Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis

Search for heavy resonances decaying to two Higgs bosons in final states containing four b quarks

A search is presented for narrow heavy resonances X decaying into pairs of Higgs bosons (H) in proton-proton collisions collected by the CMS experiment at the LHC at root s = 8 TeV. The data correspond to an integrated luminosity of 19.7 fb(-1). The search considers HH resonances with masses between 1 and 3 TeV, having final states of two b quark pairs. Each Higgs boson is produced with large momentum, and the hadronization products of the pair of b quarks can usually be reconstructed as single large jets. The background from multijet and t (t) over bar events is significantly reduced by applying requirements related to the flavor of the jet, its mass, and its substructure. The signal would be identified as a peak on top of the dijet invariant mass spectrum of the remaining background events. No evidence is observed for such a signal. Upper limits obtained at 95 confidence level for the product of the production cross section and branching fraction sigma(gg -> X) B(X -> HH -> b (b) over barb (b) over bar) range from 10 to 1.5 fb for the mass of X from 1.15 to 2.0 TeV, significantly extending previous searches. For a warped extra dimension theory with amass scale Lambda(R) = 1 TeV, the data exclude radion scalar masses between 1.15 and 1.55 TeV

Search for supersymmetry in events with one lepton and multiple jets in proton-proton collisions at root s=13 TeV

Peer reviewe

Measurement of the top quark mass using charged particles in pp collisions at root s=8 TeV

Peer reviewe

Search for anomalous couplings in boosted WW/WZ -> l nu q(q)over-bar production in proton-proton collisions at root s=8TeV

Peer reviewe

The requirements analysis for the Meta-Model Sustainability Impact Assessment Tools (SIAT) to ensure a High Use and Acceptability among Policy Decision Makers - A Critical Review

This paper focuses a critical analysis of the science-policy interface in the frame of the SENSOR project. The meta-model Sustainability Impact Assessment Tools (SIAT) is the central tool of the European research project, which was developed over four years. We illustrate the methodology and procedure of SIAT that is tailored to simulate land use policies. SIAT allows conducting ex-ante sustainability impact assessment towards the target year 2025 at the level of 570 European regions. A critical analysis at the policy-science interface discusses the procedure of the SIAT development process and reveals the mean of prototyping as basis for the requirement analysis. We summarise the major problems we faced at intuitional level that influenced the quality of the requirement analysis. Finally we conclude on the institutional reasons for asymmetric information that (i) hinders efficient stakeholder involvement and (ii) causes shortcoming to mirror precise end-user requirements in the architectural model design: Quantifying utility level on realistic needs is not precisely applicable due to (a) high opportunity costs to survey and harmonise individual requirements, (b) uncertain forecasts on costs estimates, (c) asymmetric information related to high transactions costs for communication and strategic behaviour of policy makers, researcher and IT developer, (d) requested but unfeasible technical implementation possibilities, (e) predefined and thus limited ¿room of manoeuvre¿ and constraints laid down in research proposals and resulting contracts. We conclude that the reality always differs from theoretical optimum, although actual decisions on model design should follow ideal principles as much as the information is available.JRC.DG.J.5-Agriculture and Life Sciences in the Econom

Meta-Modelling for cross-sectoral Land Use Policy Impact Analysis: A Model requirements analysis of the SIAT

The Integrated Project SENSOR developed the meta-model Sustainability Impact Assessment Tools (SIAT) to support decision making at the EU level (Sieber et al. 2008). First, this paper illustrates the concept on the functionality and methodology of the meta-model SIAT. Based on the development process and applied methods of software prototyping major model requirements have been elaborated. Four major requirement fields have been revealed. They can be subsumed into the categories of (1) performance, (2) integration, (3) institution and (4) quality (IIPQ), whereas point (1) is classified (a) spatial scales, (b) sectors and (c) indicator variables, (d) dynamic viability over time, (e) flexible system to select the level of implicit and explicit knowledge; point (2) is classified in (a) model response time (b) diversity of visualization results, (c) advanced technologies and system compatibility; point (3) on reliability information is structured in the three fields of (a) quality criteria on results, (b) assumptions and (c) back tracing. The point (4) on institutional embedment links the institutions; either trough stakeholder participation or expert consultations. The SIAT is finally classified according to the given criteria. The paper concludes on further requirements to maintain the model beyond project lifetime. Mainly funding issues, complementary further institutional embedment, flexibility towards new scopes of analysis, quality assurance and scientific publications have been described as major future challenges.JRC.DDG.J.5-Agriculture and Life Sciences in the Econom

