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Stock return prediction with tweets

Stock return prediction with tweets

Stefan
Open Dataset
Publication Date: 2020
Creators: Madhyastha, Pranava; Sowinska, Karolina

Our dataset comprises of two parts, the twitter (social media) based textual information as samples and the stock return information as labels.

Future of Business - Survey Results

Future of Business - Survey Results

Stefan
Open Dataset
Publication Date: 2018
Creators: Facebook; OECD; World Bank

The Future of Business survey is a collaboration between Facebook, the OECD and the World Bank to provide timely insights on the perceptions, challenges, and outlook of online Small and Medium Enterprises (SMEs). The Future of Business survey was first launched as a monthly survey in 17 countries in February 2016 and expanded to 42 countries in 2018. In 2019, the Future of Business survey increased coverage to 97 countries and moved to a bi-annual cadence.

The target population consists of SMEs that have an active Facebook business Page and include both newer and longer-standing businesses, spanning across a variety of sectors. To date, more than 90 million SMEs have created a Facebook Page, and more than 700,000 of these Facebook Page owners have taken the survey. With more businesses leveraging online tools each day, the survey provides a lens into a new mobilized, digital economy and, in particular, insights on the actors: a relatively unmeasured community worthy of deeper consideration and considerable policy interest.

The survey includes questions about perceptions of current and future economic activity, challenges, business characteristics and strategy. Custom modules include questions related to regulation, access to finance, digital payments, and digital skills.

Relative Weatlh Index Data

Relative Weatlh Index Data

Stefan
Open Dataset
Publication Date: 2021
Creators: Chi, Guanghua; Fang, Han; Chatterjee, Sourav; Blumenstock, Joshua E.
The Relative Wealth Index predicts the relative standard of living within countries using de-identified connectivity data, satellite imagery and other nontraditional data sources. It has been built by researchers at the University of Carlifornia – Berkeley and Facebook. The estimates are built by applying machine learning algorithms to vast and heterogeneous data from satellites, mobile phone networks, topographic maps, as well as aggregated and de-identified connectivity data from Facebook. They train and calibrate the estimates using nationally-representative household survey 20 data from 56 LMICs, then validate their accuracy using four independent sources of household survey data from 18 countries. They also provide confidence intervals for each micro-estimate to facilitate responsible downstream use. The data is provided for 93 low and middle-income countries at 2.4km resolution. It covers the time between April 01, 2021 and December 22, 2023.An interactive map of the Relative Wealth Index is available here: http://beta.povertymaps.net/

Please cite / attribute any use of this dataset using the following: Microestimates of wealth for all low- and middle-income countries Guanghua Chi, Han Fang, Sourav Chatterjee, Joshua E. Blumenstock Proceedings of the National Academy of Sciences Jan 2022, 119 (3) e2113658119; DOI: 10.1073/pnas.2113658119

 

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