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Thomson Reuters reported CAD122.65B in Market Capitalization this July of 2025, considering the latest stock price and the number of outstanding shares.Data for Thomson Reuters | TRI - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Thomson Reuters stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

[Keywords] Market include Qlik, Wolters Kluver, H.I.G. Capital, Datasift, TransUnion

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The Business Information Services market is experiencing robust growth, driven by the increasing need for data-driven decision-making across various industries. The market, estimated at $500 billion in 2025, is projected to maintain a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033, reaching approximately $850 billion by 2033. This expansion is fueled by several key factors, including the rising adoption of cloud-based solutions, the proliferation of big data analytics, and the growing demand for real-time insights. Major players like Bloomberg, Dow Jones, and Thomson Reuters are leveraging advanced technologies like artificial intelligence and machine learning to enhance their offerings and cater to the evolving needs of their clientele. Furthermore, the increasing focus on regulatory compliance across sectors is boosting the demand for comprehensive and reliable business information services. Segmentation within the market includes financial information, market research, credit information, and legal information, each contributing to the overall growth trajectory. However, the market faces certain challenges. Data security concerns and the rising costs associated with data acquisition and maintenance present hurdles for both providers and consumers. Competition is fierce, with established players constantly innovating to maintain their market share and new entrants striving to carve out a niche. The varying regulatory landscapes across different regions also influence market dynamics. Despite these challenges, the long-term outlook for the Business Information Services market remains positive, driven by the continued digital transformation across industries and the ever-increasing reliance on data for strategic decision-making. The market's expansion will likely be propelled by the growing adoption of subscription-based models and the increasing integration of business information services with other enterprise software solutions.

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The business information market, valued at $199,690 million in 2025, is projected to experience robust growth, driven by increasing demand for data-driven decision-making across diverse sectors. A compound annual growth rate (CAGR) of 7% from 2025 to 2033 indicates a significant expansion, fueled by several key factors. The rise of big data analytics and the need for comprehensive market intelligence are paramount drivers. Businesses are increasingly relying on sophisticated data analysis to gain competitive advantages, optimize operations, and make informed strategic choices. Furthermore, the growing adoption of cloud-based solutions and advanced technologies like AI and machine learning is streamlining data access and analysis, contributing to market growth. Regulatory changes mandating greater transparency and compliance also stimulate demand for robust business information services. Major players like Bloomberg, Dow Jones, and Thomson Reuters are consolidating their market positions through strategic acquisitions and technological advancements, fostering competition and innovation within the sector. While data privacy concerns and the potential for information overload could act as restraints, the overall market outlook remains positive, indicating substantial growth opportunities in the coming years. The segment breakdown, while not explicitly provided, can be reasonably inferred. The market likely encompasses various sub-segments, including financial information, industry reports, company profiles, and risk management solutions. The competitive landscape is characterized by established players offering diverse products and services, with a strong emphasis on providing accurate, timely, and insightful information. Geographic variations in growth are likely, with developed economies like North America and Europe exhibiting strong demand, while emerging markets show increasing potential. The forecast period of 2025-2033 offers a promising window for market expansion, driven by the continuous evolution of data analytics capabilities and the persistent need for effective business intelligence across all sectors.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

TRI Thomson Reuters Corp Ordinary Shares

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Thomson Reuters shares are predicted to continue their upward trend, driven by strong demand for its financial and legal information services. Analysts predict a moderate level of risk associated with this investment due to potential fluctuations in the financial markets and increased competition, but overall, the long-term outlook for the company remains positive.

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The B2B information services market, valued at $269.4 million in 2025, is experiencing robust growth. While the provided CAGR is missing, considering the substantial presence of established players like Bloomberg, Thomson Reuters, and Wolters Kluwer, coupled with the increasing reliance on data-driven decision-making across various sectors (finance, energy, healthcare, legal), a conservative estimate of a 7% CAGR over the forecast period (2025-2033) seems reasonable. This growth trajectory reflects the market's inherent value in providing crucial insights and competitive intelligence to businesses. The segmentation across professional publishing, joint information, and consultation services, further diversified by industry application, indicates the market's adaptability and its potential to cater to specialized information needs across different sectors. North America and Europe are expected to remain dominant regions, although growth in the Asia-Pacific region, fueled by expanding economies and increasing digital adoption, presents significant opportunities. The market's expansion is driven by factors such as increased regulatory compliance needs, the rise of data analytics, and the growing demand for real-time information. However, challenges remain. The market faces competitive pressures from both established players and emerging fintech companies offering niche solutions. Data security concerns and the need to maintain data accuracy are also crucial factors. Furthermore, maintaining subscription models and navigating the evolving information landscape, impacted by rapidly changing technological advancements, present ongoing strategic hurdles. Despite these challenges, the continued expansion of digital platforms and increasing data volumes suggests a positive outlook for the B2B information services market, promising consistent growth over the next decade. The diverse application across industries and the inherent value proposition of reliable, timely information assures continued demand for sophisticated and specialized B2B information services.

