Search results for: portfolio selection problem

Authors: Hannes Koester

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Driven by difficult market conditions and increasing regulations, many banks are making the strategic decision to restructure their portfolio by divesting several business segments. Using a unique dataset of 727 portfolio restructuring announcements by 161 international listed banks over the period 1999 to 2015, we investigate the impact of restructuring measurements on the stock performance as well as on the banks’ profitability and risk. Employing the event study methodology, we detect positive stock market reactions on the announcement of restructuring measurements. These positive stock market reactions indicate that shareholders reward banks’ specialization activities. However, the results of the system GMM regressions show a negative relation between restructuring measurements and banks’ return on assets and a positive relation towards the individual and systemic risk of banks. These empirical results indicate that there is no guarantee that portfolio restructurings will result in a more profitable and less risky institution.

Keywords: bank performance, bank risk, divestiture, restructuring, systemic risk

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Authors: Richa Grover, Rahul Grover, V. Balaji Rao, Kavish Kejriwal

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Selection of suppliers is a crucial problem in the supply chain management. On top of that, sustainable supplier selection is the biggest challenge for the organizations. Environment protection and social problems have been of concern to society in recent years, and the traditional supplier selection does not consider about this factor; therefore, this research work focuses on introducing sustainable criteria into the structure of supplier selection criteria. Sustainable Supply Chain Management (SSCM) is the management and administration of material, information, and money flows, as well as coordination among business along the supply chain. All three dimensions - economic, environmental, and social - of sustainable development needs to be taken care of. Purpose of this research is to maximize supply chain profitability, maximize social wellbeing of supply chain and minimize environmental impacts. Problem statement is selection of suppliers in a sustainable supply chain network by ranking the suppliers against sustainable criteria identified. The aim of this research is twofold: To find out what are the sustainable parameters that can be applied to the supply chain, and to determine how these parameters can effectively be used in supplier selection. Multicriteria decision making tools will be used to rank both criteria and suppliers. AHP Analysis will be used to find out ratings for the criteria identified. It is a technique used for efficient decision making. TOPSIS will be used to find out rating for suppliers and then ranking them. TOPSIS is a MCDM problem solving method which is based on the principle that the chosen option should have the maximum distance from the negative ideal solution (NIS) and the minimum distance from the ideal solution.

Keywords: sustainable supply chain management, sustainable criteria, MCDM tools, AHP analysis, TOPSIS method

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Authors: A. Hidalgo, A. Desportes, E. Bonin, A. Kadaoui, T. Bouaricha

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Present paper is concerned with portfolio management with Black-Litterman (B-L) model. Considered stocks are exclusively limited to large companies stocks on US market. Results obtained by application of the model are presented. From analysis of collected Dow Jones stock data, remarkable explicit analytical expression of optimal B-L parameter τ, which scales dispersion of normal distribution of assets mean return, is proposed in terms of standard deviation of covariance matrix. Implementation has been developed in Matlab environment to split optimization in Markovitz sense from specific elements related to B-L representation.

Keywords: Black-Litterman, Markowitz, market data, portfolio manager opinion

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Authors: Tetsuki Taniguchi, Yoshio Karasawa

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This paper describes a node pair selection scheme in relay-aided multiple source multiple destination communication system based on stable marriage problem. A general case is assumed in which all of source, relay and destination nodes are equipped with multiantenna and carry out multistream transmission. Based on several metrics introduced from inter-node channel condition, the preference order is determined about all source-relay and relay-destination relations, and then the node pairs are determined using Gale-Shapley algorithm. The computer simulations show that the effectiveness of node pair selection is larger in multihop communication. Some additional aspects which are different from relay-less case are also investigated.

