Mechanical Selection System of Stock Portfolio Based on Grey Relational Analysis Attribute Reduction Mechanism

An expert system for automatically selecting stock portfolio is presented. The expert system involved Grey Relational Analysis (GRA) model, the VPRS-index clustering / classification method, and Variable Precision Rough Set (VPRS) theory. The GRA model is applied to consolidate the 53 financial indices into six financial ratios (Grey Relational Grades (GRGs)) for each stock item. The VPRS-index method is used to determine the optimal number of clusters per GRG. VPRS theory is then applied to identify the stocks within the -lower approximate sets. Finally, the GRGs of each candidate stock item are consolidated to a single GRG indicating the ability of the stock item to maximize the rate of return. The validity and effectiveness of the VPRS-index clustering / classification method is first evaluated prior to that of the expert system. After that, results of this study showed that this expert system yields a higher rate of return than those of several existing portfolio selection systems. Key words: Fuzzy C-Means, VPRS, VPRS-index method, classification, stock portfolio.

PurposeThe purpose of this paper is to construct some negative grey relational analysis models to measure the relationship between reverse sequences.Design/methodology/approachThe definition of reverse sequence has been given at first based on analysis of relative position and change trend of sequences. Then, several different negative grey relational analysis models, such as the negative grey similarity relational analysis model, the negative grey absolute relational analysis model, the negative grey relative relational analysis model, the negative grey comprehensive relational analysis model and the negative Deng’s grey relational analysis model have been put forward based on the corresponding common grey relational analysis models. The properties of the new models have been studied.FindingsThe negative grey relational analysis models proposed in this paper can solve the problem of relationship measurement of reverse sequences effectively. All the new negative grey relational degree satisfying the requirements of normalization and reversibility.Practical implicationsThe proposed negative grey relational analysis models can be used to measure the relationship between reverse sequences. As a living example, the reverse incentive effect of winning Fields Medal on the research output of winners is measured based on the research output data of the medalists and the contenders using the proposed negative grey relational analysis model.Originality/valueThe definition of reverse sequence and the negative grey similarity relational analysis model, the negative grey absolute relational analysis model, the negative grey relative relational analysis model, the negative grey comprehensive relational analysis model and the negative Deng’s grey relational analysis model are first proposed in this paper.

This paper presents an automatic stock portfolio selection system. In the proposed approach, 53 financial indices are collected for each stock item and are consolidated into six financial ratios [Grey relational grades (GRGs)] using a Grey relational analysis model. The GRGs are processed using a modified form of the PBMF index method (designated as the Huang index function) to determine the optimal number of clusters per GRG. The resulting cluster indices are then processed using rough set theory to identify the stocks within the lower approximate sets. Finally, the GRGs of each stock item in the lower approximate sets are consolidated into a single GRG, indicating the ability of the stock item to maximize the rate of return. It is demonstrated that the proposed stock selection mechanism yields a higher rate of return than several existing portfolio selection systems.

In this study, the weight clustering model, which consists of Dependency of Attributes of Rough Set (RSDA) with K-means Clustering is combined with Grey Systems theory and Rough Set (RS) theory to create an automatic stock market forecasting and portfolio selection mechanism. In our proposed approach, financial data are collected every quarter and are inputted to an GM (1, 1) predicting model to forecast the future trends of the collected data over the next quarter. Next, the forecasted data of financial statement is transformed into financial ratios using a RSDA measures and clustered by using a K-means clustering algorithm, and then supplied to a RS classified module, which selects appropriate investment stocks by adopting a set of decision-making rules. Finally, a grey relational analysis technique is applied to specify an appropriate weighting of the selected stocks to maximize the portfolio's rate of return. The validity of our proposed approach is demonstrated to use the electronic stock data extracted from the financial database maintained by the Taiwan Economic Journal (TEJ). The portfolio's results derived by using our proposed weight clustering model are compared with those portfolio's results of a conventionally clustering method. It is found that our proposed method yielded a greater average annual rate of return (23.42%) on the selected stocks from 2004 to 2006 in Taiwan stock market.

