Tehran Stock Market efficiency: A quantile autoregression approach
Vol 7, Issue 1, 2024
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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.
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1. 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
2. Fama EF. Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance. 1970; 25(2): 383. doi: 10.2307/2325486
3. 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
4. Cooper JCB. World stock markets: some random walk tests. Applied Economics. 1982; 14(5): 515-531. doi: 10.1080/00036848200000046
5. Panas EE. The behaviour of Athens stock prices. Applied Economics. 1990; 22(12): 1715-1727. doi: 10.1080/00036849000000077
6. 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.
7. 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
8. 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
9. Mauro P. Stock returns and output growth in emerging and advanced economies. Journal of Development Economics. 2003; 71(1): 129-153.
10. 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
11. 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
12. Enders W. Applied econometric time series. John Wiley & Sons; 2008.
13. 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
14. 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
15. 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
16. 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
17. 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
18. Jansen B. Conditional violation of weak-form market efficiency. Managerial Finance. 2020; 46(7): 935-954. doi: 10.1108/mf-06-2019-0306
19. 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
20. Koenker R, Xiao Z. Unit Root Quantile Autoregression Inference. Journal of the American Statistical Association. 2004; 99(467): 775-787. doi: 10.1198/016214504000001114
21. Koenker R, Bassett G. Regression Quantiles. Econometrica. 1978; 46(1): 33. doi: 10.2307/1913643
22. 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
23. 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
24. 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
25. 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
26. Dickey DA, Fuller WA. Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root. Econometrica. 1981; 49(4): 1057. doi: 10.2307/1912517
27. 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
28. Hamilton J. Time series econometrics. Springer International Publishing AG; 1994.
29. Perron P. Further evidence on breaking trend functions in macroeconomic variables. Journal of econometrics. 1997; 80(2): 355-385.
30. 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
31. 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
32. 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
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