Search results

This chapter presents the survey of selected linear and mixed integer programming multi-objective portfolio optimization. The definitions of selected percentile risk measures are presented. Some contrasts and similarities of the different types of portfolio formulations are drawn out. The survey of multi-criteria methods devoted to portfolio optimization such as weighting approach, lexicographic approach, and reference point method is also presented. This survey presents the nature of the multi-objective portfolio problems focuses on a compromise between the construction of objectives, constraints, and decision variables in a portfolio and the problem complexity of the implemented mathematical models. There is always a trade-off between computational time and the size of an input data, as well as the type of mathematical programming formulation with linear and/or mixed integer variables.

The purpose of this work is to present the modelling of the conditions of provision of growth of public–private partnership using digital technologies based on the game approach.

The methods of a systemic approach, linear programming and linear correlation, as well as statistical method, are used.

We performed the modelling of the conditions of provision of growth of public–private partnership using digital technologies based on the game approach. We also determined the level of development of public–private partnerships at the level of developed (United States, United Kingdom, Sweden and Denmark) and developing countries (Bulgaria, Venezuela and South Africa) and analysed the level of implementing digital technologies within the studied countries. We note a high level of public–private partnership in developed countries and a low level of this indicator in developing countries (except for Bulgaria, which has the positive dynamics of improvement). We also determine the main competitive advantages of implementing digital technologies, which have an impact on the improvement of the state of public–private partnerships at the level of the studied countries. Using the linear method of programming (game approach), we formulate the models of development of this type of interaction, within which the positive experience of developed countries is taken into account.

The originality and value of this research consist in the study of the specifics of the use of the game approach in the modelling of public–private partnership, which is based on the use of digital technologies.

This chapter proposes a fuzzy approach to forecasting using a financial data set. The methodology used is multiple objective linear programming (MOLP). Selecting an individual forecast based on a single objective may not make the best use of available information for a variety of reasons. Combined forecasts may provide a better fit with respect to a single objective than any individual forecast. We incorporate soft constraints and preemptive additive weights into a mathematical programming approach to improve our forecasting accuracy. We compare the results of our approach with the preemptive MOLP approach. A financial example is used to illustrate the efficacy of the proposed forecasting methodology.

The process approach to multi-level organizational behavior is based on the assumption that multi-level organizational behavior is processual in nature. This article defines group and organizational processes and their representation as process frameworks. Both functional and inclusional classes of levels exist, each of which has at least five categories of levels. All ten categories are special cases of process frameworks. This article provides examples of each category level, which it uses to illustrate new models of organizational work, extended models of interdependence, a new typology of theories based on their levels of processes, and a new tool for survey research called knobby analyses. After explaining the basic idea of knobby analysis, the article briefly describes the processual theory of the organizational hologram, the use of linear programming, and causal-chain analysis to provide multi-level explanations of employee opinion data. These ideas are embodied in conducting a strategic organizational diagnosis, which is the first stage of organizational design. Organizational design encompasses multiple stages, each of which itself involves multiple, multi-level phenomena and analyses. The basic point is that the processual nature of multi-level organizational phenomena gives more hope for improvements in theory building and their application if one uses the process approach rather than a variable approach.

Purpose: The present study aims to test the Quadratic Programming model for Optimal Portfolio selection empirically.

Need for the Study: All the investors who buy financial products are motivated to obtain higher profits or, in other words, to maximise their returns. However, the high returns are often accompanied by higher risks, and avoiding such risks has become the primary concern for all investors. There is a great need for such a model to maximise profits and minimise risk, which can help design an investment portfolio with minimum risk and maximum return. The Quadratic Programming model is one such model which can be applied for selected shares to build an optimised portfolio.

Methodology: This study optimises the stock samples using a two-level screening of correlation coefficient and coefficient of variation. The monthly closing prices of the NSE-listed Indian pharmaceutical stocks from December 2019 to January 2022 have been used as sample data. The Lagrange Multiplier method is used to apply the model to achieve the optimal portfolio solution. Based on the market reality, the transaction costs have also been considered. The Quadratic programming model is further optimised to achieve the optimal portfolio for the select stocks.

Findings: The traditional portfolio theory and the modified quadratic model gives similar and consistent results. In other words, the modified quadratic model asserts the accuracy of the conventional portfolio model. The portfolio constructed in the present study gives a return much higher than the return of the benchmark portfolio of Nifty Fifty, indicating the usefulness of applying the Quadratic Programming model.

Practical Implications: The construction of an optimal portfolio using the traditional or modified Quadratic model can help investors make rational investment decisions for better returns with lower risks.