Along with the development of the economy and society and the speedy increasing number of personal vehicles, especially motorcycles, many cities in developing countries like Hanoi (Vietnam) are facing many transportation problems, notably traffic accidents involving motorcyclists. Motorcycle accidents are caused by main human-error factors, and could be considered as the typical grey system with the features of complexity and imperfect information. In this study, a grey relational analysis (GRA) model of the human-error factors is presented to quickly explore the main human-error factors contributing to traffic accidents caused by motorcycles. In this model, the grey relational degree and the grey relational order of each human-error factor also are determined to rank the main contributing human-error factors. Taking the data of road traffic accidents caused by motorcycles in Hanoi, the capital of Vietnam between 2015 and 2017 as experimental data, the grey relational degrees of human-error factors have been analyzed quantitatively through the GRA model. The experimental results show the first three human-error factors include wrong lane shifting, poor road observation, and speeding, respectively. The results also help to conduct effective countermeasures for controlling human errors and reducing road traffic accidents.

The statistical mathematical models are developed to investigate the influence of cutting parameters on surface roughness, tool wear, cutting force, tangential force and the work piece vibration in boring of AISI 4340 steels. A full factorial design of experiments is used to conduct 27 experiments on AISI 4340 as the work piece material and TiCN–Al2O3–TiN multi-layered coated carbide inserts. Online data acquisition of cutting forces on the cutting tool and the work piece vibrations are measured by using piezo-electric dynamometer and laser Doppler vibrometer, respectively. This paper proposes optimization method using grey relational analysis (GRA) and predictive models like support vector machine and response surface method are used to predict the GRG values and optimize the machining parameters. The GRA is used for converting multi response optimization problem into optimization of single objective of grey relational grade (GRG). Finally confirmation test was performed and also optimized the machining parameters to minimize the surface roughness (Ra), tool wear (VB), cutting force (Fx), tangential force (Fz) and work piece vibration (VA).

In this paper we developed new grey relational analysis by expanding a range of treatable value. The classical GRA procedure deals with almost whitened values, reference vectors (or sequences), inspected vectors and grey relational grades. The classical GRA is called White-In-White-Out (WIWO) GRA in this paper. We developed two kinds of GRA algorithm based on the topological-based GRA, after defined several new operations of the interval grey number. On the one hand, GRA which provides the grey relational grade a whitened value finally is named Grey-In-White-Out (GIWO) GRA. On the other hand, GRA which provides the grey relational grade the interval grey number is named Grey-In-Grey-Out (GIGO) GRA. Both new GRA procedures deal with the reference vector and inspected vectors as the interval grey number. Three examples are given in this paper; the properties of the grey relational grade given by the proposals are discussed.

PurposeThis paper aims to optimize the process parameters of the fused deposition modelling (FDM) process using the Grey-based Taguchi method and the results to be verified based on a technique for order preference by similarity to ideal solution (TOPSIS) and analytical hierarchy process (AHP) calculation.Design/methodology/approachThe optimization of process parameters is gaining a potential role to develop robust products. In this context, this paper presents the parametric optimization of the FDM process using Grey-based Taguchi, TOPSIS and AHP method. The effect of slice height (SH), part fill style (PFS) and build orientation (BO) are investigated with the response parameters machining time, surface roughness and hardness (HD). Multiple objective optimizations were performed with weights of w1 = 60%, w2 = 20% and w3 = 20%. The significance of the process parameters over response parameters is identified through analysis of variance (ANOVA). Comparisons are made in terms of rank order with respect to grey relation grade (GRG), relative closeness and AHP index values. Response table, percentage contributions of process parameters for both GRG and TOPSIS evaluation are done.FindingsThe optimum factor levels are identified using GRG via the Grey Taguchi method and TOPSIS via relative closeness values. The optimized factor levels are SH (0.013 in), PFS (solid) and BO (45°) using GRG and SH (0.013 in), PFS (sparse-low density) and BO (45°) using TOPSIS relative closeness value. SH has higher significance in both Grey relational analysis and TOPSIS which were analysed using ANOVA.Research limitations/implicationsIn this research, the multiple objective optimizations were done on an automotive component using GRG, TOPSIS and AHP which showed a 27% similarity in their ranking order among the experiments. In the future, other advanced optimization techniques will be applied to further improve the similarity in ranking order.Practical implicationsThe study presents the case of an automotive component, which illustrates practical relevance.Originality/valueIn several research studies, optimization was done on the standard test specimens but not on a real-time component. Here, the multiple objective optimizations were applied to a case automotive component using Grey-based Taguchi and verified with TOPSIS. Hence, an effort has been taken to find optimum process parameters on FDM, for achieving smooth, hardened automotive components with enhanced printing time. The component can be explored as a replacement for the existing product.

