جلال حقیقت منفرد

Accurate stock market prediction can assist in an efficient portfolio and risk management. However, accurately predicting stock price trends still is an elusive goal, not only because the stock market is affected by policies, market environment, and market sentiment, but also because stock price data is inherently complex, noisy, and nonlinear. Recently, the rapid development of deep learning can make the classifiers more robust, which can be used to solve nonlinear problems. This study proposes a hybrid framework using Long Short-Term Memory, Autoencoder, and Deep Neural Networks (LSTM-AE-DNNs). Specifically, LSTM-AE is responsible for extracting relevant features, and in order to predict price movement, the features are fed into two deep learning models based on a recurrent neural network (RNN) and multilayer perceptron (MLP). The dataset used for this is Dow Jones daily stock for 2008-2018, which was used in this article. Besides, to further assess the prediction performance of the proposed model, original stock features are fed to the single RNN and MLP models. The results showed that the proposed model gives the more accurate and best results compared to another. In particular, LSTM-AE+RNN shows a better performance than the LSTM-AE+MLP. In addition, hybrid models show better performance compared to a single DNN fed with the all-stock features directly.

Purpose: The aim of this study was to investigate the relationship between the learning organization and the components of the organization agility model in the metal production industries of Kaveh Industrial Town. Methodology: The research method was applied in terms of purpose and mixed in terms of data type. The statistical population of the study consisted of two parts: qualitative and quantitative. The qualitative part includes experts who use the purposeful sampling method and the principle of saturation of 25 people and the quantitative part includes all employees of the metal products industry of Kaveh industrial town, who use the purposeful sampling method with special conditions in mind as 300 people. Sample size was selected. In the qualitative section, the fuzzy Delphi process was used for the relationship between the learning organization and the components of the agility model of the organization, and the interpretive structural model (ISM) was used for the classification and leveling of the agility components. In the quantitative part, the structural equation model was used using Smart PLS software. Findings: The findings showed that the greatest impact on agility by transformational leadership, followed by learning organization, accountability, flexibility, organizational change, information technology and customer satisfaction. Conclusion: The results showed that the factors of information technology, transformational leadership, learning organization, flexibility, responsiveness, organizational change and customer satisfaction are the main structures of the organizational agility model of metal products industries in Kaveh industrial town.

Purpose of this study is to provide a structured model for the development of social entrepreneurship in the higher education system of humanities post-graduate. According to the research purpose, theoretical framework was established with three main dimensions including needs and incentives, features and measures and the consequences of social entrepreneurship development. This model has been led to interview protocol. Then researcher done 10 interviews with experts by telephone, Skype and face-to-face methods. These interviews were then coding and recoding and the concepts and categories were extracted. Then, the relationships between the categories were adjusted based on the model derived from the literature. Then with using the confirmatory factor analysis and ISM and SEM techniques, the dimensions and components of the designed model were confirmed and structured.

Network data envelopment analysis models assess efficiency of decision-making unit and its sections using historical data but fail to measure efficiency of its units and their internal stages in the future. In this paper we aim to measure efficiency of stages of bank branches and obtain efficiency trend of stages during the time, then to estimate their efficiency in the future therefore we can be aware of stages inefficiency before occurrence and prevent them. First, a two-stage structure including deposit collection and loan giving was designed for bank branches using literature review and comments of experts. Human forces and fixed assets were considered as input variables of the first stage, deposit as mediator variable, delayed claims as interim variable, and loan amount as output variable of the second stage. Then, a dynamic network data envelopment analysis model was formulated and stages efficiency were obtained for 16 consecutive periods. Therefore, efficiency trend of stages was obtained during the time. In the following, efficiency of various stages of branches were estimated using artificial neural network and some recommendations are provided according to obtained amounts in order to prevent inefficiency before occurrence.