Predicting the performance of equity anomalies in frontier emerging markets: a Markov switching model approach

Abstract

Equity anomalies in frontier markets appear and disappear over time. This article aims to demonstrate the predictability of which of these transient anomalies will be profitable using a Markov switching model. To do so, we examine 140 equity anomalies identified in the literature using a unique sample of over 3,600 stocks from 23 frontier equity markets between 1997 and 2016. The application of a Markov switching model reveals that the time-series pattern of expected returns is dependent upon the type of anomaly; some anomalies become unprofitable over time whereas profitability increases in tandem with the development of a specific stock market for other types of anomalies. Results further indicate that forecasts of the next month's return obtained from this model can translate into profitable investment strategies. We find that an anomaly selection strategy that relies on the model produces abnormal returns and outperforms a naïve benchmark that considers all the anomalies. We go onto demonstrate that our results are robust.