Tehran Stock Market efficiency: A quantile autoregression approach

Masoud Alizadeh Chamazkoti, Mehdi Fathabadi, Saleh Ghavidel Doostkouei, Mahmood Mahmoodzadeh

Article ID: 7543
Vol 7, Issue 1, 2024

VIEWS - 1568 (Abstract)

Abstract


The purpose of this paper is to evaluate the price efficiency of the Tehran Stock Market. For this aim, we used daily stock prices of 30 large companies on the Stock Exchange. In the first stage, a unit root test with the endogenous break and without a structural break was performed using augmented dickey-fuller test (ADF) tests and Phillips-perron (PP) tests. The results indicate that the price of 9 companies has a random walk process with intercept and 21 companies follow a random walk without intercept and trend component process which is known as the pure random walk process. Thus, considering the ADF and PP tests, most companies’ stock prices are efficient. Quantile autoregression results in the second stage show that the stock prices in the middle price deciles have weak efficiency, but in the lower and upper price deciles, the stock price does not follow the weak efficiency conditions. So, if the stock price deviates (up or down) from the long-term mean, the market becomes inefficient, but when the stock price is at the median level, the market is efficient. The general conclusion is that median prices are the long-term average prices that change over time, and stock prices tend to move toward that price.


Keywords


market efficiency; quantile autoregression; random walk; Tehran Stock Market

Full Text:

