BazEkon - Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie

BazEkon home page

Meny główne

Autor
Ferreira Paulo (Universidade de Évora, Portugal)
Tytuł
Apple, alphabet, or microsoft: which is the most effcient share?
Źródło
Econometric Research in Finance, 2016, vol. 1, nr 2, s. 67-79, wykr., bibliogr. 31 poz.
Słowa kluczowe
Finanse, Microsoft Windows
Finance, Microsoft Windows
Uwagi
JEL clasiffication: F36, G2, G21, G34, L1
summ.
Abstrakt
Studying the efficiency of financial assets is important because, if they are not efficient, this means that investors have some capacity to predict the behavior of those assets. In this paper, we use detrended fluctuation analysis to assess the efficiency of the three most valuable American companies, which, curiously, all happen to be from the same economic sector: Apple, Alphabet, and Microsoft. The results point to the efficiency of Apple's shares and to similar results regarding Alphabet. Only Microsoft shares show evidence of deviations from efficiency. Our results also suggest that moments of crisis could have an impact on the efficiency pattern of shares. (original abstract)
Dostępne w
Biblioteka Szkoły Głównej Handlowej
Pełny tekst
Pokaż
Bibliografia
Pokaż
  1. Anagnostidis, P., Varsakelis, C., Emmanouilides, C. (2016). Has the 2008 financial crisis affected stock market efficiency? "The case of Eurozone. Physica A", 447, 116-128.
  2. Ausloos, M. (2000). Statistical physics in foreign exchange currency and stock markets. Physica A, 285:48-65.
  3. Ausloos M, Vandewalle N, Boveroux P, Minguet A, Ivanova K. (1999). Applications of statistical physics to economic and financial topics. "Physica A", 274:229-240.
  4. Bachelier, L. (1964). Theory of Speculation. In: Cootner, P. (ed) The Random Character of Stock Prices, Cambridge, MIT Press, originally published in 1900.
  5. Bai, J., Perron, P. (1998). Estimating and testing linear models with multiple structural changes." Econometrica," 66, 47-78.
  6. Bai, J., Perron, P. (2003ª). Computation and analysis of multiple structural change models. "Journal of Applied Econometrics", 18(1), 1-22.
  7. Bai, J., Perron, P. (2003b). Critical values for multiple structural change tests." Econometrics Journal". 6, 72-78.
  8. Barkoulas, J., Baum, C. (1996). Long-term dependence in stock returns. "Economics Letters", 53:253-259.
  9. Bonanno, G., Lillo, F., Mantegna, R. (2001). "Levels of Complexity in Financial Markets. Physica A", 299:16-27.
  10. Cao, G., Zhang, M. (2015). Extreme values in the Chinese and American stock markets based on detrended fluctuation analysis." Physica A", 436(15), 25-35.
  11. Chow, G. (1960). Tests of equality between sets of coefficients in two linear regression. Econometrica. 26(3), 591-605.
  12. Christodoulou-Volosa, C., Siokis, F. (2006). Long range dependence in stock market returns. "Applied Financial Economics", 16:1331-1338.
  13. Cizeau, P., Liu, Y., Meyer, M., Peng, C., Stanley, H. (1997). Correlations in Economic Time Series. arXiv:cond-mat/9706021v1.
  14. Cont, R. (2001). Empirical Properties of Asset Returns: Stylized Facts and Statistical Issues. Quantitative Finance, I:223-236.
  15. Darbellay, G. (1998). Predictability: an Information-Theoretic Perspective. In A. Procházka, J. Uhlír, P. Rayner, N. Kingsbury (eds) Signal Analysis and Prediction, Birkhauser, Boston, 249-262.
  16. Di Matteo, T., Aste, T., Dacorogna, M. (2005). Long-term memories of developed and emerging markets: Using the scaling analysis to characterize their stage of development. "Journal of Banking & Finance", 29:827-851.
  17. Fama, E. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. "Journal of Finance", 25:383-417.
  18. Ferreira, P., A. Dionísio (2014), Revisiting serial dependence in the stock markets of the G7 countries, Portugal, Spain and Greece, "Applied Financial Economics", 24(5), 319-331.
  19. Ferreira, P., Dionísio, A. (2016). How long is the memory of the US stock market?, Physica A, 451(1): 502-506.
  20. Granger, C., Morgenstein, O. (1964). Spectral Analysis of New York Stock Market Prices. In: Cootner, P. (ed) The Random Character of Stock Prices, MIT Press, Cambridge, originally published in 1963.
  21. Granger, C., Maasoumi, E., Racine, J. (2004). A Dependence Metric for Possibly Nonlinear Processes. "Journal of Time Series Analysis", 25:649-669.
  22. Hurst, H. (1951). Long Term Storage Capacity of Reservoirs. Transactions of the American Society of Civil Engineers, 116:770-799.
  23. Kendall, M. (1953). The Analysis of Economic Time-Series. "Journal of The Royal Statistical Society", 116: 11-25.
  24. Kristoufek, L. (2015). Detrended fluctuation analysis as a regression framework: Estimating dependence at different scales. "Physical Review E", 91, 022802.
  25. Mandelbrot, B. (1977). The Fractal Geometry of Nature, New York: Freeman.
  26. Osborne, M. (1964). Brownian Motion in the Stock Prices. In: Cootner, P. (ed) The Random Character of Stock Prices, MIT Press, Cambridge, originally published in 1959.
  27. Parisi, D., Sornett, D., Helbing, D. (2013). Financial price dynamics and pedestrian counterflows: A comparison of statistical stylized facts. "Physical Review E", 87, 012804
  28. Peng, C., Buldyrev, S., Havlin, S., Simons, M., Stanley, E., Goldberger, A. (1994). Mosaic organization of DNA nucleotides. "Physical Review E", 49:1685-1689.
  29. Quandt, R. (1960). Tests of the hypothesis that a linear regression system obeys two separate regimes. "Journal of the American Statistical Association". 55, 324-330.
  30. Sadique, S., Silvapulle, P. (2001). Long-term memory in stock market returns: international evidence. "International Journal of Financial Economics". 6:59-67.
  31. Sewell, M. (2011). History of the Efficient Market Hypothesis. UCL Department of Computer Science Research Note RN/11/04.
Cytowane przez
Pokaż
ISSN
2451-1935
Język
eng
Udostępnij na Facebooku Udostępnij na Twitterze Udostępnij na Google+ Udostępnij na Pinterest Udostępnij na LinkedIn Wyślij znajomemu