A Bayesian Network Analysis of Calendar Effects in the Colombo Stock Exchange

Abstract

This study applies Bayesian Network analysis to examine the probabilistic causal relationship between calendar effects and stock market anomalies in the Colombo Stock Exchange. While prior research has explored the existence of Calendar Anomalies in the Colombo Stock Exchange, few studies have examined the underlying cause-and-effect relationship between these anomalies and their associated probabilities. This study employs a Bayesian Network model using market data from 2007 to 2020 to investigate this relationship. The results indicate that calendar effects are prevalent in the market, and the analysis identifies a probabilistic causal relationship between abnormal market returns and Day-of-the-Week and Turn-of-the-Month calendar anomalies. The findings of this study enable investors to time their trades by assigning probabilities to positive or negative market returns on specific trading days, maximizing their returns and improving the efficiency of their trades in the Colombo Stock Exchange.