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Weak form efficiency of the capital market: a spectral analytic approach (°)

Published online by Cambridge University Press:  17 August 2016

C. De Groof
C. De Groof*
Affiliation:
University of Antwerp (UFSIA)
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Extract

A capital market is said to be weakly efficient whenever the time series of past information (usually past returns) do not contain any information that enables an investor to obtain higher-than-normal yields. Operationally, the definition of weak form capital market efficiency comes down to stating that statistically no patterns of dependence can be discerned in the time series of rates of return. Up till now empirical testing of the weak form hypothesis has been carried out by means of autocorrelation- and run-analysis.

Type
Research Article
Information
Recherches Économiques de Louvain/ Louvain Economic Review , Volume 44 , Issue 2 , June 1978 , pp. 193 - 215
Copyright
Copyright © Université catholique de Louvain, Institut de recherches économiques et sociales 1978 

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Footnotes

°

Thanks are due to an anonymous referee for his helpful comments. Remaining errors are ours.

References

Bendat, J. and Piersol, A. (1971), Random Data: Analysis and Measurement Procedures, Wiley, New York.Google Scholar
Bloomfield, P. (1976) Fourier Analysis of Time Series, Wiley, London.Google Scholar
Bolch, B. and Huang, C. (1974), Multivariate Statistical Methods for Business and Economics, Prentice Hall, New Jersey.Google Scholar
Box, G. and Jenkins, G. (1970), Time Series Analysis: Forecasting and Control, Holden Day, San Francisco.Google Scholar
Chan, K., Hayya, J. and Ord, J. (1977), A Note on Trend Removal Methods: the Case of Polynomial Regression Versus Variate Differencing, Econometrica, Vol. 45, nr 3.Google Scholar
Cole, F. (1976), Forecasting by Exponential Smoothing, the Box and Jenkins Procedure and Spectral Analysis: a Simulation Study, Onderzoeksrapport 7614, Departement Toegepaste Economische Wetenschappen, Leuven.Google Scholar
Conrad, K. and Juettner, D. (1973), Recent Behavior of Stock Market Prices and the Random Walk Hypothesis, Kyklos, Basel.Google Scholar
De Groof, C. and Fabry, J. (1978), The Stationarity of Distributions with an Application to Belgian Capital Market Data, Working paper Centre for Managerial Economics and Econometrics, University of Antwerp (UFSIA), (forthcoming).Google Scholar
Dryden, M. (1970), A Statistical Study of U.K. Share Prices, Scottish Journal of Political Economy, Vol. 17.Google Scholar
Fabry, J. (1977), Returns on Belgian Common Stocks and Related Issues: a Survey and some Extensions, Bedrijfseconomische Verhandeling 77-6, Centre for Managerial Economics and Econometrics, University of Antwerp (UFSIA).Google Scholar
Fama, E. (1965), The Behavior of Stock Market Prices, Journal of Business.Google Scholar
Fama, E. (1976), Foundations of Finance, Basic Books, New York.Google Scholar
Fama, E. (1971), Efficient Capital Markets, reprinted (p. 309–353) in Intriligator, M. Frontiers of Quantitative Economics, North-Holland Publishing Cy, Amsterdam.Google Scholar
Fishman, G. (1969), Spectral Methods in Econometrics, Harvard University Press.Google Scholar
Francis, J. (1976), Investments: Analysis and Management, Mc Graw Hill, New York.Google Scholar
Fuller, W. (1976), An Introduction to Statistical Time Series, Wiley, London.Google Scholar
Granger, C. and Hatanaka, M. (1964), Spectral Analysis of Economic Time Series, Princeton University Press.Google Scholar
Granger, C. (1967), New Techniques for Analyzing Time Series and their Place in Econometrics, reprinted (p. 423–442) in Shubik, M. Essays in Mathematical Economics, Princeton University Press.Google Scholar
Hollander, M. and Wolfe, D. (1973), Nonparametric Statistical Methods, Wiley, London.Google Scholar
Jenkins, G. and Watts, D. (1968), Spectral Analysis and its Applications, Holden Day, San Francisco.Google Scholar
Jennergren, L. and Korsvold, P. (1975), The Nonrandom Character of Norwegian and Swedish Stock Market Prices, in Elton, and Gruber, , International Capital Markets, North-Holland Publishing Cy, Amsterdam.Google Scholar
Koopmans, L. (1974), The Spectral Analysis of Time Series, Academic Press, New York.Google Scholar
Lehmann, E. and D’Abrera, H. (1975), Nonparametrics, Holden Day, San Francisco.Google Scholar
Naylor, T. (1969), The Design of Computer Simulation Experiments, Duke University Press.Google Scholar
Rayner, J. (1971), An Introduction to Spectral Analysis, Pion Limited, London.Google Scholar
Regidor, B. and Sercu, P. (1976), Behavior of Share Prices of the Brussels Stock Exchange, Tijdschrift voor Economie en Management, 21°jg.Google Scholar
Solnik, B. (1973), A note on the Validity of the Random Walk for European Stock Prices, Journal of Finance, Vol. 28, nr 5.Google Scholar
Wilks, S. (1963), Mathematical Statistics, Wiley, London.Google Scholar
Working, H. (1964), A Note on the Correlation of First Differences of Averages in a Random Chain, reprinted in Cootner, P. The Random Character of Stock Market Prices, M.I.T. Press.Google Scholar
Wouters, P. (1971), De Waarschijnlijkheidsbenadering van het Koersverloop van Aandelen, Tijdschrift voor Economie.Google Scholar
Yamane, T. (1968), Mathematics for Economists, Prentice Hall, New Jersey.Google Scholar
Zygmund, A. (1966), Trigonometric Series, Cambridge University Press, New York.Google Scholar