Providing a Model for Predicting the Financial Behavior of Investors in the Iranian Stock Market
Author: Sallar Ashqi Kareem1
1Department of Accounting, Faculty of Administrative Sciences, Tishk International University, Erbil, Iraq
Abstract: The pattern of investor behaviour in the stock market is a complex process that is influenced by many factors such as Personality, culture, behavioral patterns, emotional and cognitive biases. The purpose of this study is to provide a model for predicting the financial behavior of investors in the Iranian stock market. In order to achieve the objectives of this study, the necessary data were collected in a time period during the 2019-2020 spring. Then fuzzy analysis method was used to analyze the data. The results showed that the self-attribution criterion with a weight of 0.107 is in the first priority, the criterion of remorse with a weight of 0.099 is in the second priority and finally the criterion of increasing risk with a weight of 0.094 is in the third priority.
Keywords: financial behavior, stock market, investors, predicting
References
Abdolrahmanian, M .H ., Torabi, T., Sadeghi Sharif, J., Darabi, r., (2016), “Presenting the Behavioral Pattern of Real Investors in the Tehran Stock Exchange”, Journal of Investment Knowledge, 113-120
Aivazian, V., Ge, Y., Qiu, J., (2005), The impact of leverage on firm Investment:Canadian Evidence. Journal of Corporate Finance, (11), pp. 277–291.
Corwin, S. A., & Coughenour, J. F. (2008). Limited attention and the allocation of effort in securities trading. The Journal of Finance, 63(6), 3031-3067.
Da, Z., Engelberg, J., & gao, P. (2011). In search of attention. The Journal of Finance, 66(5), 1461-1499.
Hasso, T., Pelster, M., & Breitmayer, B. (2020). Terror attacks and individual investor behavior: Evidence from the 2015–2017 European terror attacks. Journal of Behavioral and Experimental Finance, 28, 100397.
Huang, S. Y., Huang, Y. L., & Lin, T. C. (2019). Attention allocation and return co-movement: Evidence from repeated natural experiments. Journal of Financial Economics, 132, 369-383
Kahneman, D. (1973). Attention and effort (Vol. 1063). Englewood Cliffs, NJ: Prentice-Hall.
Kara, Y.; Boyacioglu, M.A.; Baykan, O.K. (2011). “Predicting direction of stock price index movement using artificial neuralnetworks and support vector machines: The sample of the Istanbul Stock Exchange”, Expert Systems with Applications, 38, 5311–5319.
Lepori, G. M. (2016). Air pollution and stock returns: Evidence from a natural experiment. Journal of Empirical Finance, 35, 25-42.
Lu, J., & Chou, R. K. (2012). Does the weather have impacts on returns and trading activities in order-driven stock markets? Evidence from China. Journal of Empirical Finance, 19(1), 79-93.
Majdi, M.; Ibrahim, A.; Hossam, F.; Abdelaziz, I. H.; Ala, M. A. & Seyedali, M. (2018). ). “Binary grasshopper optimisation algorithm approaches for feature selection problems”, Expert Systems with Applications, 117, 267-286.
Pashler, H., Johnston, J. C., & Ruthruff, E. (2001). Attention and performance. Annual review of psychology, 52(1), 629-651.
Ramos, S. B., Latoeiro, P., & Veiga, H. (2019). Limited attention, salience of information and stock market activity. Economic Modelling, 87, 92-108.
Swamy, V., Dharani, M., Takeda, F., 2019. Investor attention and Google Search Volume Index: evidence from an emerging market using quantile regression analysis. Res. Int. Bus. Finance 50, 1–17.
Takeda, F., & Wakao, T. (2014). Google search intensity and its relationship with returns and trading volume of Japanese stocks. Pacific-Basin Finance Journal, 27, 1-18.
Teng, M., & He, X. (2020). Air quality levels, environmental awareness and investor trading behavior: Evidence from stock market in China. Journal of Cleaner Production, 244, 118663.
Wen, F., Zou, Q., & Wang, X. (2020). The contrarian strategy of institutional investors in Chinese stock market. Finance Research Letters, 101845.
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