Sử dụng hình mẫu khuyết và Entropy hoán vị để kiểm định tính hiệu quả thông tin trên thị trường chứng khoán của các quốc gia ASEAN
DOI:
https://doi.org/10.24311/jabes/2018.29.11.696Keywords:
Efficient market, Bandt and Pompe method, Forbidden pattern, Permutation Entropy, Normalized permutation EntropyAbstract
his study applies the method proposed by Bandt and Pompe (2002) to daily stock index series collected from six ASEAN countries including Philippines, Vietnam, Malaysia, Indonesia, Thailand, and Singapore over the period 01/2000–11/2018 to look for the existence of a forbidden pattern and to calculate normalized permutation Entropy. The research results show that there exists a forbidden pattern in all 6 stock markets. The probability of different permutation patterns is also different. These are evidence for inefficient markets. Additionally, the result of computed normalized permutation Entropy using on stock index series also demonstrates the inefficiency of the market when this value does not reach its maximum value. This article also suggests that using a series of stock index will help to find evidence of an inefficient market much more powerful than using a series of market returns.
References
Aghamohammadi, C., Ebrahimian, M., & Tahmooresi, H. (2014). Permutation approach, high frequency trading and variety of micro patterns in financial time series.
Physica A: Statistical Mechanics and its Applications, 413, 25–130.
Alvarez-Ramirez, J., Alvarez, J., & Solis, R. (2010). Crude oil market efficiency and modeling: Insights from the multiscaling autocorrelation pattern. Energy Economics, 32(5), 993–1000.
Amigó, J. M., & Kennel, M. B. (2007). Topological permutation entropy. Physica D: Nonlinear Phenomena, 231(2), 137–142.
Bandt, C., & Pompe, B. (2002). Permutation entropy: A natural complexity measure for time series. Physical Review Letters, 88(17), 174102:1–174102:4.
Bachelier, L. (1900). Théorie De La Spéculation. Ph.D. Thesis. Sorbonne, Paris.
Cánovas, J. S., Guillamón, A., & Ruiz, M. C. (2011). Using permutations to find structural changes in time series. Fluctuation and Noise Letters, 10(01), 13–30.
Chen, S., Shang, P., & Wu, Y. (2018). Weighted multiscale Rényi permutation entropy of nonlinear time series. Physica A: Statistical Mechanics and its Applications, 496, 548–570.
Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383–417.
Matilla-García, M., & Ruiz Marín, M. (2008). A non-parametric independence test using permutation entropy. Journal of Econometrics, 144(1), 139–155.
Pincus, S. M. (1991). Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences, 88(6), 2297–2301.
Richman, J. S., & Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology-Heart and Circulatory Physiology, 278(6), H2039–H2049.
Risso, W. A. (2008). The informational efficiency and the financial crashes. Research in International Business and Finance, 22(3), 396–408.
Trần Thị Tuấn Anh. (2018). Đo lường mức độ hiệu quả thông tin trên thị trường chứng khoán các nước Đông Nam Á bằng Shannon Entropy. Tạp chí Kinh tế và Phát triển, 252(6), 22–29.
Zanin, M., Zunino, L., Rosso, O. A., & Papo, D. (2012). Permutation entropy and its main biomedical and econophysics applications: A review. Entropy, 14(8), 1553–1577.
Zhang, Y., & Shang, P. (2017). Permutation entropy analysis of financial time series based on Hill’s diversity number. Communications in Nonlinear Science and Numerical Simulation, 53, 288–298.
Zunino, L., Zanin, M., Tabak, B. M., Pérez, D. G., & Rosso, O. A. (2009). Forbidden patterns, permutation entropy and stock market inefficiency. Physica A: Statistical Mechanics and Its Applications, 388(14), 2854–2864.
Zunino, L., Tabak, B. M., Serinaldi, F., Zanin, M., Pérez, D. G., & Rosso, O. A. (2011). Commodity predictability analysis with a permutation information theory approach. Physica A: Statistical Mechanics and Its Applications, 390(5), 876–890.
Zunino, L., Olivares, F., Scholkmann, F., & Rosso, O. A. (2017). Permutation entropy based time series analysis: Equalities in the input signal can lead to false conclusions. Physics Letters A, 381(22), 1883–1892.
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