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Data collected from Datastream, a proprietary commercial database containing financial data, published by Thomson Reuters. The dataset consists of fundamental stock data; return, price, unadjusted price, in two frequencies: annual and daily. Daily set contains price index, return index, unadjusted price, the annual set contains stock fundamentals, time series data and static data such as geographical location and others. The data is used for research purposes, but also for teaching in the school of economics and finance and for staff training
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Using all stocks listed in the London Stock Exchange for the period from January 1989 to December 2018, the dataset comprises the following series:
We have produced these series using the following data from Thomson Reuters Datastream: (i) total return index (RI series), (ii) market value (MV series), (iii) market-to-book equity (PTBV series), (iv) total assets (WC02999 series), (v) return on equity (WC08301 series), (vi) tax rate (WC08346 series), (vii) primary SIC codes, (viii) turnover by volume (VO series), and (ix) the market price (P series). Following Griffin et al. (2010), we use the generic rules provided by the authors for excluding non-common equity securities from Datastream data.
REFERENCES: Amihud, Y. (2002). Illiquidity and stock returns: Cross-section and time-series effects. Journal of Financial Markets, 5, 31–56. Fama, E. F. and French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3–56. Fama, E. F. and French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116, 1–22. Griffin, J. M., Kelly, P., and Nardari, F. (2010). Do market efficiency measures yield correct inferences? A comparison of developed and emerging markets. Review of Financial Studies, 23, 3225–3277.
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
The datasets for the Role of Financial Investors on Commodity Futures Risk Premium are weekly datasets for the period from 1995 to 2015 for three commodities in the energy market: crude oil (WTI), heating oil, and natural gas. These datasets contain futures prices for different maturities, open interest positions for each commodity (long and short open interest positions), and S&P 500 composite index. The selected commodities are traded on the New York Mercantile Exchange (NYMEX). The data comes from the Thomson Reuters Datastream and from the Commodity Futures Trading Commission (CFTC).
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Consensus Economics is a world-leading international economic survey organisation, gaining forecasts and views from economists. View the data through LSEG.
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BASE YEAR | 2024 |
HISTORICAL DATA | 2019 - 2024 |
REPORT COVERAGE | Revenue Forecast, Competitive Landscape, Growth Factors, and Trends |
MARKET SIZE 2023 | 23.39(USD Billion) |
MARKET SIZE 2024 | 25.48(USD Billion) |
MARKET SIZE 2032 | 50.61(USD Billion) |
SEGMENTS COVERED | Deployment Model ,Type ,Application ,Data Source ,Industry Vertical ,Regional |
COUNTRIES COVERED | North America, Europe, APAC, South America, MEA |
KEY MARKET DYNAMICS | Rising data volumes Growing demand for realtime data Increasing adoption of cloudbased platforms Need for data governance and compliance Emergence of artificial intelligence and machine learning |
MARKET FORECAST UNITS | USD Billion |
KEY COMPANIES PROFILED | Morningstar, Inc. ,Bloomberg L.P. ,FactSet ,S&P Global Market Intelligence ,YCharts, Inc. ,IHS Markit Ltd. ,Refinitiv ,RavenPack ,AlphaSense, Inc. ,Datastream Group Limited ,Thomson Reuters Corporation ,Sentieo ,Visible Alpha LLC ,Six Financial Information |
MARKET FORECAST PERIOD | 2025 - 2032 |
KEY MARKET OPPORTUNITIES | 1 Growing demand for realtime data 2 Expansion into emerging markets 3 Integration with AI and ML 4 Cloudbased deployment models 5 Increasing regulatory compliance |
COMPOUND ANNUAL GROWTH RATE (CAGR) | 8.95% (2025 - 2032) |
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Compare financial information of companies from different industries around the globe with Worldscope Fundamentals, providing essential insights and analysis.