Keywords: relay, multiple input multiple output (MIMO), multiuser, amplify and forward, stable marriage problem, Gale-Shapley algorithm

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Authors: Gupta Amit, Kumar Ramesh, P. C. Tewari

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Globalization is putting enormous pressure on the business organizations specially manufacturing one to rethink the supply chain in innovative manners. Inventory consumes major portion of total sale revenue. Effective and efficient inventory management plays a vital role for the successful functioning of any organization. Selection of inventory policy is one of the important purchasing activities. This paper focuses on selection and ranking of alternative inventory policies. A deterministic quantitative model-based on Distance Based Approach (DBA) method has been developed for evaluation and ranking of inventory policies. We have employed this concept first time for this type of the selection problem. Four inventory policies Economic Order Quantity (EOQ), Just in Time (JIT), Vendor Managed Inventory (VMI) and monthly policy are considered. Improper selection could affect a company’s competitiveness in terms of the productivity of its facilities and quality of its products. The ranking of inventory policies is a multi-criteria problem. There is a need to first identify the selection criteria and then processes the information with reference to relative importance of attributes for comparison. Criteria values for each inventory policy can be obtained either analytically or by using a simulation technique or they are linguistic subjective judgments defined by fuzzy sets, like, for example, the values of criteria. A methodology is developed and applied to rank the inventory policies.

Keywords: inventory policy, ranking, DBA, selection criteria

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Authors: M. Chumburidze, N. Salia, T. Namchevadze

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This work deal with case processing problems in business. The task of strategic credit requirements management of cases portfolio is discussed. The information model of credit requirements in a binary tree diagram is considered. The algorithms to solve issues of prioritizing clusters of cases in business have been investigated. An implementation of priority queues to support case management operations has been presented. The corresponding pseudo codes for the programming application have been constructed. The tools applied in this development are based on binary tree ordering algorithms, optimization theory, and business management methods.

Keywords: credit network, case portfolio, binary tree, priority queue, stack

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Authors: Yeong-Seop Ahn, Myoung-Jin Kim, Young-Min Ko, Hyoung-Kyu Song

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This paper proposes a relay selection scheme utilizing an orthogonal variable spreading factor (OVSF) code in a cooperative communication. The relay selection scheme influences on the communication performance in the cooperative communication. Conventional relay selection schemes such as the best harmonic mean relay selection scheme or the threshold-based relay selection scheme should know information such as channel state information (CSI) in advance. The proposed relay selection scheme does not require information in advance by using a reference signal utilizing the OVSF code. The simulation result shows that bit error rate (BER) performance of proposed relay selection scheme is similar to the best harmonic mean relay selection scheme that is known as one of the optimal relay selection schemes.

Keywords: cooperative communication, relay selection, OFDM, OVSF code

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Authors: Issa Qabaja, Fadi Thabtah

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Email phishing classification is one of the vital problems in the online security research domain that have attracted several scholars due to its impact on the users payments performed daily online. One aspect to reach a good performance by the detection algorithms in the email phishing problem is to identify the minimal set of features that significantly have an impact on raising the phishing detection rate. This paper investigate three known feature selection methods named Information Gain (IG), Chi-square and Correlation Features Set (CFS) on the email phishing problem to separate high influential features from low influential ones in phishing detection. We measure the degree of influentially by applying four data mining algorithms on a large set of features. We compare the accuracy of these algorithms on the complete features set before feature selection has been applied and after feature selection has been applied. After conducting experiments, the results show 12 common significant features have been chosen among the considered features by the feature selection methods. Further, the average detection accuracy derived by the data mining algorithms on the reduced 12-features set was very slight affected when compared with the one derived from the 47-features set.

Keywords: data mining, email classification, phishing, online security

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Authors: Rouhallah Bagheri, Morteza Mahmoudi, Hadi Moheb-Alizadeh

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This paper aims at developing a multi-objective model for supplier selection and order allocation problem in stochastic environment, where purchasing cost, percentage of delivered items with delay and percentage of rejected items provided by each supplier are supposed to be stochastic parameters following any arbitrary probability distribution. In this regard, dependent chance programming is used which maximizes probability of the event that total purchasing cost, total delivered items with delay and total rejected items are less than or equal to pre-determined values given by decision maker. The abovementioned stochastic multi-objective programming problem is then transformed into a stochastic single objective programming problem using minimum deviation method. In the next step, the further problem is solved applying a genetic algorithm, which performs a simulation process in order to calculate the stochastic objective function as its fitness function. Finally, the impact of stochastic parameters on the given solution is examined via a sensitivity analysis exploiting coefficient of variation. The results show that whatever stochastic parameters have greater coefficients of variation, the value of the objective function in the stochastic single objective programming problem is deteriorated.

