Predicting returns with the least error is one of the most important issues in financial markets that has been considered by many researchers in recent decades .Traditional linear and nonlinear models due to the inefficiency of linear models in market turbulence, the la More
Predicting returns with the least error is one of the most important issues in financial markets that has been considered by many researchers in recent decades .Traditional linear and nonlinear models due to the inefficiency of linear models in market turbulence, the lack of correct extraction of the conditional distribution form of data due to the failure to record the conditional distribution dynamics in nonlinear models and the existence of limiting assumptions, it lacks the ability to predict returns in different market conditions. In order to eliminate the disadvantages of traditional models, in the present study using a new time-variable method called generalized autoregressive score (GAS) in order to predict the distribution of return of the total index of the stock exchange during the period 2010 to 2020 and for Bitcoin during the period 2014 to 2020. The results of modeling for the two assets by the new GAS model are compared with the results of the GARCH and AR models and their performance is tested for inside and outside the sample. The results show that in order to predict the daily return, the overall index of the new GAS model has a better performance and in order to predict the daily return of bitcoin, the GARCH model has been preferred.
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The way of allocating money to stock is the one of investors’ concerns. Investors mentally represent the stock by the distribution of its past returns and then evaluate this distribution in the way described by prospect theory. Therefore, prospect theory value as More
The way of allocating money to stock is the one of investors’ concerns. Investors mentally represent the stock by the distribution of its past returns and then evaluate this distribution in the way described by prospect theory. Therefore, prospect theory value as an influential factor in explaining stock returns has attracted many researchers. The purpose of this study was to investigate the effect of the prospect theory on future stock returns in listed companies in Tehran Stock Exchange. The statistical sample of the research, which has been selected by systematic elimination method, includes 104 companies from the companies listed in the Tehran Stock Exchange during the years 2012 to 2016. In this research, Fama and Franch model (1992) have been used to test the research hypotheses. The research model is fitted twice separately, once using panel data and again by the time series method, by the formulation of the portfolios. The results of the research show that the prospect theory value has a negative effect on stock returns.
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Herd behavior is one of the most important behavioral biases in financial markets and is one of the determinants of financial crises. Given that herd behavior directly affects price, presenting a model based solely on past prices, with good predictability, indicates the More
Herd behavior is one of the most important behavioral biases in financial markets and is one of the determinants of financial crises. Given that herd behavior directly affects price, presenting a model based solely on past prices, with good predictability, indicates the existence of market herd behavior. This article aims to investigate the existence of herd behavior in Tehran Stock Exchange and presents a new nonlinear variable time model called Generalized Autoregressive Score (GAS) and has been compared with traditional GARCH and AR nonlinear models. in order to predict the distribution of return of the total index of the stock exchange during the period 2010 to 2020. The results of modeling for the asset by the new GAS model are compared with the results of the GARCH and AR models and their performance is tested for inside and outside the sample. ample in the internal and external tests show that the new GAS model is more accurate than the traditional GARCH and AR models in predicting the daily return distribution of the total index of the Tehran Stock Exchange and also the presence of herd behavior in Iran's capital market has been approved.
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Identifying the distributional behavior of returns of risky assets is one of the necessities that has attracted the attention of many researchers. Because a more accurate knowledge and understanding of the distribution behavior of returns in them allows for more accurat More
Identifying the distributional behavior of returns of risky assets is one of the necessities that has attracted the attention of many researchers. Because a more accurate knowledge and understanding of the distribution behavior of returns in them allows for more accurate predictions of the future state of the market, especially in determining the risk-exposed value of these assets, which has a direct relationship with the distribution form of returns. The aim of the current research is to investigate the distributional changes of financial asset returns in the periods before and after covid-19 based on power law, stretched exponential function and Gaussian q-functions.In this regard, 3 variables: stock market index, gold price and exchange rate were investigated and their related Information was collected in each of the trading days during the period of 2016-03-26 to 2023-01-19 .In order to test the hypotheses, by using the Kolmogorov-Smirnov test, the empirical distribution of returns was compared with each of the mentioned distributions. The results showed that the logarithmic distributions of these assets do not follow any of the probability distributions obtained from the power law, stretched exponential and q-Gaussian.
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