Sectoral portfolio optimization by judicious selection of financial ratios via PCA

Embedding value investment in portfolio optimization models has always been a challenge. In this paper, we attempt to incorporate it by first employing principal component analysis (PCA) sector wise to filter out dominant financial ratios from each sector and thereafter, use the portfolio optimization model incorporating second order stochastic dominance (SSD) criteria to derive the final optimal investment. We consider a total of 11 well known financial ratios corresponding to each sector representing four categories of ratios, namely liquidity, solvency, profitability, and valuation. PCA is then applied sector wise over a period of 10 years from April 2004 to March 2014 to extract dominant ratios from each sector in two ways, one from the component solution and other from each category on the basis of their communalities. The two step Sectoral Portfolio Optimization (SPO) model integrating the SSD criteria in constraints is then utilized to build an optimal portfolio. The strategy formed using the former extracted ratios is termed as PCA-SPO(A) and the latter one as PCA-SPO(B). The results obtained from the proposed strategies are compared with the SPO model and two nominal SSD models, with and without financial ratios for computational study. Empirical performance of proposed strategies is assessed over the period of 6 years from April 2014 to March 2020 using a rolling window scheme with varying out-of-sample time line of 3, 6, 9, 12 and 24 months for S&P BSE 500 market. We observe that the proposed strategy PCA-SPO(B) outperforms all other models in terms of downside deviation, CVaR, VaR, Sortino ratio, Rachev ratio, and STARR ratios over almost all out-of-sample periods. This highlights the importance of value investment where ratios are carefully selected and embedded quantitatively in portfolio selection process.Comment: 26 pages, 12 table

Green suppler selection by an integrated method with stochastic acceptability analysis and MULTIMOORA

In the process of supplier selection for green supply chain management, uncertain information may appear in alternatives’ performances or experts’ preferences. The stochastic multicriteria acceptability analysis (SMAA) is a beneficial technique to tackling the uncertain information in such a problem and the MULTIMOORA is a robust technique to aggregate alternatives’ utilities. This study dedicates to proposing an SMAA-MULTIMOORA method by considering the advantages of both methods. The integrated method can accept uncertain information as inputs. The steps of the SMAA-MULTIMOORA are illustrated. A case study about the selection of green suppliers is given to show the validity and robustness of the SMAA-MULTIMOORA method

Multicriteria Decision Making in Sustainable Tourism and Low-Carbon Tourism Research: A Systematic Literature Review

Multicriteria Decision Making (MCDM) is increasingly being utilized as an analytical research tool for sectors that require decision-making with specific objectives and constraints, such as the tourism industry. Sustainable tourism, which examines the balance of numerous aspects, including stakeholders’ interests, is the critical feature propelling the increased usage of MCDM. This paper explores the use of Multicriteria Decision Making (MCDM) methods applied in studies of sustainable tourism and its derivative term, low-carbon tourism, using a systematic literature review (SLR) search from the Scopus database. The analysis has identified 189 relevant studies published between 1987 to April 2022. After selection, screening, and synthesizing processes, we selected 135 pertinent studies, which were analysed in general descriptive data, citation impacts, geographical categorization, categorization of the methodologies’ objectives, and possible trajectories of similar research in the future. We find that highly cited authors and articles are related to sustainable tourism indicators\u27 development and case studies. Furthermore, most relevant studies are concentrated in Asia and Europe rather than other regions. We also categorize the reviewed studies into six classifications depending on each method\u27s intended usage and further suggest four contexts for the studies’ future trajectory

Human Behavior Modeling: The Necessity of Narrative

As progress is made in the development of artificial intelligent mechanisms to assist human research into aspects of industrial, biomechanical and biomedical engineering, the conceptualization of mental behavior of human entities become more vital and more central to the success of any interaction between machine and humans. This discussion explores one of the most important features of human behavior, the fundamental and irreversible concept of narrative. The narrative is the essential construct for the theoretical understanding and presentation of human communication, including formal and informal logic, emotional wonder and desperation, noble and selfish biases, nationalism and globalist politics, and any form of spiritualism. This presentation offers a working definition of human narrative and proposes its basic structure that must be represented by any computer system which is required to deal with human behavior

Partner selection in virtual enterprises

Tese de doutoramento. Engenharia Industrial e Gestão. Faculdade de Engenharia. Universidade do Porto. 200

Hotel selection utilizing online reviews: a novel decision support model based on sentiment analysis and DL-VIKOR method

With the considerable development of tourism market, as well as the expansion of the e-commerce platform scale, increasing tourists often prefer to select tourism products such as services or hotels online. Thus, it needs to provide an efficient decision support model for tourists to select tourism products. Online reviews based on the user experience would help tourists improve decision efficiency on tourism products. Therefore, in this study, a quantitative method for hotel selection with online reviews is proposed. First, with respect this problem with online reviews, by analyzing sentiment words in online reviews, tourists’ sentiment preferences are transformed into the format of distribution linguistic with respect to sentiment levels. Second, from a theoretical perspective, we proposed a method to determine the ideal solution and nadir solution for distribution linguistic evaluations. Next, based on the frequency of words for evaluating hotel and the distribution linguistic evaluations, the weight vector of the evaluation features is determined. Further, a novel DL-VIKOR method is developed to rank and then to select hotels. Finally, a realistic case from TripAdvisor.com for selecting hotel is used to demonstrate practically and feasibility of the proposed model. First published online 19 July 201

JobComposer: Career path optimization via multicriteria utility learning

With online professional network platforms (OPNs, e.g., LinkedIn, Xing, etc.) becoming popular on the web, people are now turning to these platforms to create and share their professional profiles, to connect with others who share similar professional aspirations and to explore new career opportunities. These platforms however do not offer a long-term roadmap to guide career progression and improve workforce employability. The career trajectories of OPN users can serve as a reference but they are not always optimal. A career plan can also be devised through consultation with career coaches, whose knowledge may however be limited to a few industries. To address the above limitations, we present a novel data-driven approach dubbed JobComposer to automate career path planning and optimization. Its key premise is that the observed career trajectories in OPNs may not necessarily be optimal, and can be improved by learning to maximize the sum of payoffs attainable by following a career path. At its heart, JobComposer features a decomposition-based multicriteria utility learning procedure to achieve the best tradeoff among different payoff criteria in career path planning. Extensive studies using a city state-based OPN dataset demonstrate that JobComposer returns career paths better than other baseline methods and the actual career paths