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Smart network based portfolios.

ABSTRACT: In this article we deal with the problem of portfolio allocation by enhancing network theory tools. We propose the use of the correlation network dependence structure in constructing some well-known risk-based models in which the estimation of the correlation matrix is a building block in the portfolio optimization. We formulate and solve all these portfolio allocation problems using both the standard approach and the network-based approach. Moreover, in constructing the network-based portfolios we propose the use of three different estimators for the covariance matrix: the sample, the shrinkage toward constant correlation and the depth-based estimators . All the strategies under analysis are implemented on three high-dimensional portfolios having different characteristics. We find that the network-based portfolio consistently performs better and has lower risk compared to the corresponding standard portfolio in an out-of-sample perspective.

Supplementary information

The online version contains supplementary material available at 10.1007/s10479-022-04675-7.

SUBMITTER: Clemente GP

PROVIDER: S-EPMC8995926 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Smart network based portfolios.

Clemente Gian Paolo GP Grassi Rosanna R Hitaj Asmerilda A

Annals of operations research 20220411 2

In this article we deal with the problem of portfolio allocation by enhancing network theory tools. We propose the use of the correlation network dependence structure in constructing some well-known risk-based models in which the estimation of the correlation matrix is a building block in the portfolio optimization. We formulate and solve all these portfolio allocation problems using both the standard approach and the network-based approach. Moreover, in constructing the network-based portfolios ...[more]

PMID: 35431386

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Project description:IntroductionThe need for longitudinal and comprehensive evidenced development of surgical knowledge, skills and attitudes, and the attainment of appropriate competencies have spotlighted portfolio use. Yet variations in structure, content and roles as well as its resource-heavy reputation have limited its impact. The purpose of this study is to map prevailing data on the use of portfolios in postgraduate surgical education to address a lack of e-portfolio use and guide its design and structuring in surgery.Material and methodsA Systematic Evidenced Based Approach (SEBA) guided systematic scoping review (SSR) (SSR in SEBA) was conducted to map prevailing data. The constructivist ontological approach used allowed for enhanced reflexivity in the analysis and discussion.Results13,092 articles were reviewed, 839 full-text articles were evaluated, and 37 articles were included. Concurrent thematic and content analysis revealed similar themes and categories which were the role of portfolios, evaluation of portfolio use, and implementation of e-portfolios.DiscussionLessons from traditional surgical portfolios can be employed to enhance the design, support and utility of e-portfolios and yet maintain as a balanced trainee-led perspective of their progress. Focused on documenting the trainee's competencies, achievements, experiences, feedback, and reflections, a variety of methods are presently used to assess the reliability, validity, educational impact, acceptability and feasibility of surgical portfolios. The use of e-portfolios, in particular, has been adopted due to its accessibility, portability and convenience. Beyond its flexibility in access, repository and content, e-portfolios offer increased user accessibility, better quality interactions and smoother facilitation, clarification, and feedback.ConclusionsThe data in this SSR in SEBA underscores the need for systematic assessment tools to evaluate e-portfolio as well as evaluate the programs as a whole. Further research into training mentors to support e-portfolio use, assess progress and ensure effective oversight of e-portfolios are required.

Project description:This work explores an intelligent field irrigation warning system based on the Enhanced Genetic Algorithm-Backpropagation Neural Network (EGA-BPNN) model in the context of smart agriculture. To achieve this, irrigation flow prediction in agricultural fields is chosen as the research topic. Firstly, the BPNN principles are studied, revealing issues such as sensitivity to initial values, susceptibility to local optima, and sample dependency. To address these problems, a genetic algorithm (GA) is adopted for optimizing the BPNN, and the EGA-BPNN model is used to predict irrigation flow in agricultural fields. Secondly, the EGA-BPNN model can overcome the local optimization and overfitting problems of traditional BPNN through the global search ability of GA. Moreover, it is suitable for the irrigation flow prediction task with complex environmental factors in smart agriculture. Finally, comparative experiments compare the prediction accuracy of BPNN and EGA-BPNN using single and dual water level flow prediction models respectively. The results reveal that as the number of nodes in the hidden layer increases, the model's Mean Squared Error (MSE) and Relative Error (RE) show a decreasing trend, indicating an improvement in model prediction accuracy. When the number of nodes in the hidden layer increases from 6 to 16, the MSE of the single and dual water level flow prediction models decreases from 4.53×10-4 to 3.68×10-4 and 2.38×10-4 to 1.66×10-4, respectively. Under a standalone BPNN, the absolute relative error in flow prediction is 1.09%. In contrast, the EGA-BPNN model achieves a significantly lower mean absolute relative error of 0.41% for single-flow prediction, demonstrating superior prediction performance. Furthermore, compared to the BPNN, the EGA-BPNN model exhibits a 2.11 reduction in MSE, further emphasizing the positive impact of introducing the GA on model performance. The research outcomes contribute to more accurate water resource planning and management, providing a more reliable basis for decision-making.

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Smart network based portfolios.

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Smart network based portfolios.

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