Market Returns and Weak-Form Efficiency: the case of the Ghana Stock Exchange

Frimpong, Joseph Magnus and Oteng-Abayie, Eric Fosu (2007): Market Returns and Weak-Form Efficiency: the case of the Ghana Stock Exchange.

Abstract

This paper examines the weak-form efficient market hypothesis (EMH) in the case of the Ghana Stock Exchange (GSE) an emerging market. Daily returns from the Databank Stock Index (DSI) over a 5-year period 1999-2004 were used for the exercise. Random walk (RW) and GARCH(1,1) models are used as the basis for our analysis. The GSE DSI returns series exhibit volatility clustering, an indication of inefficiency on the GSE. The weak-form efficient market (random walk) hypothesis was rejected for the GSE, meaning that the market is inefficient. The inefficient market has important implications for investors, both domestic and international. Knowledge of profitable arbitrage opportunities due to market predictability serves to attract investors to diversify from more efficient markets to invest on the GSE bourse to increase their returns.

• Item Type: MPRA Paper
• Language: English
• Keywords: Ghana Stock Exchange; FINSAP; efficient market hypothesis; nonlinearity test
• Subjects: G - Financial Economics > G1 - General Financial Markets > G14 - Information and Market Efficiency ; Event Studies ; Insider Trading; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C12 - Hypothesis Testing: General; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes

References

Bollerslev, T. (1986). “Generalised Autoregressive Conditional Heterscedasticity.” Journal of Econometrics 31: 307-27 Brock, W.A., W. Dechert, H. Scheinkman, and B. LeBaron (1996). “A test for Independence Based on the Correlation Dimension.” Econometric Reviews 15, 197-235. Brooks, C. and S.P. Burke (2003). “Information Criteria for GARCH Model Selection: An Application to High Frequency Data.” European Journal of Finance 9 (6): 557- 580. Engle, R.F. (1982). “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation.” Econometrica, 50, 987-1007. Fama, E., (1965). “The Behaviour of Stock Market Prices.” Journal of Business 38 (1), 34-105. Fama, E., (1970). “Efficient Capital Markets: A Review of Theory and Empirical Work.” Journal of Finance 25, 383–417. Hinich, M. and D.M. Patterson (1995). “Detecting Epochs of Transient Dependence in White Noise”, unpublished manuscript, University of Texas at Austin. Mandelbrot, B., (1963), “The Variation of Certain Speculative Prices”, Journal of Business 36, 394-419. McLeod, A.I. and W.K. Li (1983). “Diagnostic Checking ARMA Time Series Models Using Squared-Residual Autocorrelations.” Journal of Time Series Analysis 4, 269-273. Müslümov, A., Aras, G. and B. Kurtuluş (2004). “Evolving Market Efficiency in Istanbul Stock Exchange.” Social Science Research Network, SSRN-id 890077. Nelson, D.B. (1991). “Conditional Heteroscedasticity in Asset Returns: A New Approach.” Econometrica 55, p.703-708. Patterson, D.M. and R.A. Ashley (2000). “A nonlinear time series workshop: a toolkit for detecting and identifying nonlinear serial dependence.” Boston: Kluwer Academic Publishers. Samuelson, P.A. (1965). “Proof That Properly Anticipated prices Fluctuate Randomly.” Industrial Management Review 6, 41-49. Strategic African Securities Limited (2007), SAS Investment Research-2007 Half-Year Review of Developments on the Ghanaian Financial Markets. Retrieved on 31st July 2007 from www.sas-ghana.com. Tsay, R.S. (1986). “Nonlinearity tests for Time Series.” Biometrica, 73, 461-466.