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Real-world datasets for portfolio selection and solutions of some stochastic dominance portfolio models.

ABSTRACT: A large number of portfolio selection models have appeared in the literature since the pioneering work of Markowitz. However, even when computational and empirical results are described, they are often hard to replicate and compare due to the unavailability of the datasets used in the experiments. We provide here several datasets for portfolio selection generated using real-world price values from several major stock markets. The datasets contain weekly return values, adjusted for dividends and for stock splits, which are cleaned from errors as much as possible. The datasets are available in different formats, and can be used as benchmarks for testing the performances of portfolio selection models and for comparing the efficiency of the algorithms used to solve them. We also provide, for these datasets, the portfolios obtained by several selection strategies based on Stochastic Dominance models (see "On Exact and Approximate Stochastic Dominance Strategies for Portfolio Selection" (Bruni et al. [2])). We believe that testing portfolio models on publicly available datasets greatly simplifies the comparison of the different portfolio selection strategies.

SUBMITTER: Bruni R

PROVIDER: S-EPMC4959918 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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Real-world datasets for portfolio selection and solutions of some stochastic dominance portfolio models.

Bruni Renato R Cesarone Francesco F Scozzari Andrea A Tardella Fabio F

Data in brief 20160628

A large number of portfolio selection models have appeared in the literature since the pioneering work of Markowitz. However, even when computational and empirical results are described, they are often hard to replicate and compare due to the unavailability of the datasets used in the experiments. We provide here several datasets for portfolio selection generated using real-world price values from several major stock markets. The datasets contain weekly return values, adjusted for dividends and ...[more]

PMID: 27508232

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Project description:BackgroundClinical data is instrumental to medical research, machine learning (ML) model development, and advancing surgical care, but access is often constrained by privacy regulations and missing data. Synthetic data offers a promising solution to preserve privacy while enabling broader data access. Recent advances in large language models (LLMs) provide an opportunity to generate synthetic data with reduced reliance on domain expertise, computational resources, and pre-training.ObjectiveThis study aims to assess the feasibility of generating realistic tabular clinical data with OpenAI's GPT-4o using zero-shot prompting, and evaluate the fidelity of LLM-generated data by comparing its statistical properties to the Vital Signs DataBase (VitalDB), a real-world open-source perioperative dataset.MethodsIn Phase 1, GPT-4o was prompted to generate a dataset with qualitative descriptions of 13 clinical parameters. The resultant data was assessed for general errors, plausibility of outputs, and cross-verification of related parameters. In Phase 2, GPT-4o was prompted to generate a dataset using descriptive statistics of the VitalDB dataset. Fidelity was assessed using two-sample t-tests, two-sample proportion tests, and 95% confidence interval (CI) overlap.ResultsIn Phase 1, GPT-4o generated a complete and structured dataset comprising 6,166 case files. The dataset was plausible in range and correctly calculated body mass index for all case files based on respective heights and weights. Statistical comparison between the LLM-generated datasets and VitalDB revealed that Phase 2 data achieved significant fidelity. Phase 2 data demonstrated statistical similarity in 12/13 (92.31%) parameters, whereby no statistically significant differences were observed in 6/6 (100.0%) categorical/binary and 6/7 (85.71%) continuous parameters. Overlap of 95% CIs were observed in 6/7 (85.71%) continuous parameters.ConclusionZero-shot prompting with GPT-4o can generate realistic tabular synthetic datasets, which can replicate key statistical properties of real-world perioperative data. This study highlights the potential of LLMs as a novel and accessible modality for synthetic data generation, which may address critical barriers in clinical data access and eliminate the need for technical expertise, extensive computational resources, and pre-training. Further research is warranted to enhance fidelity and investigate the use of LLMs to amplify and augment datasets, preserve multivariate relationships, and train robust ML models.

Project description:ObjectiveTo use a practical approach to examining the use of Expert Recommendations for Implementing Change (ERIC) strategies by Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) dimensions for rural health innovations using annual reports on a diverse array of initiatives.Data sources and study settingThe Veterans Affairs (VA) Office of Rural Health (ORH) funds initiatives designed to support the implementation and spread of innovations and evidence-based programs and practices to improve the health of rural Veterans. This study draws on the annual evaluation reports submitted for fiscal years 2020-2022 from 30 of these enterprise-wide initiatives (EWIs).Study designContent analysis was guided by the RE-AIM framework conducted by the Center for the Evaluation of Enterprise-Wide Initiatives (CEEWI), a Quality Enhancement Research Initiative (QUERI)-ORH partnered evaluation initiative.Data collection and extraction methodsCEEWI analysts conducted a content analysis of EWI annual evaluation reports submitted to ORH. Analysis included cataloguing reported implementation strategies by Reach, Adoption, Implementation, and Maintenance (RE-AIM) dimensions (i.e., identifying strategies that were used to support each dimension) and labeling strategies using ERIC taxonomy. Descriptive statistics were conducted to summarize data.Principal findingsA total of 875 implementation strategies were catalogued in 73 reports. Across these strategies, 66 unique ERIC strategies were reported. EWIs applied an average of 12 implementation strategies (range 3-22). The top three ERIC clusters across all 3 years were Develop stakeholder relationships (21%), Use evaluative/iterative strategies (20%), and Train/educate stakeholders (19%). Most strategies were reported within the Implementation dimension. Strategy use among EWIs meeting the rurality benchmark were also compared.ConclusionsCombining the dimensions from the RE-AIM framework and the ERIC strategies allows for understanding the use of implementation strategies across each RE-AIM dimension. This analysis will support ORH efforts to spread and sustain rural health innovations and evidence-based programs and practices through targeted implementation strategies.

Project description:We present comprehensive datasets of Brazilian disasters from January 2003 to February 2021 as well as real-world optimization instances built up from these data. The data were gathered through a series of open available reports obtained from different government and institutional sources. Afterwards, data consolidation and summarization were carried out using Excel and Python. The datasets include 9 types of disaster, such as flash floods, landslides and droughts, and the corresponding number of affected people during an 18-year or a 218-month observation period for 5,402 Brazilian municipalities, totaling more than 65,000 observations. Data on relevant geographical, demographic and socioeconomic aspects of the affected municipalities are also provided. These encompass geographic coordinates, regions, population, income, development indicators, amongst other information. From a statistical point of view, the data on disasters can address a number of applications using both supervised and unsupervised machine learning techniques such as, for time series analysis or other dynamic models using socioeconomic data as explanatory variables, i.e. data on the size of the poor population, income, education and general development. The geographic dataset can be useful for aggregating analyses concerning the various forms of territorial organization and allows for the visualization of data in maps. All the aforementioned data can be also used to devise realistic optimization instances related to diverse humanitarian logistics and/or disaster management problems, such as facility location, location-allocation, vehicle routing, and so forth. In particular, we describe two real-world instances for the location-allocation problem studied in [1]. For that purpose, we partially use the given datasets and included other information such as costs and distances relevant to the optimization model. Although using real-world cases to test optimization approaches is a common and encouraged practice in Operations Research, comprehensive datasets and practical optimization instances, as presented in this article, are rarely described and/or available in the academic literature.

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Real-world datasets for portfolio selection and solutions of some stochastic dominance portfolio models.

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Real-world datasets for portfolio selection and solutions of some stochastic dominance portfolio models.

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