The empirical findings indicate that minimum volatility days exhibit no serial dependence and maximum volatility days exhibit a high degree of serial dependence. This evidence suggests that information efficiency is not independent of market volatility. The implication of the findings is that herding models may offer greater explanatory power of volatility than an explanations based solely on the implications of information efficiency.