Keywords: supplier selection, order allocation, dependent chance programming, genetic algorithm

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Authors: Seifoddini Hamid, Mardikoraeem Mahsa, Ghorayshi Roya

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Manufacturing facility location selection is an important strategic decision for many industrial corporations. In this paper, a new approach to the manufacturing location selection problem is proposed. In this approach, cluster analysis is employed to identify suitable manufacturing locations based on economic, social, environmental, and political factors. These factors are quantified using the existing real world data.

Keywords: manufacturing facility, manufacturing sites, real world data

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Authors: Olga Biedova, Victoria Steblovskaya, Kai Wallbaum

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In the current capital market environment, investors constantly face the challenge of finding a successful and stable investment mechanism. Highly volatile equity markets and extremely low bond returns bring about the demand for sophisticated yet reliable risk management strategies. Investors are looking for risk management solutions to efficiently protect their investments. This study compares a classic Constant Proportion Portfolio Insurance (CPPI) strategy to a Volatility Target portfolio insurance (VTPI). VTPI is an extension of the well-known Option Based Portfolio Insurance (OBPI) to the case where an embedded option is linked not to a pure risky asset such as e.g., S&P 500, but to a Volatility Target (VolTarget) portfolio. VolTarget strategy is a recently emerged rule-based dynamic asset allocation mechanism where the portfolio’s volatility is kept under control. As a result, a typical VTPI strategy allows higher participation rates in the market due to reduced embedded option prices. In addition, controlled volatility levels eliminate the volatility spread in option pricing, one of the frequently cited reasons for OBPI strategy fall behind CPPI. The strategies are compared within the framework of the stochastic dominance theory based on numerical simulations, rather than on the restrictive assumption of the Black-Scholes type dynamics of the underlying asset. An extended comparative quantitative analysis of performances of the above investment strategies in various market scenarios and within a range of input parameter values is presented.

Keywords: CPPI, portfolio insurance, stochastic dominance, volatility target

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Authors: Fulya Zaralı, Harun Resit Yazgan

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Logistics centers represent areas that all national and international logistics and activities related to logistics can be implemented by the various businesses. Logistics centers have a key importance in joining the transport stream and the transport system operations. Therefore, it is important where these centers are positioned to be effective and efficient and to show the expected performance of the centers. In this study, the location selection problem to position the logistics center is discussed. Alternative centers are evaluated according certain criteria. The most appropriate center is identified using the axiomatic design method.

Keywords: axiomatic design, logistic center, facility location, information systems

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Authors: Mohammad Azam

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The purpose of this study is to investigate risk and real excess portfolio returns using inflation adjusted risk-free rates, a measuring technique that focuses on the momentum augmented Fama-French six-factor model and use monthly data from 1994 to 2022. With the exception of profitability, the data show that market, size, value, momentum, and investment factors are all strongly associated to excess portfolio stock returns using ordinary lease square regression technique. According to the Gibbons, Ross, and Shanken test, the momentum augmented Fama-French six-factor model outperforms the market. This technique discovery may be utilised by academics and professionals to acquire an in-depth knowledge of the Pakistan Stock Exchange across a broad stock pattern for investing decisions and portfolio construction.

Keywords: real excess portfolio returns, momentum augmented fama & french five-factor model, GRS-test, pakistan stock exchange

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Authors: Chien-Chih Lin

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This study argues that the optimal diversifications for the maximization of bank value are asymmetrical; they depend on the business cycle. During times of expansion, systematic risks are relatively low, and hence there is only a slight effect from raising them with a diversified portfolio. Consequently, the benefit of reducing individual risks dominates any loss from raising systematic risks, leading to a higher value for a bank by holding a diversified portfolio of assets. On the contrary, in times of recession, systematic risks are relatively high. It is more likely that the loss from raising systematic risks surpasses the benefit of reducing individual risks from portfolio diversification. Consequently, more diversification leads to lower bank values. Finally, some empirical evidence from the banks in Taiwan is provided.

