scholarly journals A Multi-Objective Decision-Making Approach For Mutual Fund Portfolio

2011 ◽  
Vol 4 (6) ◽  
Author(s):  
Hari P. Sharma Hari P. Sharma ◽  
Dinesh K. Sharma
Keyword(s):  
Decision Making ◽  
Mutual Fund ◽  
Investment Decision ◽  
Optimal Solution ◽  
Ideal Solution ◽  
Font Size ◽  
Distance Method ◽  
Multi Objective ◽  
Fund Portfolio ◽  
Priority Structure

<p class="MsoNormal" style="text-align: justify; margin: 0in 0.5in 0pt;"><span style="font-family: Times New Roman;"><span style="font-size: 10pt; mso-bidi-font-style: italic;">Investment decision-making problems ar</span><span style="font-size: 10pt;">e<span style="mso-bidi-font-style: italic;"> generally multi-objective in nature such as minimization of the risk and maximization of the expected return.<span style="mso-spacerun: yes;">&nbsp; </span>These problems can be solved efficiently and effectively using multi-objective decision making (MODM) tools such as a lexicographic goal programming (LGP).<span style="mso-spacerun: yes;">&nbsp; </span>This paper applies the LGP model for selecting an optimum mutual fund portfolio for an investor, while taking into account specific parameters including risk, return, expense ratio and others.<span style="mso-spacerun: yes;">&nbsp; </span>Sensitivity analysis on the assigned weights in a priority structure of the goals identifies all possible solutions for decision-making.<span style="mso-spacerun: yes;">&nbsp; </span>The Euclidean distance method is then used, to measure distances of all possible solutions from the identified ideal solution.<span style="mso-spacerun: yes;">&nbsp; </span>The optimal solution is determined by the minimum distance between the ideal solution and other possible solutions of the problem. The associated weights with the optimal solution will be the most appropriate weights in a given priority structure.<span style="mso-spacerun: yes;">&nbsp; </span>The effectiveness and applicability of the LGP model is demonstrated via a case example from broad categories of mutual funds.</span></span></span></p>

scholarly journals A Multi-Objective Decision Making Approach For Mutual Fund Portfolio

2011 ◽  
Vol 3 (10) ◽  
Author(s):  
Hari P. Sharma ◽  
Dinesh K. Sharma
Keyword(s):  
Decision Making ◽  
Mutual Fund ◽  
Investment Decision ◽  
Ideal Solution ◽  
Font Size ◽  
Distance Method ◽  
Multi Objective ◽  
Investment Decision Making ◽  
Fund Portfolio ◽  
Priority Structure

<p class="MsoNormal" style="text-align: justify; margin: 0in 0.5in 0pt;"><span style="font-family: Times New Roman;"><span style="color: black; font-size: 10pt; mso-bidi-font-style: italic;">Investment decision-making problems ar</span><span style="font-size: 10pt;">e<span style="color: black; mso-bidi-font-style: italic;"> generally multi-objective in nature such as minimization of the risk and maximization of the return. <span style="mso-spacerun: yes;">&nbsp;</span>These problems can be solved efficiently and effectively using multi-objective decision making (MODM) tools such as a lexicographic goal programming (LGP). <span style="mso-spacerun: yes;">&nbsp;</span>This paper applies the LGP model for selecting an optimum mutual fund portfolio for an investor, while taking into account specific parameters including risk, return, expense ratio and others. <span style="mso-spacerun: yes;">&nbsp;</span>Using sensitivity analysis on the weights in a priority structure of the goals identifies all possible solutions in the decision-making process. <span style="mso-spacerun: yes;">&nbsp;</span>The Euclidean distance method is then used, to measure distances of all possible solutions from the identified ideal solution. <span style="mso-spacerun: yes;">&nbsp;</span>The optimum possible solution is determined by the minimum distance between the ideal solution and other possible solutions of the problem. <span style="mso-spacerun: yes;">&nbsp;</span>The associated weights will be the most appropriate weights in a given priority structure. <span style="mso-spacerun: yes;">&nbsp;</span>The effectiveness and applicability of the LGP model is demonstrated via a case example from broad categories of mutual funds.</span></span></span></p>

A Study on Investment Decision of Investors With Reference to Penetration of Mutual Funds in India

2016 ◽  
Vol 5 (2) ◽  
Author(s):  
Ratish C Gupta ◽  
Dr. Manish Mittal
Keyword(s):  
Decision Making ◽  
Real Estate ◽  
Mutual Funds ◽  
Capital Markets ◽  
Mutual Fund ◽  
Financial Services ◽  
Investment Decision ◽  
Fund Industry ◽  
Mutual Fund Industry ◽  
The Impact

The Indian mutual fund industry is one of the fastest growing and most competitive segments of the financial sector. The extent of under-penetration in the market is a sore point with the financial services industry, with a large amount of savings being channelized into fixed deposits, gold and real estate rather than the capital markets. The mutual fund industry is yet to spread its reach beyond Tier I cities. The top fifteen cities contribute to 85% of the pie, with the remaining 15% distributed among other cities. The study seeks to determine the impact of decision making of investors on current situation of mutual fund industry.

