درخت حوزه‌های تخصصی

The issue of asset pricing in the market is one of the most important and old issues in the financial world. Factor pricing models seek to be able to determine a significant relationship between return on assets based on the risk parameters of that asset. A wide range of factors can be found in the literature that can be an element for measuring the risk of an asset, but the big question is which of these models will work better. The factors studied in this research include factors that cover market risk, valuation risk, psychological (technical) market risk, profit quality risk, profitability, investment, etc. In this study, we have tried to Using machine learning techniques and optimization tools is a way to derive adaptive-robust nonlinear models that can reduce the risk of model error as much as possible. In this research, two models have been developed. In the first model, using the feature extraction technique and optimization of models based on neural network, a non-linear and adaptable model has been developed for each asset. In the second approach, a portfolio of improved neural network-based models is used in the first stage, which can be used to minimize the risk of model error and achieve a model that is resistant to different market conditions. Finally, it can be seen that the development of these models can significantly improve the risk of error and average error of the model compared to traditional CAPM approaches and the Fama and French three-factor model.

Planning for economic sections and industrial activities at a regional level requires a proper understanding of a region’s potentials. The present study applies product space theory, network science, and information on Iranian provincial production structure to outline a roadmap for industrial development of Kermanshah. Analysis of mean value added and population of Iranian provinces in 2014 to 2018 indicated that of the 32 activities examined, Kermanshah has a revealed comparative advantage in five areas (agriculture and horticulture, traditional husbandry, apiculture, sericulture, and hunting) and other agricultural areas, forestry, and chemicals, with latent comparative advantage in 11 areas, and no advantage in 16 areas. Next, greedy, majority, high degree, and low degree strategies were combined with Borda’s and Copeland’s approaches to identify 11 activities for activation prioritized as follows: poultry, fishing, food, base metal, machinery & equipment, wood industry, other non-metal minerals, pharmaceuticals, metals, appliances, and rubber and plastic products.