Abstract
Modelling and forecasting financial data is an important problem which has received a lot of attention especially for the intrinsic difficulty in practical applications. The present paper investigates the weak form efficiency of some selected European markets: AEX, CAC40, DAX, FTSE100, FTSEMIB, IBEX35. In order to keep into account nonlinear structures usually found in returns time series data, a non parametric test based on neural network models has been employed. The test procedure has been structured as a multiple testing scheme in order to avoid any data snooping problem and to keep under control the familywise error rate. For sake of comparison we also discuss the results obtained by applying some classical and well known tests based on the Random Walk Hypotheses. The data analysis results clearly show that ignoring the multiple testing structure of these latter test might lead to spurious results.
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References
Antoniou, A., Ergul, N., Holmes, P.: Market Efficiency, Thin Trading and Non-linear Behaviour: Evidence from an Emerging Market. Eur. Financ. Manag. 3(2), 175–190 (1997)
Charles, A., Darné, O.: Variance-Ratio tests of random walk: an overview. J. Econ. Surv. 23(3), 503–527 (2009)
Choi, I.: Testing the random walk hypothesis for real exchange rates. J. Appl. Econom. 14, 293–308 (1999)
Chow, K.V., Denning, K.C.: A Simple Multiple Variance Ratio Test. J. Econom. 58, 385–401 (1993)
Diebold, F.X., Nason, J.A.: Nonparametric exchange rate prediction. J. Int. Econ. 28, 315–332 (1990)
Fama, E.: Market efficiency, long-term returns, and behavioral finance. J. Financ. Econ. 49, 283–306 (1998)
Kim, J.H.: Automatic Variance Ratio Test under Conditional Heteroskedascity. Financ. Res. Letters 6(3), 179–185 (2009)
Kim, Y., Kim, T.-H.: Variance-ratio Tests Robust to a Break in Drift. Eur. J. Pure and Appl. Math. 3(3), 502–518 (2010)
La Rocca, M., Perna, C.: Variable selection in neural network regression models with dependent data: a subsampling approach, Comput. Stat. Data An. 48, 415–429 (2005a)
La Rocca, M., Perna, C.: Neural network modeling by subsampling. In: Cabestany, J., Prieto, A., Sandoval, F. (eds.) Computational Intelligence and Bioinspired Computational Intelligence and Bioinspired Systems. Lecture Notes in Computer Science, p. 3512. Springer (2005b)
La Rocca, M., Perna, C.: Neural Network Modelling with Applications to Euro Exchange Rates. In: ai]_Kontoghiorghes E. J., Rustern B., Winker P. (eds.) Computational Methods in Financial Engineering, pp. 163–189 (2009)
Lim, K.P., Brooks, R.D., Hinich M.J.: Nonlinear serial dependence and the weak-form efficiency of Asian emerging stock markets. J. Int. Financ. Mark., Inst. and Money18(5), 527–544 (2008)
Lo, A.W., MacKinlay, A.C.: Stock market prices do not follow random walks: evidence from a simple specification test. Rev. Financ. Stud. 1(1), 41–66 (1988)
Politis, D.N., Romano, J.P.: The Stationary Bootstrap. J. Am. Stat. Assoc. 89, 1303–1313 (1994)
Romano, J.P., Wolf, M.: Exact and approximate stepdown methods for multiple hypothesis testing. J. Am. Stat. Assoc. 100, 94–108 (2005)
Romano, J.P., Wolf, M.: Stepwise multiple testing as formalized data snooping. Econometrica 73, 1237–1282 (2005)
Worthington, A., Higgs, H.: Random walks and market efficiency in European equity markets. Glob. J. Financ. and Econ. 1(1), 59–78 (2004)
Wright, J.H.: Alternative Variance-Ratio Tests Using Ranks and Signs. J. Bus. & Econ. Stat. 18, 1–9 (2000)
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Albano, G., La Rocca, M., Perna, C. (2014). Weak Form Efficiency of Selected European Stock Markets: Alternative Testing Approaches. In: Corazza, M., Pizzi, C. (eds) Mathematical and Statistical Methods for Actuarial Sciences and Finance. Springer, Cham. https://doi.org/10.1007/978-3-319-02499-8_1
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DOI: https://doi.org/10.1007/978-3-319-02499-8_1
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02498-1
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