Keywords: diversification, default probability, systemic risk, banking, business cycle

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Authors: Gopinath Ganapathy, Chellammal Surianarayanan

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Services are growing rapidly and generally they are aggregated into a composite service to accomplish complex business processes. There may be several services that offer the same required function of a particular task in a composite service. Hence a choice has to be made for selecting suitable services from alternative functionally similar services. Quality of Service (QoS)plays as a discriminating factor in selecting which component services should be selected to satisfy the quality requirements of a user during service composition. There are two categories of approaches for QoS based service selection, namely global and local approaches. Global approaches are known to be Non-Polynomial (NP) hard in time and offer poor scalability in large scale composition. As an alternative to global methods, local selection methods which reduce the search space by breaking up the large/complex problem of selecting services for the workflow into independent sub problems of selecting services for individual tasks are coming up. In this paper, distributed architecture for selecting services based on QoS using local selection is presented with an overview of local selection methodology. The architecture describes the core components, namely, selection manager and QoS manager needed to implement the local approach and their functions. Selection manager consists of two components namely constraint decomposer which decomposes the given global or workflow level constraints in local or task level constraints and service selector which selects appropriate service for each task with maximum utility, satisfying the corresponding local constraints. QoS manager manages the QoS information at two levels namely, service class level and individual service level. The architecture serves as an implementation model for local selection.

Keywords: architecture of service selection, local method for service selection, QoS based service selection, approaches for QoS based service selection

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Authors: Gulnara Galeeva

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The Analytic Hierarchy Process is widely used in the economic and financial studies, including the formation of investment portfolios. In this study, a generalized method of obtaining a vector of priorities for the case with separate pairwise comparisons of the expert opinion being presented as a set of several equal evaluations on a ratio scale is examined. The author claims that this method allows solving an important and up-to-date problem of excluding vagueness and ambiguity of the expert opinion in the decision making theory. The study describes the authentic wide pairwise comparison matrix. Its application in the formation of the efficient investment portfolio of intangible assets of a small business enterprise with limited funding is considered. The proposed method has been successfully approbated on the practical example of a functioning dental clinic. The result of the study confirms that the wide pairwise comparison matrix can be used as a simple and reliable method for forming the enterprise investment policy. Moreover, a comparison between the method based on the wide pairwise comparison matrix and the classical analytic hierarchy process was conducted. The results of the comparative analysis confirm the correctness of the method based on the wide matrix. The application of a wide pairwise comparison matrix also allows to widely use the statistical methods of experimental data processing for obtaining the vector of priorities. A new method is available for simple users. Its application gives about the same accuracy result as that of the classical hierarchy process. Financial directors of small and medium business enterprises get an opportunity to solve the problem of companies’ investments without resorting to services of analytical agencies specializing in such studies.

Keywords: analytic hierarchy process, decision processes, investment portfolio, intangible assets

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Authors: Chih-Fong Tsai, Ya-Han Hu

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The aim of feature selection (or dimensionality reduction) is to filter out unrepresentative features (or variables) making the classifier perform better than the one without feature selection. Since there are many well-known feature selection algorithms, and different classifiers based on different selection results may perform differently, very few studies consider examining the effect of performing different feature selection algorithms on the classification performances by different classifiers over different types of datasets. In this paper, two widely used algorithms, which are the genetic algorithm (GA) and information gain (IG), are used to perform feature selection. On the other hand, three well-known classifiers are constructed, which are the CART decision tree (DT), multi-layer perceptron (MLP) neural network, and support vector machine (SVM). Based on 14 different types of datasets, the experimental results show that in most cases IG is a better feature selection algorithm than GA. In addition, the combinations of IG with DT and IG with SVM perform best and second best for small and large scale datasets.

