Evaluation of long-term memory Tehran Stock Exchange index

اقتصاد پولی مالی

Article 6, Volume 25, Issue 15, May 2018, Pages 21-48   

Document Type: پژوهشی

DOI: 10.22067/pm.v25i16.67538

Authors

mehdi moradi^{* 1}; mostafa esmaeilpoor²

¹ferdowsi university

²azad university

Abstract

Abstract: The results of various studies on the efficiency of some stock exchanges show that these markets are inefficient even in weak form. Therefore, considering the inefficiencies of these markets, one can conclude that by trading on the basis of a set of information, economic profit can be obtained. It can also be said that inefficiency is a sign that models can be designed to generate unusual profits. Many studies designed to evaluate the long-term memory and prediction of future models is conducted.In this research, we tried to use an ARFIMA method and a periodogram analysis method (which is a time series analysis method) from another angle to study long-term memory as well as prediction model design. The main objective of this research is the analysis of time series of total، financial and industry index on a daily basis from 27 to 2015/07/15 has been collected. The results show that the index of Tehran Stock Exchange has a memory with the long-term، also the periodogram analysis method is the best method for the prediction of the stock exchange and in the end it can be stated that the ulterior period (repeatableperiod) is exsintence in the index exchange data.

Keywords

Keywords: Forecasts; Industry Index; total Index; Financial Index; long Therm Memory; ARFIMA Model; periodogram Analysis

References

[1] Allagidede, P. (2011). “Return Behavior in Africa s Emerging Equity Markets”. The Quarterly Review of Economics and Finance, 51, 133-140.

[2] Alam, Z; Siddikee, N; Masukujjaman, M. (2013). “Forecasting Volatility of Stock Indices with ARCH Model”. Journal of Financial Research, 4. (2), 126-143.

[3] Araujo, de A; Ricardo, Ferreira, A.E. Tiago. (2009). “A Morphological-Rank-Linear evolutionary method for stock market prediction” Information Sciences, 237, 3-17.

[4] Asadi, Shahrokh& Hadavandi Esmaeil& Mehmanpazir Farhad&Nakhostin Mohammad Masoud. (2012). “Hybridization of evolutionary Levenberg–Marquardt neural networks and data pre-processing for stock market prediction” Knowledge-Based Systems, 35, 245-258.

[5] Barkoulas, J. T; & Baum, C. F. (1996). “Long Term Dependence in StockReturns”. Economics Letters. 53( 3). 253-259.

[6] Diebolt ,C and V. Guiraud. (2005) " A note on long memory time series" Quality and Quantity . 39(6):827-836.

[7] Erfani, Alireza. (1388). Prediction of total index, ARIFMA Tehran Stock Exchange with model. Economic researches, University of Tehran, period 86. (in Persian)

[8] Green, L; Myerson, J. (2003). “Discounting delayedband probabilistic rewards”, Journal of Economis Psychology, 24 (5), 619-635.

[9] Komijani, Akbar., Naderi, Esmaeel., Gandali Alijani, Nadia. (1394). Long-term memory survey on Tehran Stock Exchange index stock returns. Quarterly journal of treasury of the third year, 3, 67-82(in Persian).

[10] Majhi, Ritanjali; Panda, G; Sahoo, G. (2009) “Development and performance evaluation of FLANN based model for forecasting of stock markets” Expert Systems with Applications, 36( 3), 6800-6805.

[11] Man,K.S. (2003).“Longmemorytime series and short term forecasts”.International Journal of Forecasting, 19( 3), 477-491.

[12] Moradi, Mahdi, Sadooghi yazdi, Hadi, Abdollahian, Javad., (1394). New engineering approach to predict exchange index fluctuations. Journal of Business Accounting, Shiraz University, 7( 2), 117-148. (in Persian).

[13] Murari, K. (2013). “Volatility Modeling and Forecasting for Banking Stock Returns”. International Journal of Banking, Risk and Insurance, 1( 2) , 11-22.

[14] Poon, S; C. Granger. (2003). "Forecasting Volatility in Financial Markets: A Review",Journal of Economic Literature. 41( 2), 478-539.

[15] Sheraee, Saeed., Sanaee, Alam Mohsen. (1389). Investigating the long-term sustainability of Tehtan Stack Exchange and evaluating long-term memory models. Journal of Financial Accounting Research, second year, 4( 6),173-186(in Persian).

[16] Wright ,J. (1996), "Testing for structural break at unknown date with long –memory disturbances" Journal of time series analysis, 19(3),369-376.