This paper investigates the effect of different additive manufacturing process parameters such as chamber temperature, Printing temperature, layer thickness, and print speed on five essential parameters that characterize the manufactured components: cylindricity, circularity, strength, and Young’s modulus, and deformation by Gray Relational Analysis method simultaneously. Taguchi method was used to design the experiments, and the PA6 cylindrical parts were fabricated using a German RepRap X500® 3D printer. Then the Gray Relational Grade (GRG) values were calculated for all experiments. In the 8th trial, the highest value of GRG was observed. Then, to discover the optimal parameters, the GRG data were analyzed using ANOVA and S/N analysis, and it was determined that the best conditions for enhancing GRG are 60 °C in the chamber temperature, 270 °C in the printing temperature, 0.1 mm layer thickness, and 600 mm/min print speed. Finally, by using optimal parameters, a verification test was performed, and new components were investigated. Finally, comparing the initial GRG with the GRG of the experiments showed an improvement in the gray relational grade (14%) which is accompanying with improving of GRG value.

For the reason of the complexity of CEO Strategic leadership and some shortcomings of the current evaluation methods, set up a new model for CEO strategic leadership evaluation based on grey relational analysis with harmony factor using. The grey relational analysis with harmony factor gives considerations to both the emphasis of capability and the proportion of capabilities in evaluation of CEO strategic leadership. At last, a case study is provided to verify the model. The research findings offer a workable approach in evaluating strategic leadership.

This paper presents an investigation on the effect and optimization of machining parameters on the kerf (cutting width) and material removal rate (MRR) of titanium alloy (TI-6AL-4V) using wire electrical discharge machining WEDM with a brass wire diameter of 0.5mm. The experimental studies were conducted under varyingpulse-off time, peak current, wire feed and wire tension. The settings of machining parameters were determined by using Taguchi experimental design method. The multiple performance characteristics based on the statistical-based analysis of variance (ANOVA) and grey relational analysis (GRA) was attempted. Analysis of variance was used to study the significance of process parameters on grey relational grade (GRG) which showed the most significant factor. The GRG obtained from the GRA was used to optimize the WEDM process. The optimum process parameters are determined by the GRG as the overall performance index. To validate the findings, confirmation experiment had been carried out at the optimal set of parameters, and the predicted results were found to be in good agreements with experimental findings. Improved machining performance in the WEDM process has been achieved by using this approach.

The use of high dielectric permittivity in vertical double gate MOSFET (VDG-MOSFET) can overcome the problem of power dissipation and leakage current. In this paper, we investigated the performance potential of Titanium dioxide (TiO2) which has been deposited on silicon for use as a high-permittivity gate insulator and tungsten silicide (WSi2) for use as metal gate based on 2-D numerical simulations. The analysis and optimization of n-channel VDG-MOSFET is conducted, using a combination of L9 orthogonal array (OA) of Taguchi method and grey relational analysis (GRA). Four process parameters which are VTH implant energy, halo implant dose, source/drain (S/D) implant dose and S/D implant tilt angle are optimized to obtain the desired value of on-current (ION), off-current (IOFF) and subthreshold slope (SS). The design of experiment (DoE) is based on the L9 OA of Taguchi method and then the experimental data for multiple responses are converted into a single unit called grey relational grade (GRG). The most optimal level of four process parameters towards multiple responses are selected based on the highest GRG. Based on the analysis of variance (ANOVA), the most significant factor is observed to be S/D implant dose with 35.6% factor effect on GRG. The optimized value for ION, IOFF and SS after the optimization approach are 1599.3 µA/µm, 865.5 pA/µm and 68.02 mV/dec respectively with 0.8362 of GRG.