Keywords: data mining, feature selection, pattern classification, dimensionality reduction

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Authors: Hala Alshamlan, Ghada Badr, Yousef Alohali

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Cancer is one of the dreadful diseases, which causes considerable death rate in humans. DNA microarray-based gene expression profiling has been emerged as an efficient technique for cancer classification, as well as for diagnosis, prognosis, and treatment purposes. In recent years, a DNA microarray technique has gained more attraction in both scientific and in industrial fields. It is important to determine the informative genes that cause cancer to improve early cancer diagnosis and to give effective chemotherapy treatment. In order to gain deep insight into the cancer classification problem, it is necessary to take a closer look at the proposed gene selection methods. We believe that they should be an integral preprocessing step for cancer classification. Furthermore, finding an accurate gene selection method is a very significant issue in a cancer classification area because it reduces the dimensionality of microarray dataset and selects informative genes. In this paper, we classify and review the state-of-art gene selection methods. We proceed by evaluating the performance of each gene selection approach based on their classification accuracy and number of informative genes. In our evaluation, we will use four benchmark microarray datasets for the cancer diagnosis (leukemia, colon, lung, and prostate). In addition, we compare the performance of gene selection method to investigate the effective gene selection method that has the ability to identify a small set of marker genes, and ensure high cancer classification accuracy. To the best of our knowledge, this is the first attempt to compare gene selection approaches for cancer classification using microarray gene expression profile.

Keywords: gene selection, feature selection, cancer classification, microarray, gene expression profile

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Authors: Dirk Briskorn, Michael Dienstknecht

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Cranes are a key element in construction projects as they are the primary lifting equipment and among the most expensive construction equipment. Thus, selecting cranes and locating them on-site is an important factor for a project's profitability. We focus on a site with supply and demand areas that have to be connected by tower cranes. There are several types of tower cranes differing in certain specifications such as costs or operating radius. The objective is to select cranes and determine their locations such that each demand area is connected to its supply area at minimum cost. We detail the problem setting and show how to obtain a discrete set of candidate locations for each crane type without losing optimality. This discretization allows us to reduce our problem to the classic set cover problem. Despite its NP-hardness, we achieve good results employing a standard solver and a greedy heuristic, respectively.

Keywords: positioning, selection, standard solver, tower cranes

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Authors: Pulak Swain, A. K. Ojha

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Portfolio optimization is based on dealing with the problems of efficient asset allocation. Risk and Expected return are two conflicting criteria in such problems, where the investor prefers the return to be high and the risk to be low. Using multi-objective approach we can solve those type of problems. However the information which we have for the input parameters are generally ambiguous and the input values can fluctuate around some nominal values. We can not ignore the uncertainty in input values, as they can affect the asset allocation drastically. So we use Robust Optimization approach to the problems where the input parameters comes under box uncertainty. In this paper, we solve the multi criteria robust problem with the help of  E- constraint method.

Keywords: portfolio optimization, multi-objective optimization, ϵ - constraint method, box uncertainty, robust optimization

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Authors: Gia Sirbiladze

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Route planning problems are among the activities that have the highest impact on logistical planning, transportation, and distribution because of their effects on efficiency in resource management, service levels, and client satisfaction. In extreme conditions, the difficulty of vehicle movement between different customers causes the imprecision of time of movement and the uncertainty of the feasibility of movement. A feasibility level of vehicle movement on the closed route of the disaster-streaking zone is defined for the construction of an objective function. Experts’ evaluations of the uncertain parameters in q-rung ortho-pair fuzzy numbers (q-ROFNs) are presented. A fuzzy bi-objective combinatorial optimization problem of fuzzy vehicle routine problem (FVRP) is constructed based on the technique of possibility theory. The FVRP is reduced to the bi-criteria partitioning problem for the so-called “promising” routes which were selected from the all-admissible closed routes. The convenient selection of the “promising” routes allows us to solve the reduced problem in real-time computing. For the numerical solution of the bi-criteria partitioning problem, the -constraint approach is used. The main results' support software is designed. The constructed model is illustrated with a numerical example.

