mohammadreza sharifi-ghazvini

The return distribution of a portfolio is not constant in different periods, which is affected by the dynamics of financial markets and provides the basis for the instability of the portfolio. Distributionally Robust Portfolio Optimization (DRO) takes into account the uncertainty of the portfolio due to changes in the distribution of portfolio returns. In the current research, the objective function of the portfolio model is to maximize the Calmar ratio, which is one of the reward-risk ratios, and its calculation depends on the distribution of the portfolio returns. The research strategy to robust the return distribution parameter is to consider all the returns that are located in a neighborhood of the empirical distribution of the portfolio, which was used to determine such distributions using the Wasserstein metric. A sample portfolio of the research consists of 8 indices or industries from the Tehran Stock Exchange in the period from 1390 to 1400 and on a weekly time horizon. The test data has been divided into 5 periods, and to evaluate the DRO portfolio compared to the portfolio without this feature, the result of dividing the average of Calmar's ratios into the 5 mentioned periods by their standard deviation has been used. The results show that the DRO portfolio improves this ratio by 0.27, and in addition, the minimum ratio of Calmar in 5 periods in the DRO portfolio is higher than the basket without this property.

The research strategy to stabilize the return distribution parameter is to consider all the returns that are located in a neighborhood of the empirical distribution of the portfolio, which has been used to determine such distributions using Wasserstein's metric criteria and K-L divergence. A sample portfolio of the research consists of 8 indices or industries from the Tehran Stock Exchange with the largest trading volume in the time period from the beginning of 2011 to the end of 2018 and in the weekly time horizon. The test data has been divided into 5 periods, and to evaluate the results of the stable distribution basket compared to the basket without this property, the result of dividing the average ratios of Kalmar and Rachev in the 5 periods by their standard deviation has been used. The results of the optimization with the help of particle aggregation algorithm show that the distribution stable basket of squid improves the mentioned ratio by 1.27 and in addition, the minimum ratio of squid in 5 periods in the distribution stable basket is higher than the basket without this feature. The stable distribution portfolio of Rachof improves this ratio by 1.40, and in addition, the minimum ratio of Rachof in 5 periods in the stable distribution portfolio is higher than the portfolio without this feature.

Motivation is the driving force in individuals that drives them to act. Entrepreneurial motivations are the steps needed to become entrepreneurial individuals. Researchers have conducted various studies of entrepreneurial motivation and have encountered several factors that motivate entrepreneurship. Many researchers disagree with all the specific characteristics that entrepreneurs must possess. The timeframe of this research is from November 2018 to August 2019 in relation to entrepreneurs in Semirom city. This research is aimed at investigating the effect of entrepreneurial motivation on entrepreneurship intention with the moderation of entrepreneurial education which is a case study of Semirom entrepreneurs. The statistical population of this study includes all entrepreneurs in the city of Semirom with 950 people according to the statistics of Entrepreneurship Organization. Also in terms of method, it is a field-correlation type and in terms of data collection, it is based on library and field method (questionnaire). Since the present study is descriptive, like many similar descriptive surveys, closed questionnaires have been used to collect the data needed to examine the topic and answer the questions that arise in the research. The data were analyzed using homogeneity test with SPSS 20 software and ranking of variables and their factors and hypothesis testing with SEM were performed using PLS software. Finally, it is concluded that entrepreneurial motivations such as independence, success, and financial motivations affect entrepreneurship intention. In addition, Entrepreneurial motivation is also reinforced by the moderation of entrepreneurial education.

