Search results

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.

Farming in Bangladesh is confronted with various types of uncertainties, which contribute to farmers’ income volatility over the years. As a result, cereal, mainly rice, which is a less riskier crop remained dominantly planted in the current farm plan. But the return generated from rice cultivation has not been able to improve the livelihood of the poor, as rice profitability is low compared to some profitable but risky crops like jute and vegetables. To investigate the behavioral pattern of the farmers towards risk, Dhaka division, largely known as central region of Bangladesh, is selected. The prevailing farm plan of Dhaka division is compared with the efficient one at the current level of expected return in order to check whether the current farm plan is risky or otherwise. Quadratic and MOTAD as well as linear programming techniques have been employed for the analysis. The result of the study reveals that the prevailing farm plan in Dhaka division is risky compared to the efficient plan. Since the current return level is low, the study has recommended that more jute and vegetables should be planted to achieve higher remuneration.

Addresses the problem of maintenance planning and scheduling and reviews pertinent literature. Discusses the characteristics and the complexity of the problem. Advocates mathematical programming approaches for addressing the maintenance scheduling problem. Gives examples to demonstrate the utility of these approaches. Proposes expansion of the state‐of‐the‐art maintenance management information system to utilize the mathematical programming approaches and to have better control over the maintenance scheduling problem.

Although the mean‐variance portfolio selection model has been investigated in the literature, the difficulty associated with the application of the model when dealing with large‐scale problems is limited. The aim of this paper is to close the gap by using the quadratic risk (standard deviation risk) function and finite iteration technique to remove difficulties in quadratic programming.

Using van de Panne' approach, this paper proposes a finite technique to optimize large‐scale portfolio selection problem.

The proposal of parallel algorithm structure to the model provides a clearer decision framework to significantly enhance the efficiency of the portfolio selection process.

The proposal of parallel algorithm structure to the mean‐variance portfolio selection model provides a clearer decision framework to significantly enhance the efficiency of the portfolio selection process. An empirical example that illustrates the application and benefits of the method is provided.

The purpose of this paper is to argue that simplifications of portfolio selection may no longer be necessary, based on computational advancements of portfolio theory and powerful computers.

First, the paper reviews the two branches of portfolio optimization and second, presents the results of large‐scale portfolio selection, based on exhaustive sampling in China. Some speedy results support removing the simplification.

The paper finds that for some simplification techniques, the results of simplified models and original models are quite alike, while for other techniques, the results are strikingly distinctive. Moreover, the performance of portfolio optimizers varies from being instantly fast to being unbearably slow, so it pays to be picky.

This paper reports for large‐scale portfolio selection the results of kinds of software and this alone makes the paper unique. Based on the leading software and exhaustive sampling in China, for the first time the difference between the original and simplified models is studied.

Maintenance control plays a key role in achieving the stated objective of effectiveness and efficiency of the maintenance system. In a recent paper, Gits proposed a reference framework that guides in the design and structuring of maintenance control. The framework is conceptual in nature and its use in practice is limited. Poses Gits’ framework as a set of mathematical programming models. Extends some of Gits’ procedure for maintenance control, then outlines the required expansion in the maintenance management information system (MMIS) in order to provide the needed data to execute the models. The models provide operational plans and schedules ready for implementation.

To use a new method based on α‐dense curved for solving problems of operational research.

The method of global optimization (called Alienor) is used for solving problems involving integer or mixed variables. A reducing transformation using α‐dense curves allows to transforms a n‐variables problem into a problem of a single variable.

Extends the basic method valid for continuous variables to problems involving integer, Boolean or mixed variables. All problems of operational research, linear or nonlinear, may be easily solved by or technique based on α‐dense curves (filling a n‐dimensional space). Industrial problems can be quickly solved by this technique obtaining the best solutions.

This method is deduced from the original works of Y. Cherruault and colleagues about global optimization and α‐dense curves. It proposes new techniques for solving operational research problems.

This paper aims to focus on applications of stochastic linear programming (SLP) to managerial accounting issues by providing a theoretical foundation and practical examples. SLP models may have more implications – and broader ones – in industry practice than deterministic linear programming (DLP) models do.

This paper introduces both DLP and SLP methods. In addition, continuous and discrete SLP models are explained. Applications are demonstrated using practical examples and simulations.

This research work extends the current knowledge of SLP, especially concerning managerial accounting issues. Through numerical examples, SLP demonstrates its great ability of hedging against all scenarios.

This study serves as an addition to building a cumulative tradition of research on SLP in managerial accounting. Only a few SLP studies in managerial accounting have focused on the development of such an instrument. Thus, the measurement scales in this research can be used as the starting point for further refining the instrument of optimization in managerial accounting.

The purpose of this paper is to propose a new method for computing validities in the multimodel approach.

The multimodel approach offers an interesting alternative and a powerful tool to bypass the difficulties to model, control and diagnose a nonlinear and complex system. Its idea is defined as the apprehension of a nonlinear behaviour of a system by a set of local models characterizing the system operation in different operating zones. In spite of the success of its application in different fields, many problems related to the synthesis of multimodel approach remain open. These include, in particular, the method of obtaining the contribution degrees, also called validities, of the base-models for the deduction of the multimodel output.

The presented method may lead to superior results in comparison with the residue approach commonly used in the calculation of validities. Numerical simulation results and an experimental validation on a semi-batch reactor clearly illustrated the effectiveness of the proposed method and proved its impact on the improvement of the performances of the multimodel approach. Moreover, the multimodel approach using the new validities’ computation method can lead to perfect modelling of the process.

The proposed method discussed in the paper has the potential to make the multimodel approach more efficient in the modelling of complex real systems.

A significant contribution of the paper is the formulation of a new constrained optimization problem that can be solved by using a powerful mathematical tool such as the active set method, allowing to estimate the validity indexes in the multimodel approach. The obtained optimal solution can lead to perfect modelling of nonlinear and complex real systems.