A two-sided matching decision-making approach based on regret theory under intuitionistic fuzzy environment

2021 ◽  
pp. 1-18
Author(s):  
Xiang Jia ◽  
Xinfan Wang ◽  
Yuanfang Zhu ◽  
Lang Zhou ◽  
Huan Zhou
Keyword(s):  
Decision Making ◽  
Hamming Distance ◽  
Optimal Solution ◽  
Intuitionistic Fuzzy Sets ◽  
Regret Theory ◽  
Multi Objective ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Environment ◽  
Objective Model ◽  
Single Objective

This study proposes a two-sided matching decision-making (TSMDM) approach by combining the regret theory under the intuitionistic fuzzy environment. At first, according to the Hamming distance of intuitionistic fuzzy sets and regret theory, superior and inferior flows are defined to describe the comparative preference of subjects. Hereafter, the satisfaction degrees are obtained by integrating the superior and inferior flows of the subjects. The comprehensive satisfaction degrees are calculated by aggregating the satisfaction degrees, based on which, a multi-objective TSMDM model is built. Furthermore, the multi-objective TSMDM model is converted to a single-objective model, the optimal solution of the latter is derived. Finally, an illustrative example and several analyses are provided to verify the feasibility and the effectiveness of the proposed approach.

On a Decision Making Method Based on Ideal Objective Method

2013 ◽  
Vol 347-350 ◽  
pp. 3186-3189
Author(s):  
Tian Wen Luo ◽  
Hai Feng Tan ◽  
Xiao Juan Wang ◽  
Xin Wei Bai
Keyword(s):  
Decision Making ◽  
Analytic Hierarchy Process ◽  
Solution Method ◽  
Ideal Solution ◽  
Objective Method ◽  
Analytic Hierarchy ◽  
Multi Objective ◽  
Hierarchy Process

By comparing several typical multi-objective decision-making methods, an ideal objective method is proposed, which combines Similarity to Ideal Solution Method and Analytic Hierarchy Process (AHP). After validation, this ideal objective method is both scientific and accurate in the process of target key decision.

scholarly journals TOPSIS Based Algorithm for Solving Multi-objective Multi-level Programming Problem with Fuzzy Parameters

2018 ◽  
Author(s):  
Surapati Pramanik ◽  
Partha P. Dey ◽  
Florentin Smarandache
Keyword(s):  
Programming Problem ◽  
Optimal Solution ◽  
Distance Functions ◽  
Ideal Solution ◽  
Multi Objective ◽  
Cut Method ◽  
Multi Level ◽  
Compromise Optimal Solution ◽  
Negative Ideal Solution ◽  
Fuzzy Parameters

The paper proposes TOPSIS method for solving multi-objective multi-level programming problem (MO-MLPP) with fuzzy parameters via fuzzy goal programming (FGP). At first, - cut method is used to transform the fuzzily described MO-MLPP into deterministic MO-MLPP. Then, for specific , we construct the membership functions of distance functions from positive ideal solution (PIS) and negative ideal solution (NIS) of all level decision makers (DMs). Thereafter, FGP based multi-objective decision model is established for each level DM for obtaining individual optimal solution. A possible relaxation on decisions for all DMs is taken into account for satisfactory solution. Subsequently, two FGP models are developed and compromise optimal solutions are found by minimizing the sum of negative deviational variables. To recognize the better compromise optimal solution, the concept of distance functions is utilized. Finally, a novel algorithm for MO-MLPP involving fuzzy parameters is provided and an illustrative example is solved to verify the proposed procedure.

scholarly journals A Survey of Multi-Objective Sequential Decision-Making

2013 ◽  
Vol 48 ◽  
pp. 67-113 ◽  
Author(s):  
D. M. Roijers ◽  
P. Vamplew ◽  
S. Whiteson ◽  
R. Dazeley
Keyword(s):  
Decision Making ◽  
Pareto Front ◽  
Optimal Solution ◽  
Multiple Objectives ◽  
Sequential Decision Making ◽  
Sequential Decision ◽  
Multi Objective ◽  
Scalarization Function ◽  
Future Work ◽  
Single Objective

Sequential decision-making problems with multiple objectives arise naturally in practice and pose unique challenges for research in decision-theoretic planning and learning, which has largely focused on single-objective settings. This article surveys algorithms designed for sequential decision-making problems with multiple objectives. Though there is a growing body of literature on this subject, little of it makes explicit under what circumstances special methods are needed to solve multi-objective problems. Therefore, we identify three distinct scenarios in which converting such a problem to a single-objective one is impossible, infeasible, or undesirable. Furthermore, we propose a taxonomy that classifies multi-objective methods according to the applicable scenario, the nature of the scalarization function (which projects multi-objective values to scalar ones), and the type of policies considered. We show how these factors determine the nature of an optimal solution, which can be a single policy, a convex hull, or a Pareto front. Using this taxonomy, we survey the literature on multi-objective methods for planning and learning. Finally, we discuss key applications of such methods and outline opportunities for future work.

scholarly journals An Improved SPEA2 Algorithm with Local Search for Multi-Objective Investment Decision-Making