A project portfolio is a crucial decision-making process used to prepare an optimum collection of vast alternative projects. In most of the previous modeling methods, the focus is directed towards maximizing project efficiency and so, the role of risky aspects in selecting appropriate projects has been neglected. This paper presents an integrated multi-objective mathematical programming (MOMP) based on efficiency-risk for selecting a project portfolio using various techniques including data envelopment analysis (DEA), risk priority number (RPN) and non-dominated sorting genetic algorithm (NSGA-ΙΙ). The proposed model can support both the capability to nominate mutually exclusive projects (conflicting projects that only one of them can be done) or any type of predecessor projects (doing a project depends on another project) and concurrent projects (need to be done at the same time) selection. Another advantage of the model is that the hyper heuristic solutions can be found in the form of several non-dominated cases and it is possible for organization’s experts to choose the best and the most suitable solutions.  In the competitive world, intelligent optimum decision making is a vital task in the success of large systems. Due to lack of resources, it is not always possible to fully evaluate all proposed developmental projects. In this context, it is important to make an optimal portfolio of desired projects. The present article aimed to identify an applied methodology for the selection of project portfolios with regard to efficiency and risk assessment and the alignment of the projects’ purposes with the organization’s goals, while also considering the existing resource limitations and regarding risk as a main indicator. The efficiency of the projects is examined with regard to the resources used. The present paper seeks to respond to the following main questions:ü  How can an optimal project portfolio be selected for an organization with maximum efficiency and minimum risk?
ü  Does project risk assessment in view of the two criteria of impact and probability sufficiently address the economic conditions and the complexity of the projects in the majority of research and development projects?
ü  What is the drawback of current risk assessment methods for project portfolios?
Responding to these questions can help organization managers select optimal project portfolios and achieve their strategic organizational objectives. Many studies have been conducted on the selection of project portfolios and most of them follow a qualitative and quantitative approach and use combination classification. One of the applied studies conducted on project selection is by Eilat et al. , (2008), in which projects were selected within the combinational framework of DEA and a balance score card was used to select proper research and development projects (Tahri, 2015) presented two numerical methods for mathematical optimization problems for both single and multiple objectives using two values (0 and 1) as the decision variable. (Huang et al. , 2016) discussed the joint problem of optimal project selection and scheduling in situations when the projects’ initial outlays and net cash inflows are determined by experts’ estimates due to the lack of historical data. A literature review reveals that there are a lot of optimization models available to prepare project portfolios. Most of them have one objective function to maximize project benefits or to minimize operating costs subject to operational constraints. In most modeling methods, the role of risk aspects in selecting appropriate projects has been neglected. A few of them have explored the aspect of model constraints. In other words, in the past, the main focus is directed towards economic projects while the sustainable factors (e. g. , environmental and social risks) have been neglected. For this purpose, this study presents an integrated MOMP based on efficiency-risk for selecting a project portfolio using various techniques including DEA, RPN and non-dominated sorting genetic algorithm (NSGA-ΙΙ). Step1) Preparing a list of candidate & feasible projects
Step2) Calculating efficiency of each project by using the DEA Step3) Calculating the risk priority number for each project
Step4) Developing a MOMP model to select the best projects
Step5) Solving the model using a Non-dominated Sorting Genetic Algorithm ΙΙ (NSGAΙΙ) One of the main advantages of NSGAII is that the at-hand solutions can be found in the form of several non-dominated cases and therefore, it is possible for organization’s experts to choose the best and the most suitable solutions. Stated differently, the expert’s knowledge and viewpoints can be considered due to flexibility and availability of different solutions. Besides, it is feasible to compute the adjusted efficiency and to select the solution (s) producing the maximum efficiency as the final optimal answer (s). This article used the concepts of DEA, RPN, MOMP and NSGAII modeling to propose an applied methodology for extracting an optimal portfolio of projects. In other words, we suggest the use of a Non-dominated Sorting Genetic Algorithm ΙΙ as a solution method for the presented multi-objective model in order to deduce the optimal projects portfolio. By comparing the results for the proposed algorithm and existing methods, it was concluded that the previous methods can give portfolio of projects with suitable profit but these solutions face high risk and low reliability. Thus, such solutions are not acceptable by managers in organizations. The lack of attention to risk aspects leads to the non-realization of the estimated profit due to high probability of risk. Therefore, the valuable resources will be wasted. Whereas, by adjusting profit with the risk numbers, it is indicated that the profit gained by our method is so better and higher than previous methods after risk. This can indicate novelty, applicability, and performance of our method.
References
Eilat, H. , Golany, B. & Shtub, A. (2008). R&D project evaluation: An integrated DEA and balanced scorecard approach. Omega, 36, 895-912.
Huang, X. , Zhao, T. & Kudratova, S. (2016). Uncertain mean-variance and mean-semivariance models for optimal project selection and scheduling. Knowledge-Based Systems, 93, 1-11.
Tahri, H. (2015). Mathematical Optimization Application in Project Portfolio Management. Procedia-Social and Behavioral Sciences, 210, 339-347.