Keywords: q-rung ortho-pair fuzzy sets, facility location selection problem, multi-objective combinatorial optimization problem, partitioning problem

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Authors: Keenoo Bibi Sumera, Alsheikh Mona, Mubarak Jan Beebee Zeba Mahetaab

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Background: Due to the busy schedule of the practicing clinicians at the hospitals, students may not always be attended to, which is to their detriment. The clinicians at the hospitals are also not always acquainted with teaching and/or supervising students on their placements. Additionally, there is a high student-patient ratio. Since they are the prospective clinical doctors under training, they need to reach the competence levels in clinical decision-making skills to be able to serve the healthcare system of the country and to be safe doctors. Aims and Objectives: A reflective portfolio was used to provide a means for students to learn by reflecting on their experiences and obtaining continuous feedback. This practice is an attempt to compensate for the scarcity of lack of resources, that is, clinical placement supervisors and patients. It is also anticipated that it will provide learners with a continuous monitoring and learning gap analysis tool for their clinical skills. Methodology: A hardcopy reflective portfolio was designed and validated. The portfolio incorporated a mini clinical evaluation exercise (mini-CEX), direct observation of procedural skills and reflection sections. Workshops were organized for the stakeholders, that is the management, faculty and students, separately. The rationale of reflection was emphasized. Students were given samples of reflective writing. The portfolio was then implemented amongst the undergraduate medical students of years four, five and six during clinical clerkship. After 16 weeks of implementation of the portfolio, a survey questionnaire was introduced to explore how undergraduate students perceive the educational value of the reflective portfolio and its impact on their deep information processing. Results: The majority of the respondents are in MD Year 5. Out of 52 respondents, 57.7% were doing the internal medicine clinical placement rotation, and 42.3% were in Otorhinolaryngology clinical placement rotation. The respondents believe that the implementation of a reflective portfolio helped them identify their weaknesses, gain professional development in terms of helping them to identify areas where the knowledge is good, increase the learning value if it is used as a formative assessment, try to relate to different courses and in improving their professional skills. However, it is not necessary that the portfolio will improve the self-esteem of respondents or help in developing their critical thinking, The portfolio takes time to complete, and the supervisors are not useful. They had to chase supervisors for feedback. 53.8% of the respondents followed the Gibbs reflective model to write the reflection, whilst the others did not follow any guidelines to write the reflection 48.1% said that the feedback was helpful, 17.3% preferred the use of written feedback, whilst 11.5% preferred oral feedback. Most of them suggested more frequent feedback. 59.6% of respondents found the current portfolio user-friendly, and 28.8% thought it was too bulky. 27.5% have mentioned that for a mobile application. Conclusion: The reflective portfolio, through the reflection of their work and regular feedback from supervisors, has an overall positive impact on the learning process of undergraduate medical students during their clinical clerkship.

Keywords: Portfolio, Reflection, Feedback, Clinical Placement, Undergraduate Medical Education

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Authors: Sirapop Nuannimnoi, Hojjat Baghban, Ching-Yao Huang

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Over the past decade, with the fast development of blockchain technology since the birth of Bitcoin, there has been a massive increase in the usage of Cryptocurrencies. Cryptocurrencies are not seen as an investment opportunity due to the market’s erratic behavior and high price volatility. With the recent success of deep reinforcement learning (DRL), portfolio management can be modeled and automated. In this paper, we propose a novel DRL-based multi-agent system to automatically make proper trading decisions on multiple cryptocurrencies and gain profits in the highly volatile cryptocurrency market. We also extend this multi-agent system with horizontal federated transfer learning for better adapting to the inclusion of new cryptocurrencies in our portfolio; therefore, we can, through the concept of diversification, maximize our profits and minimize the trading risks. Experimental results through multiple simulation scenarios reveal that this proposed algorithmic trading system can offer three promising key advantages over other systems, including maximized profits, minimized risks, and adaptability.

