درخت حوزه‌های تخصصی

One of the main concerns of investors is the evaluation of the return on investment, which is conducted using various models such as the CAPM (single-factor model), Fama-French three/five-factor models, and Roy and Shijin’s six-factor model and other models known as multi-factor models. Despite the widespread use of these models, their major drawbacks include sensitivity to unexpected changes, sudden shocks, high turbulence of price bubble, and so on. To eliminate such negatives, the multi-factor model using the penalty function method is used, in which, instead of averaging, the optimization and avoidance of the effects of abnormal changes and other factors affecting the capital market are considered. In order to evaluate stock returns, it is possible to select effective factors, to simulate and develop a model appropriate to the conditions governing the capital market in Iran. In the present study, by forming portfolios of investments and identifying and refining effective factors, the classification and estimation of the hybrid model of penalty and multi-factor (P & PCA) functions were performed based on the functional data during 2007-2017. The results of this study indicated that the extensive use of the simulation algorithm for the penalty function in the form of P & PCA estimation method improves the efficiency of multi-factor methods in stock return evaluation, and that the use of the hybrid algorithm of penalty and multi-factor functions, compared to the exclusive use of multi-factor models, brings a higher accuracy in estimating stock returns.

Banks are the financial institutions that collect assets from various sources and allocate them to the sectors that require liquidity. Therefore, banks are an inherent element in the system of every country. As private banks enter financial markets, the demand for diverse banking services increases dramatically. Banks seek to use various techniques to improve their performance in attracting customers to increase their market share and profitability. In this regard, assessing the performance of banks is of utmost importance and has become a major activity of bank managers. With the constant changes in the modern world and incessant attempts of competitors to increase their market share by gaining competitive advantage, special attention should be paid to ambidexterity as a key strategy to increase competitive advantage and achieve high performance in dynamic business environments. The present study aimed to identify the ambidextrous factors affecting the performance of banks and present a model to assess the performance of an ambidextrous bank using an agent-based modeling approach. The main objective of the research is to achieve an applied model for managing the performance of the banking industry. The simulation model is processed using the agent-based modeling approach in AnyLogic software environment.