Investigating the Long-Term Memory of Total Price Index of the Tehran Stock Exchange (A Case Study: Pharmaceutical Industry)

Document Type : Original Article

Authors

1 Associate Professor, A Faculty Member of the Department of Accounting, Payame Noor University

2 Instructor, A Faculty Member of the Department of Accounting, Payame Noor University

3 Ph. D. Student in Accounting, Instructor, Payame Noor University

Abstract

Introduction: This study meets two objectives: first, investigating the presence of long-term memory of total price index in the pharmaceutical industry of the Tehran Stock Exchange; second, evaluating the accuracy of predicting models which include the long-term memory of the total price index of this industry.
Method: In this research, the methods of Maximum Likelihood (MLE), Whittle, GPH, and Sperio have been used in order to evaluate the fractional integration parameter (market memory). In the beginning, among the four above-mentioned methods, two methods of MLE and Whittle could process the best pattern of ARFIMA to the data. Then, by using statistics and choosing criteria for the best one, this pattern was selected as the best for evaluating by MLE method.
Results:  The total price index in the pharmaceutical industry of the Tehran Stock Exchange has a long-term memory, and the best pattern for predicting the stock total price index of pharmaceutical industry is ARFIMA (1, 0.13, 1).
Conclusion: It can be claimed with high certainty that the time-series of total price index in the pharmaceutical industry of the Tehran Stock Exchange has a long-term memory. The existence of this feature is a good reason for rejecting the weak shape of the market efficiency assumption. According to this assumption, the price of assets should not be predictable by historical data. The existence of long-term memory of total price index in the pharmaceutical industry indicates self-correlation among evidence with high intervals. Thus, it is possible to use historical data in order to predict the future indices which will be provided by applying a profitable strategy.

Keywords

References

1        Yajima, Y. (1985). “On Estimation of Long-Memory Time Series Models”. Australian & New Zealand Journal of Statistics, Vol. 27, No. 3, pp. 303-320.
2        Sho-araei, S. and M. Sanaei Allam (2010). “Investigating the Existence of Long-Term Memory in the Tehran Stock Exchange and Evaluating the Models including Long-Term Memory”. Financial Accounting Research, Vol. 2, No. 4, pp. 173-186. [In Persian]
3        Abbasi, E.; Ahmadi, H.; and E. Heydari (2013). “Ranking of Pharmaceutical Companies Using an Integrative Approach of Multi-Standard Decision-Making and Genetic Algorithm”. Journal of Health Accounting, Vol. 2, No. 1, Issue. 3, pp. 57-77. [In Persian]
4        Mandelbrot, B. B. and V. Ness (1968). “Fractional Brownian Motions, Fractional Noises and Applications”. SIAM Review, Vol. 10, No. 4, pp. 422-237.
5        Granger, C. W. and R. Joyeux (1980). “An Introduction to Long-Memory Time Series and Fractional Differencing”. Journal of Time Series Analysis, Vol. 1, No.1, pp. 15-39.
6        Hosking, J. R. M. (1981). “Fractional Differencing”. Biometrika. Vol. 68, No.1, pp. 165-176.
7        Green, W. H. (2003). Econometric Analysis. 5th Edition, New Jersey: Prentice Hall.
8        Barkoulas, J. T.; Baum, C. F.; and N. Travlos (2000). “Long Memory in the Greek Stock Market”. Applied Financial Economics. Vol. 10, Issue. 2, pp. 177-184.
9        Baillie Richard, T. (1996). “Long Memory Processes and Fractional Integration in Econometrics”. Journal of Econometrics. Vol. 73, No. 5, pp. 5-59.
10    Diebolt, C. and V. Guiraud (2005). “A Note on Long Memory Time Series”. Quality and Quantity, Vol. 39, No. 6, pp. 827-836.
11    Erfani, A. (2008). “Investigating Long-Term Memory of Total Price Index of Tehran Stock Exchange”. Humanities and Social Sciences Research, Vol. 8, No. 28, pp. 77-93. [In Persian]
12    Keshavarz, H. and B. Samadi (2009). “Evaluating and Predicting the Efficiency of  Volatility of the Tehran Stock Exchange and Comparing the Accuracy of Methods in Estimating Value at Risk: An Application of FIGARCH Family Models”, Tehran Journal of Economic Research, Vol. 44, No. 1, pp. 163-180. [In Persian]
13    Mahmoudi, V.; Mohammadi, S.; and H. Chitsazan (2010). “Investigating the Trend of Long-Term Memory in Global Markets of Oil”, Economic Modeling Research, Vol. 1, No. 1, pp. 29-48. [In Persian]
14    Mohamadi, S. and H. Chitsazan (2011). “Investigating the Long-Term Memory of Tehran Stock Exchange”. Economic Research, Vol. 45, pp. 207-226. [In Persian]
15    Dadashi, I.; Asghari, M.; Zarei, S.; and M. Jaffari Baei (2013). “Examining the Effect of Capital Structure and Financing on the Technical Efficiency of Pharmaceutical Companies Listed on Tehran Stock Exchange”. Journal of Health Accounting, Vol. 2, No. 1, Issue. 3, pp.1-19. [In Persian]
16    Mandelbrot, B. B. and V. Ness (1968). “Fractional Brownian Motions, Fractional Noises and Applications”. SIAM Review. Vol. 10, No. 4, pp. 422-437.
17    Greene, M. and B. Fielitz (1977). “Long Term Dependence in Common Stock Returns”. Journal of Financial Economics, Vol. 5, No. 4, pp. 339-349.
18    Lo, A. (1991). “Long Term Memory in Stock Market Prices”. Econometrica, Vol. 59, No. 5, pp. 1279-1313.
19    Crato, N. and P. J. De Lima (1994). “Long-Range Dependence in the Conditional Variance of Stock Returns”. Economics Letters, Vol. 5, No. 8, pp. 281-285.
20    Berg, L. (1998). “Short and Long-Run Dependence in Swedish Stock Returns”. Applied Financial Economics, Vol. 8, No. 4, pp. 435-443.
21    Grau-Carles, P. (2000). “Empirical Evidence of Long-Range Correlations in Stock Returns”. Statistical Mechanics and its Applications, Vol. 299, No. 3, pp. 396-404.
22    Olan, T. H. (2002). “Long Memory in Stock Returns: Some International Evidence”. Applied Financial Economics, Vol. 12, No. 10, pp. 725-729.
23    Elder, J. and A. Serletis (2008). “Long Memory in Energy Futures Prices”. Review of Financial Economics, Vol. 17, No. 2, pp. 146-155.
24    Alagide, P. (2011). “Return Behavior in Africa’s Emerging Equity Markets”. The Quarterly Review of Economics and Finance, No. 51, Issue. 2, pp. 133-140.
25    Kittiakarasakun, J. and Y. Tse (2011). “Modeling the Fat Tails in Asian Stock Markets”. International Review of Economics and Finance, Vol. 21, No. 20, pp. 430-440.
Journal of Health Accounting
Volume 2, Issue 4 - Serial Number 6
December 2014
Pages 78-96

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