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

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: 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.

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).

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.