6 datasets found
  1. Worldscope Fundamentals

    • lseg.com
    Updated May 13, 2025
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    LSEG (2025). Worldscope Fundamentals [Dataset]. https://www.lseg.com/en/data-analytics/financial-data/company-data/fundamentals-data/worldscope-fundamentals
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    csv,html,json,pdf,sql,string formatAvailable download formats
    Dataset updated
    May 13, 2025
    Dataset provided by
    London Stock Exchange Grouphttp://www.londonstockexchangegroup.com/
    Authors
    LSEG
    License

    https://www.lseg.com/en/policies/website-disclaimerhttps://www.lseg.com/en/policies/website-disclaimer

    Description

    Compare financial information of companies from different industries around the globe with Worldscope Fundamentals, providing essential insights and analysis.

  2. r

    Financial time series data for 22 distinct equity markets in developed...

    • researchdata.edu.au
    Updated Apr 27, 2017
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    Alexeev, Vitali (2017). Financial time series data for 22 distinct equity markets in developed countries for 70 000 stocks over 42 years [Dataset]. https://researchdata.edu.au/927329/927329
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    Dataset updated
    Apr 27, 2017
    Dataset provided by
    University of Tasmania, Australia
    Authors
    Alexeev, Vitali
    Description

    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

  3. I/B/E/S Estimates | Company Data

    • lseg.com
    Updated Jun 2, 2025
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    LSEG (2025). I/B/E/S Estimates | Company Data [Dataset]. https://www.lseg.com/en/data-analytics/financial-data/company-data/ibes-estimates
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    csv,html,json,pdf,python,sql,text,user interface,xmlAvailable download formats
    Dataset updated
    Jun 2, 2025
    Dataset provided by
    London Stock Exchange Grouphttp://www.londonstockexchangegroup.com/
    Authors
    LSEG
    License

    https://www.lseg.com/en/policies/website-disclaimerhttps://www.lseg.com/en/policies/website-disclaimer

    Description

    Browse LSEG's I/B/E/S Estimates, discover our range of data, indices & benchmarks. Our Data Catalogue offers unrivalled data and delivery mechanisms.

  4. o

    Data from: Liquidity, time-varying betas and anomalies. Is the high trading...

    • explore.openaire.eu
    • data.mendeley.com
    Updated Nov 19, 2019
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    Paper Authors Paper Authors (2019). Liquidity, time-varying betas and anomalies. Is the high trading activity enhancing the validity of the CAPM in the UK equity market? [Dataset]. http://doi.org/10.17632/56n2yxgpcf.1
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    Dataset updated
    Nov 19, 2019
    Authors
    Paper Authors Paper Authors
    Area covered
    United Kingdom
    Description

    Using all stocks listed in the London Stock Exchange for the period from January 1989 to December 2018, the dataset comprises the following series: 1. Annual returns for 20 asset growth portfolios, following Fama and French (1993) methodology. 2. Annual returns for 25 portfolios size-book to market equity, following Fama and French (1993) methodology. 3. Annual returns for 62 industry portfolios, using two-digit SIC codes. 4. Fama and French (1993) factors for their three-factor model (RM, SMB and HML). 5. Fama and French (2015) factors for their five-factor model (RM, SMB, HML, RMW, and CMA). 6. Variation of the Amihid illiquidy measure for the London Stock Exchange, following Amihud (2002) methodology. 7. Three-month interest rate of the Treasury Bill for the United Kingdom, as provided by the OECD database. 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.

  5. f

    Datasets for the Role of Financial Investors in Commodity Futures Risk...

    • figshare.com
    application/x-rar
    Updated Dec 6, 2019
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    Mohammad Isleimeyyeh (2019). Datasets for the Role of Financial Investors in Commodity Futures Risk Premium [Dataset]. http://doi.org/10.6084/m9.figshare.9334793.v2
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    application/x-rarAvailable download formats
    Dataset updated
    Dec 6, 2019
    Dataset provided by
    figshare
    Authors
    Mohammad Isleimeyyeh
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    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).

  6. O

    Online Trade Management Software Report

    • datainsightsmarket.com
    doc, pdf, ppt
    Updated Jun 3, 2025
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    Data Insights Market (2025). Online Trade Management Software Report [Dataset]. https://www.datainsightsmarket.com/reports/online-trade-management-software-1934175
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    doc, pdf, pptAvailable download formats
    Dataset updated
    Jun 3, 2025
    Dataset authored and provided by
    Data Insights Market
    License

    https://www.datainsightsmarket.com/privacy-policyhttps://www.datainsightsmarket.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global online trade management software market is experiencing robust growth, driven by the increasing need for efficient and streamlined international trade processes. The market, estimated at $5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $12 billion by 2033. This expansion is fueled by several key factors, including the rising adoption of cloud-based solutions, the growing complexity of global trade regulations, and the increasing demand for real-time visibility into supply chains. Businesses are seeking software solutions to automate tasks such as customs compliance, documentation management, and transportation tracking, leading to reduced operational costs and improved efficiency. The market is witnessing a shift towards integrated platforms that offer comprehensive functionalities, consolidating various trade management processes into a single system. This trend is encouraging businesses to adopt advanced analytics capabilities for better decision-making and risk mitigation. The competitive landscape is fragmented, with numerous established players and emerging vendors vying for market share. Key players like Amber Road, SAP SE, Thomson Reuters, and Oracle are leveraging their existing enterprise resource planning (ERP) and supply chain management (SCM) solutions to expand their online trade management software offerings. The growth of e-commerce and cross-border trade is further bolstering market demand, especially in regions like North America and Asia-Pacific. While the market faces challenges such as high implementation costs and the need for specialized expertise, the overall growth trajectory remains positive, driven by the aforementioned factors and the increasing digitalization of global trade processes. The market segmentation is likely driven by deployment mode (cloud vs. on-premise), industry vertical, and geographic region, although further data would refine this understanding.

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LSEG (2025). Worldscope Fundamentals [Dataset]. https://www.lseg.com/en/data-analytics/financial-data/company-data/fundamentals-data/worldscope-fundamentals
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Worldscope Fundamentals

Explore at:
csv,html,json,pdf,sql,string formatAvailable download formats
Dataset updated
May 13, 2025
Dataset provided by
London Stock Exchange Grouphttp://www.londonstockexchangegroup.com/
Authors
LSEG
License

https://www.lseg.com/en/policies/website-disclaimerhttps://www.lseg.com/en/policies/website-disclaimer

Description

Compare financial information of companies from different industries around the globe with Worldscope Fundamentals, providing essential insights and analysis.

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