درخت حوزه‌های تخصصی

This study aims at getting a better performance for optimal stock portfolios by modeling stocks prices dynamics through a continuous paths Levy process. To this end, the share prices are simulated using a multi-dimensional geometric Brownian motion model. Then, we use the results to form the optimal portfolio by maximizing the Sharpe ratio and comparing the findings with the outputs of the conventional model. To examine the robustness of the results, we have evaluated its performance for different investment horizons and various volumes of price information over a long period (approximately twenty years) in the Tehran Stock Exchange (TSE). Findings indicate that within the trading dates spanning the interval 24-Mar-2001 to 19-Sep-2020, the return of the portfolios obtained from applying this simulation scheme for maximization of Sharpe ratio is (244% on average) higher and their risk (standard deviation) are lower (1227% on average) than those realized by the conventional methods. Additionally, a comparison of the simulation approach with a performance of the actual market portfolios indicates that the Sharpe ratios of the simulation method are higher (0.055% on average) than those resulting from the total market performances. The results of the stochastic dominance test show that our proposed strategy has a first-order stochastic dominance (FSD) over the conventional one and market portfolios, that means at each level of cumulative distribution, the Sharpe ratio of our method is higher, and as FSD test makes no assumptions about the curvature of investors' utility functions, these results do not depend on the degree of risk aversion of investors, and as long as investors prefer a higher Sharpe ratio, they would be better off if they follow our proposed strategy.

As a mechanism to enhance financial system stability and a process that allows banks to change their role from traditional lenders to originators and distributors of loans, securitization reduces the dependence on customer deposits. Also, it expands lending capacity, manages banks credit risk, and transforms illiquid assets into saleable securities. In this research, GMM method in three formats is used for the 16 selected Iranian banks. Results show that real sector growth positively and significantly increase financial stability in the Iranian economy. This is because of the economic scale augmentation and its impact on creating new financial resources. Meanwhile, the non-performing loans ratio significantly diminishes banking stability as well as it lowers banks' capacity to generate revenues from intermediary activities. Moreover, return is affected by the inflationary conditions which heightens revenue making and equity factors in banks' balance sheets. In order to generate higher revenues and gain upper profits, banking resources are occasionally withdrawn to enter other financial markets. Loans to deposits ratio, representing the credit risk in banking systems, denotes that higher risk in credit areas exacerbates financial stability due to the higher probability of risk appetite in generating loans to the general public. Also, security size highlights that although it is expected that securitization augments the financial stability in the banking system, other indicators would also be influential on financial stability. In other words, the higher the security size, the bigger its impact on banking stability. Furthermore, Lending capacity augments as a result of risk management and transforming illiquid assets into saleable securities.

Despite the importance of the business model in implementing strategy, this issue is less addressed than strategy. This important issue regarding banks' business model has received less attention, and research in Iran and other countries has caused a deprivation of knowledge. In addition to the scientific pathology of the business model in Iranian commercial banks, the present article seeks to determine the weights of importance and prioritization of the main categories. It also prioritizes and determines the importance of each of the concepts of the main categories of the commercial banking business model. The research method is applied in terms of results and descriptive in terms of purpose and quantitative-qualitative from the data dimension. The research method's strategy in the qualitative part is the grounded theory, and in the quantitative part is the process of hierarchical analysis. The data analysis method in the qualitative part is the coding method, and the quantitative part is based on pairwise comparisons and incompatibility rate analysis. The research community is the experts of commercial banks, and its examples are Mellat, Tejarat, Melli, Sepah, Saderat, Shahr, Eghtesad-e-Novin, and Ayandeh banks. The sampling method is a purposeful judgment with the snowball method and data collection tools in the qualitative part of the interview and review of documents in the quantitative part of the questionnaire. The research findings led to identifying the pathology of Iranian commercial banks' business model in 7 categories and 36 concepts, which are prioritized and contributed based on the importance and role of each of them in the business model.

This paper studies the effect of the space (distance) between lotteries' outcomes on risk-taking behavior and the shape of estimated utility and probability weighting functions. Previously investigated experimental data shows a significant space effect in the gain domain. As compared to low spaced lotteries, high spaced lotteries are associated with higher risk aversion for high probabilities of gain and higher risk-seeking for low probabilities of gain. Hence, the investigation is carried under cumulative prospect theory that respects framing effect and characterizes risk attitudes with respect to probabilities and outcomes. The observed certainty equivalents of lotteries are assumed to be driven by cumulative prospect theory. To estimate the parameters of cumulative prospect theory with maximum likelihood, the usual error term is added. The cumulative prospect theory is incapable of explaining the space effect as its parameters cannot explain the average behavior. Taking account of heterogeneity, a two-component mixture model shows that behavioral parameters of around 25% of the sample can explain the observed differences in relative risk aversions. The results confirm the previous findings of aggregation bias associated with representative-agent models. Furthermore, the results have implications for experimental designs as high space between lotteries' outcomes is required to guarantee the curvature of utility functions.