Evaluating the characteristics of a non-standardised Model Requirements Analysis (MRA) for the development of policy impact assessment tools

The aim of this paper is to provide a critical analysis of the strengths and weaknesses of a nonstandardised Model Requirements Analysis (MRA) used for the purpose of developing the Sustainability Impact Assessment Tool (SIAT). By ‘non-standardised’ we mean not strictly following a published MRA method. The underlying question we are interested in addressing is how non-standardised methods, often employed in research driven projects, compare to defined methods with more standardised structure, with regards their ability to capture model requirements effectively, and with regards their overall usability. Through describing and critically assessing the specific features of the nonstandardised MRA employed, the ambition of this paper is to provide insights useful for impact assessment tool (IAT) development. Specifically, the paper will (i) characterise kinds of user requirements relevant to the functionality and design of IATs; (ii) highlight the strengths and weaknesses of nonstandardised MRA for user requirements capture, analysis and reflection in the context of IAT; (iii) critically reflect on the process and outcomes of having used a non-standardised MRA in comparison with other more standardised approaches. To accomplish these aims, we first review methods available for IAT development before describing the SIAT development process, including the MRA employed. Major strengths and weaknesses of the MRA method are then discussed in terms of user identification and characterisation, organisational characterisation and embedding, and ability to capture design options for ensuring usability and usefulness. A detailed assessment on the structural differences of MRA with two advanced approaches (Integrated DSS design and goal directed design) and their role in performance of the MRA tool is used to critique the approach employed. The results show that MRA is able to bring thematic integration, establish system performance and technical thresholds as well as detailing quality and transparency guidelines. Nevertheless the discussion points out to a number of deficiencies in application - (i) a need to more effectively characterise potential users, and; (ii) a need to better foster communication among the distinguished roles in the development process. If addressed these deficiencies, SIAT non-standardised MRA could have brought out better outcomes in terms of tool usability and usefulness, and improved embedding of the tool into conditions of targeted end-users.JRC.J.4-Agriculture and Life Sciences in the Econom

Machine learning using the extreme gradient boosting (XGBoost) algorithm predicts 5-day delta of SOFA score at ICU admission in COVID-19 patients

Background: Accurate risk stratification of critically ill patients with coronavirus disease 2019 (COVID-19) is essential for optimizing resource allocation, delivering targeted interventions, and maximizing patient survival probability. Machine learning (ML) techniques are attracting increased interest for the development of prediction models as they excel in the analysis of complex signals in data-rich environments such as critical care. Methods: We retrieved data on patients with COVID-19 admitted to an intensive care unit (ICU) between March and October 2020 from the RIsk Stratification in COVID-19 patients in the Intensive Care Unit (RISC-19-ICU) registry. We applied the Extreme Gradient Boosting (XGBoost) algorithm to the data to predict as a binary out- come the increase or decrease in patients’ Sequential Organ Failure Assessment (SOFA) score on day 5 after ICU admission. The model was iteratively cross-validated in different subsets of the study cohort. Results: The final study population consisted of 675 patients. The XGBoost model correctly predicted a decrease in SOFA score in 320/385 (83%) critically ill COVID-19 patients, and an increase in the score in 210/290 (72%) patients. The area under the mean receiver operating characteristic curve for XGBoost was significantly higher than that for the logistic regression model (0.86 vs . 0.69, P < 0.01 [paired t -test with 95% confidence interval]). Conclusions: The XGBoost model predicted the change in SOFA score in critically ill COVID-19 patients admitted to the ICU and can guide clinical decision support systems (CDSSs) aimed at optimizing available resources