The effect of value of small-volume transactions on the TEDPIX of Iran
Subject Areas :
Financial Economics
Reza Fallah-Moghaddam
1
,
Saman Babaie-Kafaki
2
1 - Department of Mathematics Education, Farhangian University, Tehran, Iran,
2 - Department of Statistics and Computer Science, Faculty of Mathematics, Semnan University, Semnan, Iran
Received: 2023-12-14
Accepted : 2024-01-30
Published : 2024-03-20
Keywords:
C13,
F19,
C19,
Technical Analysis,
Value of small-volume transactions,
D19,
Keywords: Statistical tests,
Black-Scholes Model. JEL classification: C02,
Abstract :
Abstract
The value of small-vlume transactions stock (shares + preemptive rights + shares ETF) is obtained by deducting the value of block transactions from the value of total transactions in the Iranian Stock Exchange. This research includes two main parts. Considering the importance of the Black-Scholes price model in financial mathematics, using deep mathematical tools such as nonlinear analysis, stochastic analysis, Ito's lemma and the Radon-Nikodym derivative, a theoretical mathematical result was obtained regarding the Black-Scholes price model. we found Theorem 1 in chapter (4) is actually a deep theoretical mathematical relationship for predicting the price in the future. Also, the results of this theorem were used to investigate the trend of the value of small stock transactions in the Iranian stock market in the future time frames. The second topic we discuss in this article is to investigate the impact of changes in the value of small-vlume transactions stock on Tedpix of the Iranian stock market, using modeling and mathematical tools, including the Black-Scholes model, financial technical analysis tools, statistical methods and the Hurst test.
Usually, mathematical and computational tools use stock price data to predict the trend of financial markets. Also, the number of traded shares of a stock company can be an indicator to identify the entry point for buying or the exit point for selling. Considering the extreme price fluctuations in the last few years in the Iranian stock market and the difficulty of setting a ceiling for the price, as well as the existence of processes such as capital increases in company assemblies that change the total number of tradable shares of a company, the importance of using data on the value of small stock transactions It is revealed as a combination of price and number of shares data.
In this study, in a period of about seventeen months (in which the most severe fluctuations in the history of the Iranian Stock Exchange occurred), weekly information on the average value small-volume transactions has been examined. The results of this research confirm that with the increase or decrease of the total index of the Iranian stock market, we see a similar trend in the value small-volume transactions. We also showed that the value of small stock trades is trending by using the Hurst test.
References:
فهرست منابع
زمانیان غلامرضا، جلالی ام‌البنین ، کردی تمندانی علی. (1396) رهیافت مدل احتمال مبادله آگاهانه در بررسی اثر عدم تقارن اطلاعات بر بازده سهام و حجم معاملات در شرکتهای منتخب بورس اوراق بهادار تهران. فصلنامه اقتصاد مالی 41. 43-66.
شکوه احمدعلی، دامن‌کشیده مرجان، هادی‌نژاد منیره. (1396) برآورد شدت تأثیرگذاری سیاست‌های پولی بر شاخص قیمت سهام در ایران (مطالعه موردی صنایع پتروشیمی فعال در بورس اوراق بهادار تهران). فصلنامه اقتصاد مالی 40. 139-158.
فدایی‌نژاد اسماعیل، فراهانی رضا. (1396) اثرات متغیرهای کلان اقتصادی بر شاخص کل بورس اوراق بهادار تهران. فصلنامه اقتصاد مالی 39. 1-26.
_||_
Abardanell, J. S., and Brian J. B. (1998). Abnormal returns to a fundamental analysis strategy. The Accounting Review 73 (1): 19–45.
Abardanell, J. S., and Brian J. B. (1997). Fundamental analysis, future earnings, and stock prices. Journal of Accounting Research 35(1): 1–24.
Brooks, A. (2011). Trading Price Action Trends: Technical Analysis of Price Charts Bar by Bar for the Serious Trader. Canada: John Wiley & Sons.
Chan, E. (2013). Algorithmic Trading: Winning Strategies and Their Rationale (Vol. 625). John Wiley & Sons.
Copeland, T. & Antikarov, V. (2001). Real Options. Texere LLC, New York.
Dixit, R. K. and Pindyck, R. S. (1994). Investment under Uncertainty. Princeton University Press.
