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527 results for “Financial”
Benchmark and training data for replicating financial and insurance examples
<p>This dataset contains training, validation and out-of-sample test data for two European calls and two examples of portfolio of variable annuity guarantees.</p>
Survey data on financial literacy, financial inclusion, informal financial business practices, and intentions towards formalization of female small vendors in Lima, Peru
<p><span>This dataset encapsulates a comprehensive survey aimed at understanding informal business practices and financial literacy among small business vendors in Peru. The dataset comprises three key components: the survey questionnaire, raw survey data, and a detailed codebook. Researchers interested in the dynamics of financial practices in emerging markets may find this dataset particularly valuable, as it allows for the exploration of factors influencing financial decisions in small enterprises, with potential modifications suggested for adapting the survey to different national or cultural contexts. This dataset not only contributes to empirical research in financial behavior but also supports gender-specific studies by allowing the variable 'sex' to be adapted to 'gender' with multiple response options. </span></p> <p><span>The data and supplementary material is divided in tree files:</span></p> <p><span>The survey, presented in "Survey IFE.docx," includes questions across various domains such as informal business practices, financial literacy, financial inclusion, intentions towards financial formalization, and the formality of business ventures, along with demographic variables like age, sex, business age, and number of employees. </span></p> <p><span>The raw data, stored in "Dataset.csv," records responses from 118 participants, mapped against 31 indicators. </span></p> <p><span>The "Codebook.doc" provides exhaustive details about the survey variables, coding of responses, and the methodology employed, facilitating the replication of the study and application of the dataset in varied research contexts.</span></p>
How Can Subjective Well-being be Increased? The Role of Social Comparison and Subjective Financial Well-Being
<p><strong>Data Dictionary :</strong></p> <p>CFA = Confirmatory factor analysis</p> <p>Finwell = SFW = Financial well being</p> <p>CS = PK =social comparison</p> <p>IFLS = Indonesian Family Life Survey</p> <p> </p> <p> </p>
Financial Metrics Dataset of US companies
<p>Financial Metrics Dataset of US companies obtained from 10-K filings (in XBRL format) from SEC. The dataset contains financial metric answers for 9,263 US companies and in total 800,714 metric answers related to 28 financial metrics.</p>
Dataset of FEUTURE Online Paper No. 9 "The Financial Flows and the Future of EU-Turkey Relations"
<p>The dataset provides the annual GDP (in US Dollar) and the annaul GDP growth (in %) of Turkey between 1960 and 2016.</p>
Geo-referenced Harmonized Financial Data on Soil Defense Public Works in Italy
<p>The dataset collects financial data about public works in Italy, specifically, it focuses on soil defense investments. The data is sourced from three distinct platforms: the OpenCoesione website, the OpenBDAP database, the Ministry of Economy and Finance's open data platform, and the ReNDiS database, provided by ISPRA, that exclusively gathers information about interventions in soil defense. The data obtained is interconnected using unique project codes (CUP) to prevent duplication.</p> <p>Georeferencing involves integrating geographic references into the three datasets. It enhances the accuracy of spatial analyses of spatial defense investments and provides valuable context for understanding the geographical distribution of available financial data. By incorporating geographic references such as regions, provinces, and municipalities analysts can gain insights into the spatial patterns and relationships within the datasets. This step is crucial for effective decision-making and policy formulation in the field of soil defense investments.</p> <p>Geographical references for each project were integrated using codes and names of regions, provinces, and municipalities from the ISPRA database. This database retrieves information directly from ISTAT websites, ensuring constant updates to names and codes, thus enhancing the accuracy of spatial analyses.</p> <p>Furthermore, geographical codes facilitated the association of centroids coordinates and polygon shapes for each financial observation, enhancing spatial visualization and analysis of soil defense investments, empowering decision-makers with a deeper understanding of the geographic distribution and impact of these initiatives. This comprehensive approach allows for a deeper exploration of the geographical factors influencing soil defense investments, including identifying hotspots of activity, assessing spatial trends, and understanding the localized impact of interventions on environmental sustainability and community resilience.</p> <p>The zip folder comprises four subfolders and two files. Among the files, one is a text file containing metadata, while the other is a CSV file consolidating merged data at the national level from three repositories. The subfolders contain data categorized by region and data categorized by region sourced from the three distinct repositories.</p> <p> Datasets present 28 variables: </p> <ul> <li>Columns 1-2: descriptive variables;</li> <li>Column 3: total amount financed for each intervention;</li> <li>Columns 4-9: geo-reference variables;</li> <li>Columnn 10:25: key dates of the public works process;</li> <li>Column 26: source of the data;</li> <li>Columns 27-28: geo-referencing (centroids and areal shape).</li> </ul> <p>An additional dataset has been added comprising all Italian municipalities, including thos that lack information on soil defense investments. In such a way, there are geographical information regarding all the peninsula. </p>
