درخت حوزه‌های تخصصی

Cluster fluctuations and fractal structures are important features of space-time correlation in complex financial systems. However, the microscopic mechanism of creation and expansion of these two features in financial markets remains challenging. In the current research, by using factor-based model design and considering a new interactive mechanism called multi-level clustering, the formation process of cluster fluctuations was investigated with regard to the fractal structure of financial markets. For this purpose, the daily information of the final price of 150 shares that were accepted in the Tehran Stock Exchange, after the final screening, was entered in 5 sections with 30 shares in each section, in the desired model, and they were aggregated in three stock levels., sector and market were measured. Due to the fact that some investors have a longer investment horizon in the stock market and due to the limitation of the investigated time period, the maximum investment horizon of 1000 days has been determined in the model.In addition, the data studied in the research model are from August 2012 to September 2018. The findings of the research showed that the intensity of the tendency of collec-tive behavior at the sector level is much stronger than at the market level. In addition, based on the findings of the research, it was determined that the distribution of simulation eigenvalues in three levels is significantly similar to the distribution of real data. Also, according to the investor's time horizon, the studied series always has a long-term memory for fluctuations. In addi-tion, it was found that long-term memory is directly related to fractal dimen-sions. The findings of this research, in addition to providing a new insight into the space-time correlations of financial markets, show that multi-level conglomeration is one of the mechanisms for creating the microscopic mi-crostructure of such markets. In other words, multi-level collective behavior is an important factor in the occurrence of cluster and fractal fluctuations in the market, and therefore, it should be considered from this point of view in the interpretation of the concept of risk and the definition of risk manage-ment strategies.

The main aim of this study was to identify and analyze the effect of social capital on entrepreneurial activities of Fars province specialized companies in Iran to improve their status. This descriptive research was accomplished using survey and required data was collected through questionnaire. Stratified random sampling was used and sample size was estimated to be 380 rural women. The validity and reliability of the questionnaire was confirmed by the viewpoints of professors as well as conducting a pilot study, calculating the Cronbach's alpha coefficient, respectively. Based on the results of path analysis, social capital and social networks activities had a direct and significant effect on entrepreneurial activities. Business plan writing skill, creativity, entrepreneurial motivation, family communication, supportive policies, educational-counseling policies, and business environments also had a direct and significant effect on social capital. Creativity, entrepreneurial motivation, family communication and educational policies had a direct and significant effect on social networking activities, as well. Enhancing the ability of entrepreneurs to start and continue entrepreneurial activities, paying attention to the role of social networks and their interacting as well as considering social capital as a link between business networks by identifying entrepreneurial opportunities and providing resources and facilities are essential in this regard.

One of the biggest risks in the economies of countries is the bankruptcy of industries and companies. Predicting the bankruptcy of companies and industries provides the opportunity for the government, investors, and shareholders to take strategic actions to deal withcrises and prevent potential losses. From an economic perspective, financial distress can be interpreted as the company being loss-making, in which case the company has failed to succeed. In fact, in this case, the company's rate of return is less than the cost of capital. Another form of financial distress occurs when the company fails to comply with one or more of the debt covenant terms, such as maintaining the current ratio or the special value ratio to total assets according to the contract. Therefore, there is no general agreement on the definition of financial distress, and the reasons that lead companies towards failure are very different. One possible reason for failure is the wrong direction ofthe company, which can be analyzed through the financial ratios of the companies. Since the 1960s, many studies have only examined financial ratios as indicators of failure. Therefore, the analysis is the basis for improving and developing failureprediction models. In this study, the influential variables in predicting bankruptcy were first selected using backgrounds. The most important factors were identified with the opinions of experts, and the weight of each criterion was obtained using the AHP technique. Then, these criteria were ranked using the TOPSIStechnique to identify the most important factors. After completing these steps, by entering these variables into an artificial neural network of the multilayer perceptron type as input and using the backpropagation algorithm to train the network, the prediction accuracy increased, and accurate and reliable results were obtained