Quantifying the Information Flow between Ghana Stock Market Index and Its Constituents Using Transfer Entropy

Prince Mensah Osei,Anokye M Adam

doi:10.1155/2020/6183421

Abstract

We quantify the strength and the directionality of information transfer between the Ghana stock market index and its component stocks as well as observe the same among the individual stocks on the market using transfer entropy. The information flow between the market index and its components and among individual stocks is measured by the effective transfer entropy of the daily logarithm returns generated from the daily market index and stock prices of 32 stocks ranging from 2nd January 2009 to 16th February 2018. We find a bidirectional and unidirectional flow of information between the GSE index and its component stocks, and the stocks dominate the information exchange. Among the individual stocks, SCB is the most active stock in the information exchange as it is the stock that receives the highest amount of information, but the most informative source is EGL (an insurance company) that has the highest net information outflow while the most information sink is PBC that has the highest net information inflow. We further categorize the stocks into 9 stock market sectors and find the insurance sector to be the largest source of information which confirms our earlier findings. Surprisingly, the oil and gas sector is the information sink. Our results confirm the fact that other sectors including oil and gas mitigate their risk exposures through insurance companies and are always expectant of information originating from the insurance sector in relation to regulatory compliance issues. It is our firm conviction that this study would allow stakeholders of the market to make informed buy, sell, or hold decisions.

Highlights

Information flow across markets shows how markets depend on each other
Effective Transfer Entropy Measure of the Information Flow between Ghana Stock Exchange Composite Index (GSECI) and Its Constituent Stocks. e strength and the direction of information flow between the Ghana Stock Exchange (GSE) index and the constituent stocks proxied by the effective transfer entropy are quantified using equation (3) after shuffling the data to account for small sample effects
We used daily logarithm returns computed from daily stock prices of the selected stocks and the GSE index, spanning from the period 2nd January 2009 to 16th February 2018. e results reveal interesting findings regarding the information transfer between the GSE index and its constituents and the information exchange among the stocks on GSE

Summary

Introduction

Information flow across markets shows how markets depend on each other. Quantifying such information transfer to identify information from dominant markets is of paramount interest to market agents. E predominantly used concept for detecting the lead-lag relationship between time series was Granger causality that requires vector autoregression (VAR) or error correction model (VECM) framework and imposes restrictive assumptions concerning the underlying dynamics [1]. Pricing individual stocks in a market depends on available information to the entire market and information that is more particular to the stock concerned. Models such as the capital asset pricing model (CAPM) explain stock price movements by incorporating market return. Such a model captures the linear correlation between the market return and the individual stock return. Such a model captures the linear correlation between the market return and the individual stock return. e

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Conclusion