Hossein Naderi; Mehrdad Ghanbari; Babak Jamshidi Navid; Arash Nademi
Abstract
The modeling of strategies for buying and selling in Stock Market Investment has been the object of numerous advances and uses in economic studies, both theoretically and empirically. One of the popular models in economic studies is applying the Markov Switching models for forecasting the time series ...
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The modeling of strategies for buying and selling in Stock Market Investment has been the object of numerous advances and uses in economic studies, both theoretically and empirically. One of the popular models in economic studies is applying the Markov Switching models for forecasting the time series observations based on stock prices. The semi-parametric estimators for these models are a class of popular methods that have been used extensively by researchers to increase the accuracy of estimation. The main part of these estimators is based on kernel functions. Despite the existence of many kernel functions that are capable in applications for forecasting the stock prices, there is a widely use of Gaussian kernel in these estimators. But there is a question if other types of kernel function can be used in these estimators. This paper tries to introduce the other kernel functions that can be a good replacement for this kernel function to increase the ability of Markov Switching models. We first test six popular kernel functions to find the best one based on simulation studies and then offer the new strategy of buying and selling stocks by the best kernel function selection on real data.
Fatemeh Ahmadi; Mehrdad Ghanbari; Babak Jamshidi Navid; Shahram Mami
Abstract
Nowadays, predicting the financial behavior of investors plays a crucial role in decision-making and the financial policy-making process. This study is aimed at providing a paradigm to predict the financial behavior of investors in Iran’s stock market. 24 experts were interviewed to identify the ...
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Nowadays, predicting the financial behavior of investors plays a crucial role in decision-making and the financial policy-making process. This study is aimed at providing a paradigm to predict the financial behavior of investors in Iran’s stock market. 24 experts were interviewed to identify the variables, and 24 variables were identified. The interpretive structural paradigming was carried out using a self-interaction matrix based on the experts’ opinions. The MICMAC analysis has been used to identify the types of the variables. As findings of the study, a five-level paradigm was determined, in which environmental factors and the background of financial behavior on the fifth level were the most influential variables and also arbitrage, bias, and the perceptual mistake were the most impressible variables of the paradigm on the first level. MICMAC analysis of this study suggested that the variable of environmental factors had low dependence and high efficacy. Furthermore, psychological projection, perceptual mistake, arbitrage, and bias are dependent variables with high dependence and low efficacy. Other variables are mediator variables with high dependence and effectiveness.
Ahmad Zandi; Mehrdad Ghanbari; Babak Jamshidi Navid; Alireza Moradi
Abstract
Information is like a strategic decision-making tool in which the quality of decisions will merely depend on the information used at the time of making those decisions. The purpose of this research is to assist individual investors in Tehran Stock Exchange by providing them a logistic model enabling ...
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Information is like a strategic decision-making tool in which the quality of decisions will merely depend on the information used at the time of making those decisions. The purpose of this research is to assist individual investors in Tehran Stock Exchange by providing them a logistic model enabling them to predict their trading behavior. The data in this research has been collected from the statistical population of the study through variables, believed to have effects on the investors' process of decision making. Therefore, in order to achieve the statistical data, 2400 transactions in the form of 100 transactions, including buy and sell of stock shares from March 2017 until February 2019 have respectively been collected as samples. Based on the results of the logistic regression test, the behavior of institutional investors, as well as the volume of stocks traded have a significant positive impact on the behavior of individual investors (the probability of buying shares by them) versus the Beta, earnings per share, and dividends per share that have a negative effect on the probability of purchasing shares by individual investors. The analysis of the results suggests that individual investors are mainly subject to collective behavior which in particular is the same behavior of institutional investors. On the other hand, they tend to invest in stocks with low beta (defensive stocks) along with factors such as earnings per share and dividends per share which have less impact on the probability of stock purchases by individual investors.