Keywords: cryptocurrency portfolio management, algorithmic trading, federated learning, multi-agent reinforcement learning

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Authors: Jiraporn Lao-Un, Dararat Khampusaen

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Lack of exposure to English language in the authentic English setting naturally leads to a lack of fluency in the language. As a result, Thai EFL learners are struggling in meeting with the communication 'can do' descriptors of the Common European Framework of References (CEFR) required by the Ministry of Education. This initial phase of the ongoing study, employing the e-portfolio to promote the English speaking ability, probed into the effects of the use of e-portfolio on Thai EFL nursing students' speaking ability. Also, their opinions towards the use of e-portfolio to enhance their speaking ability were investigated. The participants were 44 undergraduate nursing students at a Thai College of Nursing. The participants undertook four lessons to promote their communication skills according to the CEFR criteria. Throughout the semester, the participants videotaped themselves while completing the four speaking tasks. The videos were then uploaded onto the e-portfolio website where the researcher provided them with the feedbacks. The video records were analyzed by the speaking rubric designed according to the CEFR 'can do' descriptors. Also, students were required to record self-reflections in video format and upload onto the same URL Students' oral self-reflections were coded to find out the perceptions towards the use of the e-portfolio in promoting their speaking ability. The results from the two research instruments suggested the effectiveness of the tool on improving speaking ability, learner autonomy and media literacy skills. In addition, the oral reflection videos revealed positive opinion towards the tool. The discussion offers the current status of English speaking ability among Thai EFL students. This reveals the gaps between the EFL speaking ability and the CEFR ‘can do’ descriptors. In addition, the author raises the light on integration of the 21st century IT tool to enhance these students’ speaking ability. Lastly, the theoretical implications and recommendation for further study in integrating electronic tools to promote language skills in the EFL context are offered for further research.

Keywords: EFL communication, EFL speaking, English communication, E-learning, E-portfolio, speaking ability, Thai EFL learners

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Authors: Serhat Tüzün, Tufan Demirel

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The objective of the present study is to select the most suitable location for a wind power plant station through Choquet integral method. The problem of selecting the location for a wind power station was considered as a multi-criteria decision-making problem. The essential and sub-criteria were specified and location selection was expressed in a hierarchic structure. Among the main criteria taken into account in this paper are wind potential, technical factors, social factors, transportation, and costs. The problem was solved by using different approaches of Choquet integral and the best location for a wind power station was determined. Then, the priority weights obtained from different Choquet integral approaches are compared and commented on.

Keywords: multi-criteria decision making, choquet integral, fuzzy sets, location of a wind power plant

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Authors: Emmanuel U. Unuafe, Oyegoke T. Bukoye, Sandhya Sastry, Yanqing Duan

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Globally, there is increasing interest in project management due to a shortage in infrastructure services supply capability. Hence, it is of utmost importance that organisations understand that choosing a particular project over another is an opportunity cost – tying up the organisations resources. In order to devise constructive ways to bring direction, structure, and oversight to the process of project selection has led to the development of tools and techniques by researchers and practitioners. However, despite the development of various frameworks to assist in the appraisal and selection of government projects, failures are still being recorded with government projects. In developing countries, where frameworks are rarely used, the problems are compounded. To improve the situation, this study will investigate the current practice of construction project selection processes within the Nigeria public sector in order to inform theories of decision making from the perspective of developing nations and project management practice. Unlike other research around construction projects in Nigeria this research concentrate on factors influencing the selection process within the Nigeria public sector, which has received limited study. The authors report the findings of semi-structured interviews of top management in the Nigerian public sector and draw conclusions in terms of decision making extant theory and current practice. Preliminary results from the data analysis show that groups make project selection decisions and this forces sub-optimal decisions due to pressure on time, clashes of interest, lack of standardised framework for selecting projects, lack of accountability and poor leadership. Consequently, because decision maker is usually drawn from different fields, religious beliefs, ethnic group and with different languages. The choice of a project by an individual will be greatly influence by experience, political precedence than by realistic investigation as well as his understanding of the desired outcome of the project, in other words, the individual’s ideology and their level of fairness.

Keywords: factors influencing project selection, public sector construction project selection, projects portfolio selection, strategic decision-making

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Authors: Guilherme Baldo Carlos

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The flexible job-shop scheduling problem (FJSP) is an NP-hard combinatorial optimization problem, which can be applied to model several applications in a wide array of industries. This problem will have its importance increase due to the shift in the production mode that modern society is going through. The demands are increasing and for products personalized and customized. This work aims to apply a meta-heuristic called a genetic algorithm (GA) to solve this problem. A GA is a meta-heuristic inspired by the natural selection of Charles Darwin; it produces a population of individuals (solutions) and selects, mutates, and mates the individuals through generations in order to find a good solution for the problem. The results found indicate that the GA is suitable for FJSP solving.

