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38 results for “financial data”
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>
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>
Figure 2.Flow Chart for Data preprocessing & Training-Comparative study of Financial Time Series Prediction by Artificial Neural Network with Gradient Descent Learning
<p>Methodology<br> This paper develops an ANN based comparative predictive model for NASDAQ stock<br> prediction. The first ANN model is developed with Multi-Layer Feed forward Network<br> Architecture & the second model is developed with Recurrent Neural Network Architecture. In this<br> paper gradient descent based back propagation learning algorithm is used for the supervised<br> learning of the predictive network.</p>
Data Materials for Fortune Telling Studies: Positive Fortune Telling Enhances Men's Financial Risk Taking
<p>We share the data materials for three studies and a meta-analysis on the effects of positive fortune telling on enhancing men's financial risk taking. A paper entitled as "<strong>Positive Fortune Telling Enhances Men’s Financial Risk Taking</strong>" was written up for publication.</p>
Supporting data for "A service to help insurers understand the financial impacts of changing flood risk in Europe, based on PESETA IV"
<p>Supporting data for the paper "A service to help insurers understand the financial impacts of changing flood risk in Europe, based on PESETA IV".</p> <p><a href="https://doi.org/10.1016/j.cliser.2023.100395">https://doi.org/10.1016/j.cliser.2023.100395</a></p>
Search Behavior can Affect Financial Decision Results: A Behavior Study of Google Trends Data and Linguistic Scale
As search engines have become the main information resources of our daily life, studies about search behavior on the internet have gained great popularity with the growing knowledge of how the search behavior itself can affect our daily decisions, e.g. what to purchase, where to travel and even how to define beauty. However, there is no consensus conclusion whether the search behavior itself or the linguistic meaning behind it that can affect their decision. After analyzing the linguistic meanings of 13,915 English words obtained from Google Trends and its profit gained from the US house market by automatic transactions. It is found that linguistic meanings can affect financial decision results as word clusters with supervised machine learning methods.
Data from: Community Credit Photovoice project on trust in consumer financial services
<p>The Community Credit research project explores pathways for trusted collaboration between credit unions and the communities they serve. To understand the experiences of people historically underserved by the consumer financial services industry, we focused in particular on the lived experience of low-income residents in Southern California. As part of a larger, mixed-methods study, in 2022 we conducted a five-week photovoice workshop exploring participants' economic lives and experiences with consumer financial services. Photovoice is a community-based participatory research method in which participants narrate and analyze their lived experiences using photography. This data set contains the transcripts from the photovoice sessions after excluding any personally identifying data.</p> <p>All study materials and procedures were approved by the University of California, Irvine Office of Human Research Protections and the Institutional Review Board (protocol ID 20216839). This material is based upon work supported by the National Science Foundation under Grant No. 2137567. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.</p>
Data from: Community Credit interviews on trust in consumer financial services
<p>The Community Credit research project explores pathways for trusted collaboration between credit unions and the communities they serve. To understand the experiences of people historically underserved by the consumer financial services industry, we focused in particular on the lived experience of low-income residents in Southern California. As part of a larger, mixed-methods study, in 2022 we conducted 30 semi-structured interviews exploring people's everyday financial practices and their attitudes toward consumer financial services providers. This data set contains the transcripts from the interviews after excluding any personally identifying data.</p> <p>All study materials and procedures were approved by the University of California, Irvine Office of Human Research Protections and the Institutional Review Board (protocol ID 20216839). This material is based upon work supported by the National Science Foundation under Grant No. 2137567. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.</p>
Data of paper entitled "The Impact of Financial Influencers, Social Influencers, and FOMO Economy on the Decision-Making of Investment on Millennial Generation and Gen Z of Indonesia"
<p>The repository contains:</p> <ol> <li>Result of inner model of PLS</li> <li>Result of outer model of PLS</li> <li>Questionnaire result</li> </ol>
Cashtag Piggybacking dataset - Twitter dataset enriched with financial data
<p>This dataset is composed of </p> <ul> <li>Twitter dataset of ~9M tweets mentioning stocks (cashtags) traded on the most important US markets, shared between May and September 2017 (users data enriched with bot classification label)</li> <li>Financial information about ~30k companies found in those tweets, retrieved from Google Finance</li> </ul> <p>Refer to the paper below for more details.</p> <p>Cresci, S., Lillo, F., Regoli, D., Tardelli, S., & Tesconi, M. (2019). Cashtag Piggybacking: Uncovering Spam and Bot Activity in Stock Microblogs on Twitter. <em>ACM Transactions on the Web (TWEB)</em>, <em>13</em>(2), 11.</p>
Data from: Community Credit survey on trust in consumer financial services
<p class="MsoNormal">The Community Credit research project explores pathways for trusted collaboration between credit unions and the communities they serve. To understand the experiences of people historically underserved by the consumer financial services industry, we focused in particular on the lived experience of low-income residents in Southern California. As part of a larger, mixed-methods study, in 2022 we conducted an online survey investigating people's everyday financial practices, evolving perceptions of trust and risk, and their unmet financial needs. The general population survey data was collected between April 15 and April 22, 2022. The credit union data was collected between May 3 and July 18, 2022. This data set contains the responses of the survey participants after excluding any personally identifying data.</p> <p class="MsoNormal">All study materials and procedures were approved by the University of California, Irvine Office of Human Research Protections and the Institutional Review Board (protocol ID 20216839). This material is based upon work supported by the National Science Foundation under Grant No. 2137567. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.</p>
Data from: Community Credit mapping of trust in consumer financial services
<p class="MsoNormal">The Community Credit research project explores pathways for trusted collaboration between credit unions and the communities they serve. To understand the experiences of people historically underserved by the consumer financial services industry, we focused in particular on the lived experience of low-income residents in Southern California. As part of a larger, mixed-methods study, in 2022 we mapped the landscape of financial services providers and advertisements in low-income neighborhoods in Orange County. Through documenting the presence of alternative financial services (AFS) providers and fringe financial advertisements, alongside traditional financial services providers, we investigated the spatial relationship between these businesses, as well as the factors that create consumers' sense of (dis)trust in them. This data set contains photographs taken as part of this mapping research.</p> <p class="MsoNormal">All study materials and procedures were approved by the University of California, Irvine Office of Human Research Protections and the Institutional Review Board (protocol ID 20216839). This material is based upon work supported by the National Science Foundation under Grant No. 2137567. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.</p>
Search Behavior can Affect Financial Decision Results: A Behavior Study of Google Trends Data and Linguistic Scale
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Data from: Community Credit Photovoice project on trust in consumer financial services
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Data from: Community Credit mapping of trust in consumer financial services
Open the record for dataset details and reuse information.
Data from: Community Credit interviews on trust in consumer financial services
Open the record for dataset details and reuse information.
Data from: Community Credit survey on trust in consumer financial services
Open the record for dataset details and reuse information.
Data set (stata format) + do file for : Risk misperceptions of structured financial products with worst-of payout characteristics revisited
<p>This file contains the data set (in stata format) and do file associated with the paper entitled "Risk misperceptions of structured financial products with worst-of payout characteristics revisited"</p>
Data and code for "Financial incentives for vaccination do not have negative unintended consequences"
<p>Data and code for "Financial incentives for vaccination do not have negative unintended consequences"</p> <p><a href="https://www.nature.com/articles/s41586-022-05512-4">https://www.nature.com/articles/s41586-022-05512-4</a></p>
ScienceDex guides
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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.