The optimization of Investment Beliefs in Tehran Stock Exchange Break Points Based on Heterogeneous Agent Models Framework and Agent Based Modelling with Genetic Algorithm
Subject Areas : Financial engineering
mehdi
khoshnood
1
(Department of Financial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran)
Fraydoun
Rahnamay Roodposhti
2
(Department of Financial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran)
Hashem
Nikoomaram
3
(Department of Business Management, Sience & Rasearch Branch, Islamic Azad University,Tehran, Iran.)
Keywords: Genetic Algorithm, Behavioral Finance, market sentiment, heterogeneous agent model, agent based modelling, over confidence,
Abstract :
This paper survey beliefs of investor on Tehran stock exchange at three break point date (BPD). at first three BPD with several criterion : average of the share price indices , average value of the stock market turnover , average value of the stock market capitalization .according this three BPD are : the election of Mahmood Ahmadinejad at 2005 , financial crisis at 2008 and the election of Hassan rouhani at 2013 . In addition this paper is base of Brock and Hommes heterogeneous agent model (HAM) framework. Samples are the shares of companies that 40 days before and 40 days after was traded .then with MATLAB software code was writhed and simulation done. Finding shows that strategy of contrarian trend chaser is the best and we can with genetic algorithm optimize average and standard deviation of coefficient of investment strategy and adaption with real market at break point dates.
_||_
1) Azar, Adel; Saranj; Alireza; Sadeghi Moghadam, Ali Asghar; Rajabzadeh, Ali. (2017). Factor-oriented modeling of shareholders' behavior in Iran's capital market. Financial Research Quarterly, Volume 20, Number 2, 130-150.
2) Ansari, H. A. (2011). Design and explanation of heterogeneous expectations pricing model in Tehran Stock Exchange. Tehran: University of Tehran.
3) Telangi, Ahmed. (1383). Confrontation of modern financial theory and behavioral finance. Financial Research, No. 18, 1-17.
4) Heydari, Hassan; Salmasi, Parisa; Rashki, Saeed; Hamidreza, an activist. (2018). Testing the hypothesis of heterogeneity of market actors using the STAR model with a multivariate transfer function (Tehran Stock Exchange case study). Financial knowledge of securities analysis - Year 12, Number 40, 83-99.
5) Rai, Reza; Falahpour, Saeed. (1383). Behavioral finance, a different approach in finance. Financial Research, No. 18, 27-48.
6) Saidi, Ali; Farhanian, Seyyed Mohammad Javad. (1390). Basics of economics and behavioral finance. Tehran: University of Economic Sciences Press.
7) Vakilifard, Hamidreza; Khoshnoud, Mehdi; Farughnejad, Haider. (2013). Agent-based modeling in financial markets. Investing Science Quarterly Journal, 139-158.
8) Alsulaiman, T. (2017). Behavioral Financial Networks: An Agent-Based Approach to Bounded Rational Heterogeneous Agents in the Financial Markets. New Jersey, US – 07030: STEVENS INSTITUTE OF TECHNOLOGY.
9) Boswijk, H.P., C.H. Hommes and S. Manzan. (2007). Behavioral Heterogeneity in Stock Prices, vol 31. Journal of Economic Dynamics and Control, 1938-1970.
10) Brock, W. H. (1997). A Rational route to randomness. Econometrica, Vol 65, No 5, 1059-1095.
11) Brock, W.A., Hommes, C.H. (1998). Heterogeneous beliefs and routes to chaos in a simple asset pricing model. Journal of Economic Dynamics and Control 22, 1235-1274.
12) Chiarella, Carl, Xuezhong He, Zwinkels R.C. (2010). Heterogeneous Expectations in Asset Pricing: Evidence from the S&P500. Working paper: university.
13) De Groot, J.A.N. (2011). Heterogeneous behavior in European stock market indices. Master Thesis Financial Economics., 798-844.
14) Dixit, Avinash K. (2013). Thomas Schelling's Contributions to Game Theory. Scandinavian Journal of Economics, Vol. 108, No. 2, 213-229.
15) Evstigneev, I. V., Hens, T., Schenk-Hopp´e, K. R. (2009). Evolutionary finance. Handbook of Financial Markets:, 507-566.
16) Hommes, C. (2006). Heterogeneous agent models in economics and finance. Handbook of Computational Economics, vol. 2, 1109-1186.
17) Hommes, C.H., Sonnemans, J., Tuinstra, J., van de Velden, H. (2005). Coordination of expectations in asset pricing experiments. Review of Financial Studies 18, 955–980.
18) Hommes, C.H., Wagener, F. (2008). Complex Evolutionary Systems in Behavioral Finance. Amsterdam: CeNDEF Working Papers from Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
19) Hong, H., Stein, J. (1999). A unified theory of underreaction, momentum trading and overreaction in asset markets. Journal of Finance 54, 2143–2184.
20) khashanah, K; alsulaiman, T. (2017). Connectivity, Information Jumps, and Market Stability:. Hindawi Complexity, Volume 2017, Article ID 6752086, 2-17.
21) Kukacka, J, Barunik J,. (2013). Behavioral breaks in the heterogeneous agent model: The impact of herding, overconfidence, and market sentiment. Physica A 392, 5920-5938.
22) LeBaron, B. (2006a). Agent-based computational finance. Handbook of Computational Economics. Vol. 2, 1187-1233.
23) Tesfatsion, L. (2006). Agent-based computational economics. Handbook of Computational Economics. Vol. 2., 1109-1186.
24) Tongya Wang. (2014). Behavioral Biases and Evolutionary Dynamics in an Agent-Based Financial Market. Leeds: University of Leeds.
25) Yosra M. Rekik, Hachicha w, Boujelbene Y. (2014). Agent-Based Modeling and Investors' Behavior Explanation of Asset Price Dynamics on Artificial Financial Markets. Proceedings of Economics and Finance 13, 30-46.