Synthetic, realistic vehicular traces for financial district of Quito using SUMO
<p>These files present the map of the financial district of Quito simulated in SUMO. The contributions are.</p> <ul> <li>Careful validation of the imported maps from OpenStreetMaps (imported in November 2019) including time intervals in traffic lights, location of traffic lights, suppression on non-existing junctions, edges, etc.</li> <li>Simulation of realistic number of vehicles considering the statistics from traffic authority of Quito</li> <li>Configuration of the generation tools provided in the SUMO package. We used all the meaningful configuration for each tool to obtain synthetic realistic vehicular trace</li> </ul> <p>In addition, we include the scripts and program to analyze vehicular traces from the point of view of vehicular communications. The scripts were developed using awk, bash and R. The software to compute connectivity matrix was developed with C++</p>
Dataset from 10-K financial reports Netflix 2011 - 2022
<p>Dataset from Netflix's 10-K annual reports, which include externally audited data about financial activities of businesses based in the US. For a description of the data compiled see the .docx document. The code included was used in the following research:</p><p>Title: Evidence of diseconomies of scale in subscription-based video on demand services.<br><br>Abstract: This study provides evidence of diseconomies of scale in Netflix, a major subscription-based video on demand (SVOD) service provider. This contradicts the common belief in prevalent economies of scale for such e-businesses. We, however, rely on a comprehensive analysis of a dataset where we have collected and combined publicly available and audited financial data, mostly coming from Netflix's 10-K reports. In our analysis we employ several user-cost models, namely a baseline linear model, a power law model, an exponential model, and a logarithmic model. Such models often appear (in different variations) in economics literature, but are almost inexistent in the rhetoric around SVOD business models. Corroborating the applications of all these mathematical models on the financial data of Netflix identifies a super-linear increase in costs with expanding user basis, indicating the rising per-user costs that defines diseconomies of scale. These findings provide critical insights into SVOD service scalability, challenging prevailing assumptions and informing expectations about cost dynamics in this industry.</p>
Can Digital Currencies End Financial Exclusion in Indonesia? Economic Realities and Policy Ambitions
<p>Digitalisation is driving Indonesia’s economic development. The empowerment<br> of an efficient system of digital payments is a key underlying factor for the<br> establishment of an inclusive and well-developed digital economy. As a first<br> and fundamental step, the Bank of Indonesia launched the “National Cashless<br> Movement” (GNNT) in 2014. This vision was further advanced with the “Indonesia<br> Payment System Blueprint 2025” (IPS), which aimed at boosting digitalisation in<br> the banking industry through open banking and technology developments. As a<br> result, Indonesia’s digital economy and finance is recording remarkable upward<br> trends – such as an increase of 36.9 per cent in e-commerce and 52.6 per cent in<br> fintech lending transactions between 2020 and 2021. Similarly, cashless payments<br> are experiencing a spectacular growth. The usage of the Quick Response Indonesia<br> Standard (QRIS) system, which enhances cashless payment, has doubled. Credit<br> card transactions and e-money usage increased by 20 per cent and 51.6 per cent,<br> respectively, in the same two-year period.</p> <p>In this context of vibrant, quick transformations, a diverse ecosystem – which<br> comprises incumbent financial institutions, start-ups and technology companies<br> – is trying to advance Indonesia’s digital payments while targeting the goal of<br> financial inclusion. The rise of digital payment solutions indeed provides a unique<br> window of opportunity to tackle the current 92 million unbanked Indonesians<br> and 62 million small and medium-sized enterprises (SMEs) that are excluded from<br> the formal economy in Indonesia.</p>
Dataset: HBT Financial, Inc. (HBT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Hanmi Financial Corporation (HAFC) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Fulton Financial Corporation (FULT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Fulton Financial Corporation (FULTP) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: First Savings Financial Group, Inc. (FSFG) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Primis Financial Corp. (FRST) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Franklin Financial Services Corporation (FRAF) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: First Financial Northwest, Inc. (FFNW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: First Financial Bancorp. (FFBC) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Flushing Financial Corporation (FFIC) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Financial Institutions, Inc. (FISI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.