방위산업은 경제논리보다 안보논리가 우선시되며 고비용, 장기간의 연구개발이 이루어져왔다. 하지만, 최근 몇 년간 급변하는 기술의 발전과 안보상황을 통해 방위산업에서도 체계개발간에 있어 비용절감과 개발기간 단축의 필요성이 대두되었다. 이에 민수 분야의 부품 모듈화 전략을 방위산업에서 활용하기 위해 OO 사업 LRU(Line Replaceable Unit)들의 모듈화 우선순위를 도출하고자 한다. 프로젝트 평가기법 중 하나인 GRA(Grey Relational Analysis)를 사용하였으며, 문헌연구를 통해 선정된 6개의 평가지표 데이터를 수집하여 OO 사업의 11개 모듈에 대하여 우선순위를 선정하였다. 그 결과, M11(메인보드)과 M8(EMI모듈), M3(싱글보드컴퓨터)의 Grey 관계등급이 0.83, 0.81, 0.80 순으로 도출되었고, 우선순위에 따라 모듈화 하는 것이 비용 절감 및 기간 단축의 효과가 있을 것으로 판단된다. 본 연구는 유사 무기체계 개발이나 향후 성능개량 사업 등을 고려한 LRU 모듈화 설계 시, 모듈화 적용 판단 및 의사결정의 근거가 되는 기초 연구로 활용할 수 있을 것이다. In the defense industry, national security takes priority over economic sense and this has translated into high cost and long-term research and development. However, the exponential growth of technology and rapid changes in the security situation in recent years have resulted in a call for the development of systems at a low cost within a short period of time. In order to implement a modularization strategy in the field of defense, the introduction of line replaceable units in OO systems needs to be prioritized. This study selects six criteria following a literature review and prioritizes 11 modules for OO systems using the project evaluation method, Grey Relational Analysis (GRA). Based on the GRA results, the grey relational grades were derived as 0.83, 0.81 and 0.80 for the M11 (Main board), M8 (EMI module), M3 (Single board computer) modules, respectively. The cost and time of development is expected to be reduced in accordance with the grey relational grade. The results of this research could be utilized for decision making on adopting modularization in similar system development or product improvement programs (PIPs).

The aim of this study is to evaluate the entrepreneurial environment of selected countries, and analyze and rank them on the basis of entrepreneurship related indicators. This study’s design comprises a review of the literature, extraction of secondary data on the phenomenon, and analysis. The research gap has been established through a review of the literature, leading toward the development of problem statement. The cross-sectional data, related to entrepreneurship indicators, is extracted from website of World Development Indicators (2021) for 48 selected countries. Using positivism as a research philosophy and deduction as a research approach, the data are analyzed through grey relational analysis (GRA). On the basis of grey relational grades, this study also classified the countries on the continuum of “much better” to “worse.” The results of the study show that the United States, United Kingdom, Japan, Australia, Hong Kong SAR, China, France and Canada attained the highest grey relational grades and are considered the countries having much better entrepreneurial environment; whereas Poland, Kuwait, Namibia and so on attained the lowest grey relational grades and are considered the countries having worst entrepreneurial environment. This research has several practical implications for different economies/countries, entrepreneurial ventures, aspiring entrepreneurial, and researchers. On the basis of findings of this study, policy makers should refine country-level entrepreneurial policies while keeping in view the respective grey relational grades.

Through a pin-on-disc type wear setup, the dry sliding wear behavior of SiC-reinforced aluminum composites produced using the molten metal mixing method was investigated in this paper. Dry sliding wear tests were carried on SiC-reinforced metal matrix composites (MMCs) and its matrix alloy sliding against a steel counter face. Different contact stresses, reinforcement percentages, sliding distances, and sliding velocities were selected as the control variables, and the responses were selected as the wear volume loss (WVL) and coefficient of friction (COF) to evaluate the dry sliding performance. An L25 orthogonal array was employed for the experimental design. Initially, the optimization of the dry sliding performance of the SiC-reinforced MMCs was performed using grey relational analysis (GRA). Based on the GRA, the optimum level parameters for overall grey relational grade in terms of WVL and COF were identified. Analysis of variance was performed to determine the effect of individual factors on the overall grey relational grade. The results indicated that the sliding velocity was the most effective factor among the control parameters on dry sliding wear, followed by the reinforcement percentage, sliding distance, and contact stress. Finally, the wear surface morphology and wear mechanism of the composites were investigated through scanning electron microscopy.