Gbenro, N., Moussa, R. K. (2019). Asymmetric mean reversion in low liquid markets: Evidence from BRVM. Journal of Risk and Financial Management 12(1), 38.
Grimes, A. (2012). The Art and Science of Technical Analysis: Market Structure, Price Action, and Trading Strategies. Canada: John Wiley & Sons.
Fama, E. F. (1965). The behavior of stock-market prices. The Journal of Business, 38(1): 34-105.
Flannery, M. J., and Aris A. P. (2002). Macroeconomic Factors do influence aggregate stock returns. The Review of Financial Studies 15(3): 751–782.
Fuller, W. A. (1976). Introduction to Statistical Time Series. New York: John Wiley & Sons.
Kalantari, A., Paktinat, N. (2014). The effect of trade volume on TEPIX index in bear and bull cycles: An application of Markov-Switching model. Iranian Journal of Economic Research 19(58) 183-206. (In Persian)
Karpoff, J. M. (1987). The relation between price changes and trading Journal of Financial and Quantitative Analysis 22: 109-126.
Irwin, S. H., Park, C. H. (2007). What do we know about the profitability of technical analysis? Journal of Economic Surveys 21(4): 786–826.
Ladde, G. S. and Wu, L. (2009). Development of modified geometric Brownian motion models by using stock price data and basic statistics. Nonlinear Analysis: Theory, Methods and Applications 71(12): 1203-1208
Maheu, J. M., Mccurdy, T. H., Song, Y. (2012). Components of bull and bear markets: bull corrections and bear rallies. Journal of Business & Economic Statistics 30(3), 391-403.
Mohajer, H., Ovaghar, F. (2021). Detection of mean reversion point based on quantiles of extreme value distribution: Evidences from Iran and international markets. Journal of Decisions and Operations Research. 6(2): 288–303. (In Persian)
Moshiri, S., Kohzadi, N., Cameron, N. (2002). Testing for stochastic nonlinearity in the rational expectations permanent income hypothesis. Iranian Economic Review 6: 63-78.
Murphy, J. J. (1999). Technical Analysis of the Financial Markets. New York Institute of Finance.
Najarzadeh, R., Moosavian, A., Moamadi, M., Salehabadi, A. (2016). A sharia compliant solution for launching index derivatives in Tehran Stock Exchange, Islamic Finance Research Bi-quarterly Journal, 5: 5-38.
Narayan, P. K. (2007). Mean reversion in stock prices: New evidence from panel unit root tests. Studies in Economics and Finance, 24(3): 233-244.
Palwasha, R. I., Ahmad, N., Ahmed, R. R., Veinhardt, J., Štreimikienė, D. (2018). Speed of mean reversion: An empirical analysis of KSE, LSE and ISE indices. Technological and Economic Development of Economy, 24(4): 1435-1452.
Roozegar, R., Soufi, B., Taherizadeh, H. R. (2018). Calacualting value at risk and expected shortfall of some statistical distributions. Journal of Decisions and Operations Research, 3(1): 72-81. (In Persian)
Safari, Kh., Beshkoh, M. (2019). The role of trading volumes and liquidity on investment opportunities and capital expenditures in companies listed in the Tehran Stock Exchange, Accounting & Auditing Researches, 44: 195-210.
Shaik, M., Maheswaran, S. (2018). Expected lifetime range ratio to find mean reversion: Evidence from Indian stock market. Cogent Economics & Finance, 6(1): 1475926.
Sun, W. (2003). Relationship between trading volume and security prices and returns, MIT Laboratory for Informationand Decision System, Technical Report P-2638.
Sun, K. H. (2005). Brownian motion and the economic world-article One. Surprise 95.
Titman, S., Martin, J. (2008). Valuation. Boston: Pearson Education Inc..
Tsay, R. S. (2002). Analysis of Financial Time Series. New York: John Wiley & Sons.
Wilmott, P. (Ed.) (2005). The Best of Wilmott 1: Incorporating the Quantitative Finance Review. John Wiley & Sons.
Wu, Y., & Lux, N. (2018). UK house prices: bubbles or market efficiency? Evidence from regional analysis. Journal of Risk and Financial Management, 11(3): 54.