PDF


References


Kendall DG. Stochastic Processes Occurring in the Theory of Queues and their Analysis by the Method of the Imbedded Markov Chain. The Annals of Mathematical Statistics. 1953; 24(3): 338-354. doi: 10.1214/aoms/1177728975 Fama EF. Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance. 1970; 25(2): 383. doi: 10.2307/2325486 Sharma JL, Kennedy RE. A Comparative Analysis of Stock Price Behavior on the Bombay, London, and New York Stock Exchanges. The Journal of Financial and Quantitative Analysis. 1977; 12(3): 391. doi: 10.2307/2330542 Cooper JCB. World stock markets: some random walk tests. Applied Economics. 1982; 14(5): 515-531. doi: 10.1080/00036848200000046 Panas EE. The behaviour of Athens stock prices. Applied Economics. 1990; 22(12): 1715-1727. doi: 10.1080/00036849000000077 Chaudhuri K, Wu Y. Random walk versus breaking trend in stock prices: Evidence from emerging markets. Journal of Banking & Finance. 2003; 27(4): 575-592. Nartea GV, Valera HGA, Valera MLG. Mean reversion in Asia-Pacific stock prices: New evidence from quantile unit root tests. International Review of Economics & Finance. 2021; 73: 214-230. doi: 10.1016/j.iref.2020.12.038 Bose N. Endogenous growth and the emergence of equity finance. Journal of Development Economics. 2005; 77(1): 173-188. doi: 10.1016/j.jdeveco.2004.03.005 Mauro P. Stock returns and output growth in emerging and advanced economies. Journal of Development Economics. 2003; 71(1): 129-153. Narayan PK, Smyth R. Mean reversion versus random walk in G7 stock prices evidence from multiple trend break unit root tests. Journal of International Financial Markets, Institutions and Money. 2007; 17(2): 152-166. doi: 10.1016/j.intfin.2005.10.002 Lu YC, Chang T, Hung K, et al. Mean reversion in G-7 stock prices: Further evidence from a panel stationary test with multiple structural breaks. Mathematics and Computers in Simulation. 2010; 80(10): 2019-2025. doi: 10.1016/j.matcom.2010.02.010 Enders W. Applied econometric time series. John Wiley & Sons; 2008. Rönkkö M, Holmi J, Niskanen M, et al. The adaptive markets hypothesis: Insights into small stock market efficiency. Applied Economics. 2024; 56(25): 3048-3062. doi: 10.1080/00036846.2024.2326039 Campisi G, Muzzioli S, De Baets B. A comparison of machine learning methods for predicting the direction of the US stock market on the basis of volatility indices. International Journal of Forecasting. 2024; 40(3): 869-880. doi: 10.1016/j.ijforecast.2023.07.002 Gil-Alana LA, Infante J, Martín-Valmayor MA. Persistence and long run co-movements across stock market prices. The Quarterly Review of Economics and Finance. 2023; 89: 347-357. doi: 10.1016/j.qref.2022.10.001 Zebende GF, Santos Dias RMT, de Aguiar LC. Stock market efficiency: An intraday case of study about the G-20 group. Heliyon. 2022; 8(1): e08808. doi: 10.1016/j.heliyon.2022.e08808 Diallo OK, Mendy P, Burlea-Schiopoiu A. A method to test weak-form market efficiency from sectoral indices of the WAEMU stock exchange: A wavelet analysis. Heliyon. 2021; 7(1): e05858. doi: 10.1016/j.heliyon.2020.e05858 Jansen B. Conditional violation of weak-form market efficiency. Managerial Finance. 2020; 46(7): 935-954. doi: 10.1108/mf-06-2019-0306 Durusu-Ciftci D, Ispir MS, Kok D. Do stock markets follow a random walk? New evidence for an old question. International Review of Economics & Finance. 2019; 64: 165-175. doi: 10.1016/j.iref.2019.06.002 Koenker R, Xiao Z. Unit Root Quantile Autoregression Inference. Journal of the American Statistical Association. 2004; 99(467): 775-787. doi: 10.1198/016214504000001114 Koenker R, Bassett G. Regression Quantiles. Econometrica. 1978; 46(1): 33. doi: 10.2307/1913643 Ito M, Noda A, Wada T. International stock market efficiency: a non-Bayesian time-varying model approach. Applied Economics. 2014; 46(23): 2744-2754. doi: 10.1080/00036846.2014.909579 Ito M, Noda A, Wada T. The evolution of stock market efficiency in the US: a non-Bayesian time-varying model approach. Applied Economics. 2015; 48(7): 621-635. doi: 10.1080/00036846.2015.1083532 Noda A. A test of the adaptive market hypothesis using a time-varying AR model in Japan. Finance Research Letters. 2016; 17: 66-71. doi: 10.1016/j.frl.2016.01.004 Dickey DA, Fuller WA. Distribution of the Estimators for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association. 1979; 74(366a): 427-431. doi: 10.1080/01621459.1979.10482531 Dickey DA, Fuller WA. Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root. Econometrica. 1981; 49(4): 1057. doi: 10.2307/1912517 Dolado JJ, Jenkinson T, Sosvilla‐Rivero S. Cointegration and unit roots. Journal of Economic Surveys. 1990; 4(3): 249-273. doi: 10.1111/j.1467-6419.1990.tb00088.x Hamilton J. Time series econometrics. Springer International Publishing AG; 1994. Perron P. Further evidence on breaking trend functions in macroeconomic variables. Journal of econometrics. 1997; 80(2): 355-385. Vogelsang TJ, Perron P. Additional Tests for a Unit Root Allowing for a Break in the Trend Function at an Unknown Time. International Economic Review. 1998; 39(4): 1073. doi: 10.2307/2527353 Narayan PK, Narayan S. Mean reversion in stock prices: new evidence from panel unit root tests. Studies in Economics and Finance. 2007; 24(3): 233-244. doi: 10.1108/10867370710817419 Hamid K, Suleman MT, Shah SZA, et al. (2010). Testing the weak form of efficient market hypothesis: Empirical evidence from Asia-Pacific markets. SSRN. 2010; 58: 121-133.



DOI: https://doi.org/10.24294/fsj.v7i1.7543

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Masoud Alizadeh Chamazkoti, Mehdi Fathabadi, Saleh Ghavidel Doostkouei, Mahmood Mahmoodzadeh

License URL: https://creativecommons.org/licenses/by/4.0/