چکیده:
The issue of behavioral finance is one of the new debates raised by some financial pundits over the past two decades. The unknown factors affecting stock price changes are always a reason to use stock price prediction. In most predictive models, the system performs prediction using only one indicator, but in the proposed model in this study, a two-level system of multilayered perceptron neural networks is presented, which uses several indicators for prediction. In this study, required information of Tehran stock exchange price indicators, for fiscal years 2012 - 2017 was collected. In order to analyze the financial behavior, after examining the effect of each behavioral factor on the investment of financial assets, the results show that all factors other than "over-confidence" affect investment, but the effectiveness of each factor, including "relative profit and loss", " disposition effect", "conservatism", "herd behavior", " representativeness", " ownership" and " regret aversion" is different. Among these factors, the "relative profit and loss" has had the most impact on the investment of financial assets in the stock exchange, and the " regret aversion" has the least, which proceeding a direct impact on the stock price index. Also, for better training of the neural network and consequently improving the results, grasshopper optimization algorithm is used to select the best samples. The results show that the proposed model could have lower predictive error than other models.
خلاصه ماشینی:
The present research pursues the following scientific objectives: - Explaining the selection of the best samples as well as the effective factors in financial time series prediction with the aim of increasing prediction accuracy - Examining and comparing the behavioral factors of Tehran Stock Exchange investors, while considering the prediction of company stock prices alongside other factors, including regret aversion, endowment effect, mental accounting, overconfidence, representativeness heuristic, herd behavior, conservatism, and the ownership effect on financial asset investment Presenting a model based on investors' financial behavior for predicting stock prices using metaheuristic neural network methods Seyyed Hossein MirAlavi 1 / Zahra Poorzamani 2 / Azita Jahanshad 3 Abstract Behavioral finance is one of the new topics that has been raised by some financial thinkers over the past two decades.
Associate Professor, faculty member of Islamic Azad University, Central Tehran Branch, Accounting Department, Tehran, Iran Providing a Model based on the financial behavior of investors in order to Predict stock Prices using ultra-innovative Methods of neural networks Seyyed Hosein Miralavi4 5 Zahra Pourzamani 6 Azita Jahanshad Abstract: The issue of behavioral finance is one of the new debates raised by some financial pundits over the past two decades.
In the proposed model in this research, a two-level system of multilayer perceptron neural networks is suggested, and several indicators are used for prediction; also, for better training of the neural network and consequently improving the obtained results, the locust optimization algorithm has been used to select the best samples.
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