Keywords: genetic algorithm, evolutionary algorithm, scheduling, flexible job-shop scheduling

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Authors: Cavidan Yakupoglu, Kurt Rohloff

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In this study, we aim to provide a novel parameter selection model for the BFVrns scheme, which is one of the prominent FHE schemes. Parameter selection in lattice-based FHE schemes is a practical challenges for experts or non-experts. Towards a solution to this problem, we introduce a hybrid principles-based approach that combines theoretical with experimental analyses. To begin, we use regression analysis to examine the parameters on the performance and security. The fact that the FHE parameters induce different behaviors on performance, security and Ciphertext Expansion Factor (CEF) that makes the process of parameter selection more challenging. To address this issue, We use a multi-objective optimization algorithm to select the optimum parameter set for performance, CEF and security at the same time. As a result of this optimization, we get an improved parameter set for better performance at a given security level by ensuring correctness and security against lattice attacks by providing at least 128-bit security. Our result enables average ~ 5x smaller CEF and mostly better performance in comparison to the parameter sets given in [1]. This approach can be considered a semiautomated parameter selection. These studies are conducted using the PALISADE homomorphic encryption library, which is a well-known HE library. The abstract goes here.

Keywords: lattice cryptography, fully homomorphic encryption, parameter selection, LWE, RLWE

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Authors: Yi-Min Lan, Hung-Wen Cheng, Hsuan-Ling Chang, Jou-Ping Yu

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The study applies machine learning models to construct momentum strategies and utilizes the information coefficient as an indicator for selecting stocks with strong and weak momentum characteristics. Through this approach, the study has built investment portfolios capable of generating superior returns and conducted a thorough analysis. Compared to existing research on momentum strategies, machine learning is incorporated to capture non-linear interactions. This approach enhances the conventional stock selection process, which is often impeded by difficulties associated with timeliness, accuracy, and efficiency due to market risk factors. The study finds that implementing bidirectional momentum strategies outperforms unidirectional ones, and momentum factors with longer observation periods exhibit stronger correlations with returns. Optimizing the number of stocks in the portfolio while staying within a certain threshold leads to the highest level of excess returns. The study presents a novel framework for momentum strategies that enhances and improves the operational aspects of asset management. By introducing innovative financial technology applications to traditional investment strategies, this paper can demonstrate significant effectiveness.

Keywords: information coefficient, machine learning, momentum, portfolio, return prediction

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Authors: Mia Françoise

Abstract:

This report aims to develop a strategy for assisting short-term investors to benefit from the current economic climate in South Africa by utilizing technical analysis techniques and predictive analytics. As part of this research, value investing and technical analysis principles will be combined to maximize returns for South African investors while optimizing volatility. As an emerging market, South Africa offers many opportunities for high growth in sectors where other developed countries cannot grow at the same rate. Investing in South African companies with significant growth potential can be extremely rewarding. Although the risk involved is more significant in countries with less developed markets and infrastructure, there is more room for growth in these countries. According to recent research, the offshore market is expected to outperform the local market over the long term; however, short-term investments in the local market will likely be more profitable, as the Johannesburg Stock Exchange is predicted to outperform the S&P500 over the short term. The instabilities in the economy contribute to increased market volatility, which can benefit investors if appropriately utilized. Price prediction and portfolio optimization comprise the two primary components of this methodology. As part of this process, statistics and other predictive modeling techniques will be used to predict the future performance of stocks listed on the Johannesburg Stock Exchange. Following predictive data analysis, Modern Portfolio Theory, based on Markowitz's Mean-Variance Theorem, will be applied to optimize the allocation of assets within an investment portfolio. By combining different assets within an investment portfolio, this optimization method produces a portfolio with an optimal ratio of expected risk to expected return. This methodology aims to provide a short-term investment with a stock portfolio that offers the best risk-to-return profile for stocks listed on the JSE by combining price prediction and portfolio optimization.

Keywords: financial stocks, optimized asset allocation, prediction modelling, South Africa

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