جستجوی مقالات مرتبط با کلیدواژه "بازار بورس" در نشریات گروه "مدیریت"

Cognitive biases and irrational behavior may be derived from collectively beliefs and values or dominant society culture. The incidence of such behavior could cause inefficiencies of capital markets, especially by investors and in decision-making process, in the stock market. In this regard, the present study, Future Oriented and Uncertainty Avoidance as the two dimensions of national culture were selected based on previous studies. Cognitive Biases in the form of five components,Overconfidence, Self-Attribution, Recenc, Ambiguity Aversion & Conservatism as a mediator variable and irrational behavior includes four dimensions, Herd Behavior, Gamblers, Law of Small Numbers & Home Bias, as a dependent variable were determined. The study sample included 424 individual investors from the stock exchange.To collecting data; Likert 5-point scale was used. Reliability and validity was confirmed respectively through Cronbach's alpha coefficient, content and construct validity using confirmatory factor analysis.In order to model fitting and testing the hypotheses, SPSS18 and Amos was used. The results show that the dimension of Future Oriented have a significant relationship with cognitive biases (significance coefficient 2.126) and irrational behaviors (3.448) while dimension of Uncertainty Avoidance was not significantly correlated with cognitive biases (-0.87) and irrational behaviors (through the mediation of cognitive biases significance coefficient 2.127).

It is well known that many financial time series show choatic behaviors. The research examines the complex dynamical behaviors using chaos test and attractor point's analysis in Tehran Stock Exchange (TSE) during the period 1385-93.
Positiveity of Lyapunov Exponent is an operational definition of chaos. We select some sample stocks from Tehran Stock Exchange and utilize three major methods for estimating Lyapunov Exponents on their prices series.
Incoming results indicate that the prices series are chaotic and estimated fixed points show local equilibriums, which are interest of policy makers, analysis and investors in financial markets.

Aim and The main purpose of our study was to investigate the relationship between the five personality dimensions with perceptual errors in decision-making, Among investors of Stock Exchange in the Isfahan.
In this descriptive- regression study, the population was consisted of all investors, Experts institutional investors, financing institutions, mutual funds, securities broker active in the Hall of stock between the second half of 2013 and the first quarter of 2014. The sample size was gained based on the statistical population and randomly selected proportional (n=120). Data were collected using the five-factor personality questionnaire included 32 items NEOPI-FF with short form. The content validity was confirmed by experts. Cronbach's alpha was used to determine the reliability of the method. In this research, data analyzed at 95 percent confidence level in two levels: descriptive and perceptive. Frequency, percentage, average and standard deviation are used in descriptive level. Regression model, Pearson correlation index, ANOVA, Kolmogorov–Smirnov are used in perceptive level. SPSS was used as software.
Findings: The findings showed a significant inverse relationship between neuroticism and halo effect, a significant positive relationship between extraversion and stereotypical error, a significant direct relationship between conscientiousness and error chance, a significant reverse relationship between openness and error chance. There was no significant relationship between Perceptual errors and personality type compatibility. Between types of stereotypical character only extraversion and conscientiousness could predict cognitive errors and other errors were not predictable by personality types.
The results indicate that in people who are dutiful personality type, the cognitive errors and random mystery are further.

Stock price prediction is a very important financial topic, and is considered a challenging task and worthy of the considerableattention received from both researchers and practitioners. Stock price series have properties of high volatility, complexity,dynamics and turbulence, thus the implicit relationship between the stock price and predictors is quite dynamic. Hence, it isdifficult to tackle the stock price prediction problems effectively by using only single soft computing technique.In this research, in the first step, the possibility of predicting stock price of National Iranian Copper Industries Company wasstudied. Then, for predicting of stock price after one day neural network of MLP by learning algorithm of Levenberg-Marquardt were used. Then optimize structure of neural network was trained with the standard BP algorithm, the learningrate is 3/0 has the best performance. And for this learning rate, sensitive of standard BP algorithm was calculated to minimizelocal. At the end, standard BP algorithm with momentum is used. The results showed that predicting by standards BPalgorithm with momentum is better than the standard BP algorithm.