2019 ◽  
Vol 9 (8) ◽  
pp. 1675 ◽  
Author(s):  
Xi Liu ◽  
Dan Zhang
Keyword(s):  
Decision Making ◽  
Local Search ◽  
Investment Decision ◽  
Global Search ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Improved Method ◽  
Optimal Boundary ◽  
Multi Objective ◽  
Investment Decision Making

Enterprise investment decision-making should not only consider investment profits, but also investment risks, which is a complex nonlinear multi-objective optimization problem. However, traditional investment decisions often only consider profit as a goal, resulting in an incorrect decision. Facing the high complexity of investment decision-making space, traditional multi-objective optimization methods pay too much attention to global search ability because of pursuing convergence speed and avoiding falling into local optimum, while local search ability is insufficient, which makes it difficult to converge to the Pareto optimal boundary. To solve this problem, an improved SPEA2 algorithm is proposed to optimize the multi-objective decision-making of investment. In the improved method, an external archive set is set up separately for local search after genetic operation, which guarantees the global search ability and also has strong local search ability. At the same time, the new crossover operator and individual update strategy are used to further improve the convergence ability of the algorithm while maintaining a strong diversity of the population. The experimental results show that the improved method can converge to the Pareto optimal boundary and improve the convergence speed, which can effectively realize the multi-objective decision-making of investment.

PERANCANGAN SPK KELAYAKAN PENERIMA KREDIT PADA DISKOPUKM PROVSU MENGGUNAKAN METODE TOPSIS

2019 ◽  
Vol 3 (1) ◽  
pp. 36-45
Author(s):  
Mhd Arief Ansyari ◽  
Mhd. Zulfansyuri Siambaton
Keyword(s):  
Decision Making ◽  
Optimal Solution ◽  
Point Of View ◽  
Ideal Solution ◽  
Shortest Distance ◽  
Geometric Point ◽  
Savings And Loan ◽  
Relative Proximity ◽  
Negative Ideal Solution ◽  
North Sumatra

The Happy Savings and Loan Cooperative is one of the non-bank financial institutions in the form of cooperatives that serve the needs of its members in lending services with collateral in the form of member deposits, land or building certificates, books for motorized or automobile owners (BPKB) with the aim of providing services and facilities for candidates creditors in the economy. needs Lending funds to prospective creditors must also with the approval of the head of the cooperative / cooperative council. The agreement generally requires considerations such as analyzing the ability to pay prospective creditors. Because each prospective creditor has different economic conditions, it must be observant in making decisions. In determining whether a loan recipient is appropriate or not, there must be an assessment of the criteria established as a reference for decision making. TOPSIS is one of the multi-criteria decision-making methods first introduced by Yoon and Hwang (1981). TOPSIS uses the principle that the chosen alternative must have the shortest distance from the positive ideal solution and the longest distance (the farthest) from the negative ideal solution from a geometric point of view using the Euclidean distance (distance between two points) to determine the relative proximity of an alternative to the optimal solution. Keywords: TOPSIS, Decision Support System, North Sumatra Cooperative

Genetic Algorithm for Multi-Objective Exergetic and Economic Optimization of Parabolic Trough Collectors Integration Into Combined Cycle System (ISCCS)

2010 ◽  
Author(s):  
Ali Baghernejad ◽  
Mahmood Yaghoubi
Keyword(s):  
Decision Making ◽  
Pareto Frontier ◽  
Optimal Solution ◽  
Combined Cycle ◽  
Exergy Destruction ◽  
Exergetic Efficiency ◽  
Economic Optimization ◽  
Cost Rate ◽  
Multi Objective ◽  
Cycle System

Thermoeconomic analyses of any thermal system design are always based on the economic objectives. However, knowledge of economic optimization may not be sufficient for decision making process, since solutions with higher thermodynamic efficiency, in spite of small increases in total costs, may result in much more interesting designs due to changes in the energy market prices or in the energy policies. In this paper a multi-objective optimization scheme is developed and applied for an Integrated Solar Combined Cycle System (ISCCS) that produces 400 MW of electricity to find solutions that simultaneously satisfy exergetic as well as economic objectives. This corresponds to search for a set of Pareto optimal solutions with respect to the two competing objectives. The optimization process is carried out by a particular class of search algorithms known as multi-objective evolutionary algorithms (MOEAs). For such MOEAs, an example of decision-making is presented and a final optimal solution has been introduced. The final optimal solution that is selected in this analysis belongs to the region of Pareto Frontier with significant sensitivity to the costing parameters. However, the region with lower sensitivity to the costing parameter is not reasonable for the final optimum solution due to a weak equilibrium of Pareto Frontier in which a small changes in exergetic efficiency of plant due to variation of operating parameters may lead to the danger of increasing the cost rate of product, drastically. The analysis shows that optimization process leads to 3.2% increasing in the exergetic efficiency and 3.82% decreasing of the rate of product cost. Also optimization leads to the 2.73% reduction on the fuel exergy, 5.71% reductions in the total exergy destruction and also 3.46% and 7.32% reductions in the fuel cost rate and cost rate relating to the exergy destruction, respectively.

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