Nonparametric Efficiency Testing of Asian Stock Markets Using Weekly Data

The efficiency of speculative markets, as represented by Fama's 1970 fair game model, is tested on weekly price index data of six Asian stock markets - Hong Kong, Indonesia, Malaysia, Singapore, Taiwan and Thailand - using Sherry's (1992) non-parametric methods. These scientific testing methods were originally developed to analyze the information processing efficiency of nervous systems. In particular, the stationarity and independence of the price innovations are tested over ten years, from June 1986 to July 1996. These tests clearly show that all six stock markets lacked at least one of the two required fair game attributes, and, accordingly, Fama's Efficient Market Hypothesis must be rejected for these Asian markets. However, Singapore emerged from these tests as the most efficient regional Asian stock market. A tentative ranking in order of stock market efficiency is: Singapore, Thailand, Indonesia, Malaysia, Hong Kong and Taiwan. Singapore's stock market pricing is closest to the speculative market behavior which can support stock options. Our tests show both Hong Kong and Taiwan to be inefficient markets. Both exhibit non-stationary (likely because of continuing institutional changes) and dependent price innovations, making them particularly unsuitable for stock option pricing. In Taiwan the weekly price innovations show even higher order (Markov) dependencies. Although the price innovations in Malaysia, Thailand and Indonesia are at least stationary at the weekly level, they exhibit regular higher-order transitions and the large sustained movements in both bull and bear markets, which are so characteristic for illiquid emerging markets. All six Asian stock markets exhibit strong price trend behavior, which, perhaps, can be profitably exploited by technical analysis with first- order Markov filters (e.g., Kalman filters) in windows of between a week and more than a month.

[1]  E. Fama EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK* , 1970 .

[2]  James B. Ramsey,et al.  The Application of Wave Form Dictionaries to Stock Market Index Data , 1996 .

[3]  Stéphane Mallat,et al.  Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..

[4]  P. Boothe,et al.  The statistical distribution of exchange rates: Empirical evidence and economic implications , 1987 .

[5]  C. Los Econometrics of models with evolutionary parameter structures , 1984 .

[6]  J. Poterba,et al.  The Persistence of Volatility and Stock Market Fluctuations , 1984 .

[7]  Narasimhan Jegadeesh,et al.  Evidence of Predictable Behavior of Security Returns , 1990 .

[8]  M. Ariff Effects of Financial Liberalization on Four Southeast Asian Financial Markets, 1973-94 , 1996 .

[9]  The Stock Market: Bubbles, Volatility, and Chaos , 1990 .

[10]  E. Fama,et al.  BUSINESS CONDITIONS AND EXPECTED RETURNS ON STOCKS AND BONDS , 1989 .

[11]  Robert N. McCauley,et al.  Manias, Panics, and Crashes: A History of Financial Crises , 1979 .

[12]  Richard Startz,et al.  Mean Reversion in Stock Prices? a Reappraisal of the Empirical Evidence , 1988 .

[13]  Randolph Nelson,et al.  Randomness and Probability , 1995 .

[14]  T. Andersen THE ECONOMETRICS OF FINANCIAL MARKETS , 1998, Econometric Theory.

[15]  A. Lo,et al.  Stock Market Prices Do Not Follow Random Walks: Evidence from a Simple Specification Test , 1987 .

[16]  E. Fama,et al.  Permanent and Temporary Components of Stock Prices , 1988, Journal of Political Economy.

[17]  James B. Ramsey,et al.  Economic and financial data as nonlinear processes , 1988 .

[18]  C. Los Nonparametric Testing of the High-Frequency Efficiency of the 1997 Asian Foreign Exchange Markets , 1998 .

[19]  Adrian Pagan,et al.  Testing for covariance stationarity in stock market data , 1990 .

[20]  R. E. Kalman,et al.  RANDOMNESS AND PROBABILITY , 1995 .

[21]  C. Rio,et al.  The Random Walk Hypothesis in the Spanish Stock Market: 1980–1992 , 1997 .

[22]  C. Los Optimal Multi-Currency Investment Strategies with Exact Attribution in Three Asian Countries , 1998 .

[23]  Clive W. J. Granger,et al.  Spectral analysis of New York stock market prices , 1963 .

[24]  E. Fama Stock Returns, Expected Returns, and Real Activity , 1990 .

[25]  M. Dacorogna,et al.  Statistical study of foreign exchange rates, empirical evidence of a price change scaling law, and intraday analysis , 1990 .

[26]  T. W. Epps,et al.  The Stochastic Dependence of Security Price Changes and Transaction Volumes: Implications for the Mixture-of-Distributions Hypothesis , 1976 .

[27]  A. Lo Long-Term Memory in Stock Market Prices , 1989 .

[28]  P. Phillips,et al.  Testing the covariance stationarity of heavy-tailed time series: An overview of the theory with applications to several financial datasets , 1994 .

[29]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[30]  E. Fama,et al.  Dividend yields and expected stock returns , 1988 .

[31]  J. Poterba,et al.  What moves stock prices? , 1988 .

[32]  M. Shapiro,et al.  Stock Market Forecastability and Volatility: A Statistical Appraisal , 1989 .

[33]  W. Lewis,et al.  Manias, Panics and Crashes: A History of Financial Crises , 1979 .

[34]  M. Hinich Testing for dependence in the input to a linear time series model , 1996 .

[35]  P. Clark A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices , 1973 .