Investigation of Fractal Property Price and Stock Returns of Tehran Stock Exchange Companies Using Nonlinear ARIFMA Model

Subject Areas : Financial engineering

amirhosein abdolmaleki ¹ (Department of Accounting, Tehran South Branch, Islamic Azad University, Tehran, Iran)
mohsen hamidian ² (Department of Accounting, Tehran South Branch, Islamic Azad University, Tehran, Iran)
ali baghani ³ (Department of Accounting, Tehran South Branch, Islamic Azad University, Tehran, Iran)

Received: 2020-01-05 Accepted : 2020-03-24 Published : 2020-09-22

Keywords: Stock Returns, stock price, Fractal Characteristics, Chaos and Hurst Index,

Abstract :

Much evidence suggests that time series such as stock market prices are complex and random, which makes their changes unpredictable. However, these time series are likely to be a nonlinear dynamic or, in other words, a chaotic process and can therefore be predictable. Therefore, in this study, stock prices and stock returns of Tehran Stock Exchange companies during the period 2014-2018 and monthly intervals were tested to determine whether these variables have fractal properties in their behavior. To achieve the above objective, our model estimation is used to explain the mass fraction of moving average. The findings of the above tests indicate that stock prices and stock returns experience a turbulent and definite process. This implies that the capital market is inefficient, and because of its long-term memory, it can be useful in predicting long-term performance and may have a guide to better understanding market failure factors such as the lack of transparency of information flow and action to address it.

References:

تهرانی، رضا، انصاری، حجت‌اله و علی‌رضا سارنج (1389). بررسی وجود پدیده‌ی بازگشت به میانگین در بورس اوراق بهادار تهران، بررسی‌های حسابداری و حسابرسی، دوره‌ی ۱۵، شماره‌ی ‌‌۵۴، صص ۳۲-۱۷‌.

خواجوی، شکراله و هادی عبدی طالب‌بیگی (۱۳۹۵). تجزیه و تحلیل ابعاد فراکتال بر شاخص بازده نقدی و قیمت سهام شرکت‌های پذیرفته شده در بورس اوراق بهادار تهران، دانش سرمایه‌گذاری، سال پنجم، شماره‌ی ۱۸، صص: ۹۳-۷۹.

دانیالی ده حوض، محمود؛ منصوری، حسین. (1391). بررسی کارایی بورس اوراق بهادار تهران در سطح ضعیف و اولویت بندی عوامل مؤثر بر آن، فصلنامه پژوهشنامه اقتصادی(رویکرداسلامی- ایرانی)، 12(47): 71-96.

رهنمای رودپشتی، ف.، و کلانتری دهقی، م. (1393). "مدل‌های مولتی فراکتال درعلوم مالی: ریشه،ویژگی‌هاوکاربردهایآن‌ها". دانش مالی تحلیل اوراق بهادار، شماره 24، 47-25.

کاظمی روچی مصطفی، کیومرث بیگلر و کشاورز بهادری مهدی (1397)، تاثیر خاصیت فراکتالی شبکه بازار سهام بر بازده سهام، پایان نامه کارشناسی ارشد، موسسه آموزش عالی رجاء

محمدی، شاپور و چیت­سازیان، هستی، (۱۳۹۰). «بررسی حافظه بلندمدت بورس اوراق بهادار تهران». نشریه تحقیقات اقتصادی دانشگاه تهران/ شماره ۹۷/ ص ۲۰۲-۲۲۱.

 

Alvarez-Ramírez, J. & Rodríguez, E. (2012). Temporal variations of serial correlations of trading volume in the US stock market, Physica A, 4128-4135.

Black, E. D. (2000). Financial Market Analysis. 2nd Edition, New York: John Wiley and sons.

Chen, C., & Wang, Y. (2017). Understanding the multifractality in portfolio excessreturns. Physica A, 466, 346–355.

Ho, S.A., Machado, J.A.T., Quintino, D.D., Balthazar, J.M. (2016). Partial chaos suppression in a fractional order macroeconomic model, Mathematics and Computers in Simulation, 122: 55-68.

Mensi, W., et al. An analysis of the weak form efficiency, multifractality and long memory of global, regional and European stock markets. The Quarterly Review of Economics and Finance (2018), https://doi.org/10.1016/j.qref.2018.12.001

Rosenblum, B. and Kuttner F. (2006). Quantum Enigma: Physics Encounters Consciousness. Oxford University Press, Incorporated.

Sensoy, A., & Tabak, B. M. (2015). Time-varying long term memory in the EuropeanUnion stock markets. Physica A, 436, 147–158.

Velasquéz .T. (2009). Chaos theory and the science of fractals, and their application in risk management. Cand. merc. Copenhagen Business School, Cand.merc. Finance & Strategic Management, Supervisor: Michael Clemens.

Weiss, G. (1992). Chaos hits wall street-the theory, that is, Business Week November. pp. 138-140.

Zhang, G., & Li, J. (2018). Multifractal analysis of Shanghai and Hong Kong stockmarkets before and after the connect program. Physica A, 503, 611–622.

 

Alvarez-Ramírez, J. & Rodríguez, E. (2012). Temporal variations of serial correlations of trading volume in the US stock market, Physica A, 4128-4135.

Black, E. D. (2000). Financial Market Analysis. 2nd Edition, New York: John Wiley and sons.

Chen, C., & Wang, Y. (2017). Understanding the multifractality in portfolio excess returns. Physi

Daniali De Hoz, Mahmoud; Mansouri, Hossein. (2011). Investigating the performance of Tehran Stock Exchange at a weak level and prioritizing factors affecting it, Quarterly Journal of Economic Research (Islamic-Iranian approach), 12(47): 71-96.

Kazemi Rochi Mostafa, Kyomarth Bigler and Keshavarz Bahadri Mehdi (2017), the effect of the fractal property of the stock market network on stock returns, master's thesis, Rajah Institute of Higher Education

Khajawi, Shokrale and Hadi Abdi Talebbeigi (2015). Fractal dimension analysis on cash return index and stock price of companies listed on Tehran Stock Exchange, Investment Knowledge, Year 5, Number 18, pp: 79-93.

Mohammadi, Shapour and Chit-Sazian, Hasti, (1390). "Investigation of long-term memory of Tehran Stock Exchange". Journal of Economic Research of Tehran University / No. 97 / pp. 202-221.

Rahnema Rudpashti, F., and Kalantari Dehaghi, M. (2013). "Multifractal models in financial sciences: their origin, characteristics and applications". Financial Knowledge of Securities Analysis, No. 24, 25-47.

Tehrani, Reza, Ansari, Hojatoleh and Alireza Saranj (1389). Examining the phenomenon of mean reversion in Tehran Stock Exchange, Accounting and Auditing Reviews, Volume 15, Number 54, pp. 17-32.