Is the 52-high-price Strategy Explained by Behavioral Finance? (Uncertainty Effect)

Document Type : Original Article


1 PhD Candidate Faculty of Management and Economy, Science and Research Branch, Islamic Azad University, Tehran, Iran (corresponding author)

2 Professor and Faculty Member Department of Accounting, Faculty of Management and Economy, Science and Research Branch, Islamic Azad University,Tehran, Iran


The aim of this study is to investigate a behavioral approach by anchoring bias as a criterion to explain 52-week-high strategy and trough this we can find an explain for momentum strategy at uncertainty situation, to the companies listed on the Tehran Stock Exchange. The information uncertainty criteria include the book value to market value (BV / MV), company age (Age), the size of the entity (MV), lowest price to the highest price of the stock ratio (LHR), the standard deviation of stock returns (STD) and the standard deviation of operating cash flow (CFVOLA). To investigate this issue  four-hypotheses developed for this purpose and data of 99 companies of Tehran Stock Exchange was analyzed for the period 2007 to 2015.the research method performed by panel data analyzed, Results show that for all the variables except for STD (standard deviation of stock returns), by increasing the degree of information uncertainty, stock returns trend increases(decreases) for winning(loser) portfolios.


1)     Abu Torabi, Gholamreza; Fallahpour, Saeed, and Saadi, Rasol. (2013). The relationship between the individual daily stock returns and the highest price in last 52 weeks of in Tehran Stock Exchange
2)     Akbari, Zahra, Hakak, Muhammad. (2012). Reviews and test acceleration of the phenomenon of boom and slump, Quarterly Science and Research Journal of investment knowledge
3)     Raee, Reza, Eslami Bidgoli, Gholamreza., and Bayati, M. Mirza. (2010). Stock valuation and heterogeneity of stakeholders behavior in the Tehran Stock Exchange. Knowledge of accounting, No. 5, pp. 103-125.
4)     Raee, Reza, and Fallahpour, Saeed. (2004). Behavioral finance as a different approach in the financial sphere. Financial Research, No. 7, pp. 53-99.
5)     Rahnama Roodposhti, Faraydon; and Zand, Vahid. (2012). Book of behavioral finance, and nervous financial (fiscal new paradigm).
6)     Rahnama Roodposhti, Faraydon; Saeedi, Ali, Madanchi Zaj, Mahdi and Nikomaram, Hashem (2015). The speed of adjustment in stock prices more reactive and less reactive approach to evaluating the performance of investors and financial markets efficiency. Quarterly Science and Research Journal of Investment knowledge
7)     Zarei, Reza and Shavakhi, Alireza. (2006). Evaluate the performance of investment strategies in the Tehran Stock Exchange, Financial Research.
8)     Saeedi, Ali; Farhanian, Sayed Mohammad Javad. (2011). Basics of economics and behavioral finance, Tehran, information and exchange services company.
9)     Seif Allahi, Razeye and Kordluee, Hamidreza. (2015). A comparative study of behavioral factors in investing in financial assets. Quarterly Science and Research Journal of investment knowledge
10)  Ghalibaf Asl, Hassan and Rasekh, Somayeh. (2013). The survey of effectiveness of price limits in Tehran Stock Exchange, management studies in Iran, Issue 3, Pages 210-19.
11)  Hirshleifer, D. (2001). Investor psychology and asset pricing. Journal of        Finance,56(4), 1533-1591.
12)  Hong, H., Lim, T., Stein, J.(2000). Bad news travels slowly: size, analyst coverage and the profitability of momentum strategies. J. Finance 40, 265–295
13)  Hua, J. (2011). The impact of information uncertainty on stock price performance and managers equity financing decision.
14)  Imhoff, E.A., Lobo, G.J. (1992). The effect of ex ante earnings uncertainty on earnings response coefficients. Account. Rev. 67, 427–429
15)  Jegadeesh, N., Titman, S.(2001). Profitability of momentum strategies: an evaluation of alternative explanations. J. Finance 56, 699–720
16)  Jegadeesh, N., Titman, S.(1993). Returns to buying winners and selling losers:   implications for stock market efficiency. J. Finance 48, 65–91
17)  Jiang, G. H., Lee, C. M., & Zhang, a. Y. (2005). Information uncertainty and expected returns. Review of Accounting Studies, 10(2-3), 185-221.
18)  Kahneman, D., Tversky, A. (1982). Judgment Under Uncertainty: Heuristics and Biases. Cambridge University Press, Cambridge
19)  Lang, M., Lundholm, R. (1996).Corporate disclosure policy and analyst behavior. Account. Rev. 71, 467–492
20)  Lim, T. (2001). Rationality and analysts’ forecast bias. J. Finance 56, 369–385
21)  Lo, A., MacKinlay, C. (1990). When are contrarian profits due to stock market overreaction? Rev. Financ. Stud. 3, 175–205
22)  Moskowitz, T., Grinblatt, M.(1999). Do industries explain momentum? J. Finance 54, 1249–1290
23)  Rey, D., Schmid,M.(2007) Feasible momentum strategies: evidence from the     Swiss stock market. Financ. Mark. Portf. Manag. 21, 325–352
24)  Rouwenhorst, K.(1998). International momentum strategies. J. Finance 53, 267–284
25)  Shleifer, A., & Vishny, a. R. (1997). The limits of arbitrage. Journal of Finance,52(1), 35-55.
26)  Siganos, A.(2010). Can small investors exploit the momentum effect. Financ. Mark. Portf. Manag. 24(2), 171– 192
27)  Tversky, A., Kahneman, D. (1974). Judgment under uncertainty: heuristics and biases. Science 185, 1124–1131.
28)  Zhang, F.(2006). Information uncertainty and stock returns. J. Finance 61, 105–136
29)  Zhang, X. (2006). Information uncertainty and stock returns. Journal of Finance, 61 (1), 105-136.