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The B2B Information Services market is experiencing robust growth, with a market size of $106 million in 2025 and a projected Compound Annual Growth Rate (CAGR) of 14.3% from 2025 to 2033. This expansion is fueled by several key drivers. Firstly, the increasing reliance of businesses on data-driven decision-making across diverse sectors like finance, energy, healthcare, and legal, necessitates access to high-quality, reliable information. Secondly, advancements in technology, particularly in data analytics and artificial intelligence, are enabling the creation of more sophisticated and insightful information products and services. Furthermore, the growing complexity of regulatory environments and the need for risk mitigation are driving demand for specialized B2B information services. Segmentation within the market reveals strong performance across Professional Publishing, Joint Information, and Consultation Services, with Finance, Energy, and Medical & Healthcare applications leading the way. Key players like Bloomberg, Thomson Reuters, and Wolters Kluwer are well-positioned to capitalize on these trends, leveraging their extensive data networks and established brand reputations. Competitive pressures are intense, however, requiring continuous innovation in data delivery methods and service offerings. Geographic expansion, particularly within North America and Europe, presents significant opportunities, while emerging markets in Asia-Pacific show promising, albeit more volatile, growth potential. The forecast period of 2025-2033 anticipates continued market expansion, driven by ongoing technological advancements and the increasing digital transformation of businesses globally. However, potential restraints include the rising cost of data acquisition and processing, increasing competition from new entrants offering niche services, and the need for continuous investment in cybersecurity and data privacy measures. Successful companies will need to adapt to evolving customer demands by offering personalized, value-added services and leveraging data analytics to deliver actionable insights. This will necessitate strategic partnerships, mergers, and acquisitions to enhance market reach and service offerings. Focus on data quality, security, and compliance will remain crucial for maintaining trust and achieving long-term success within this competitive landscape.

1. The series of files named as ‘*_polarity.csv’ in folder ‘polarity’ includes the trading polarities of stocks listed on Shenzhen Stock Exchange from May 4 to July 31 2015. The eight numbers in the filenames specify the dates. The columns of these dataframes indicate the stock names, while the indices of dataframes indicate the time. The granularity of trading polarity is 1 minute for every stock. These trading polarities are calculated from the serial numbers for buyers and sellers in transactions data. The original transactions data is not publicly available due to the company’s license requirement.2. The files in the 'log_ret' folder cover the log returns of 1646 stocks listed on Shenzhen Stock Exchange from May 4 to July 31 2015. These data are calculated from the intraday price trends data provided by Thomson Reuters’ Tick History. The original price trends data is not publicly available due to the company’s license requirement.3. The file named as "stock_market_value.csv" gives the capitalization of stocks in June 31 2015, which is downloaded from Wind Information and we have converted the unit of measure from RMB into a dollar. Due to license requirements of the data companies, all of the above files have converted the names of stocks into integers in a consistent way. 4. Please cite the following paper:Shan Lu, Jichang Zhao and Huiwen Wang. Trading Imbalance in Chinese Stock Market—A High-Frequency View. Entropy, 2020, 22(8), 897.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Thorn in Reuters' (TRI) Side?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

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 B2B information services market, valued at $118 million in 2025, is experiencing robust growth, projected to expand at a compound annual growth rate (CAGR) of 14.9% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing reliance on data-driven decision-making across various industries, including finance, energy, healthcare, and legal sectors, is a primary catalyst. Businesses are increasingly recognizing the value of accurate, timely, and actionable intelligence to gain a competitive edge, optimize operations, and mitigate risks. Furthermore, the ongoing digital transformation and the proliferation of big data are creating significant opportunities for providers of sophisticated analytics and information solutions. The market is segmented by application (finance, energy, medical & healthcare, legal & tax, others) and type (professional publishing, joint information, consultation services), reflecting the diverse needs of different business sectors. North America currently holds a significant market share, driven by the presence of major players and high technological adoption rates. However, rapidly developing economies in Asia-Pacific, particularly China and India, are expected to witness substantial growth in the coming years, contributing significantly to the overall market expansion. The competitive landscape is characterized by both established industry giants like Bloomberg, Thomson Reuters, and Wolters Kluwer, and specialized niche players offering targeted solutions. Competitive pressures will likely increase with technological advancements and the emergence of innovative business models. The sustained growth trajectory of the B2B information services market is anticipated to continue throughout the forecast period. This is predicated on several factors. Firstly, the increasing sophistication of data analytics capabilities is empowering businesses to leverage information more effectively. Secondly, regulatory changes and compliance requirements across various sectors are driving demand for specialized information services. Thirdly, the growing adoption of subscription-based models is contributing to predictable and recurring revenue streams for providers. Finally, mergers and acquisitions within the industry are further consolidating market share and fostering innovation. Despite these positive trends, challenges remain, including managing data security concerns, ensuring data accuracy and reliability, and adapting to the evolving technological landscape. Strategic partnerships and technological advancements will be crucial in addressing these challenges and maintaining the market's robust growth.

The global Business Information market, valued at $176.68 billion in 2025, is projected to experience robust growth, driven by the increasing reliance on data-driven decision-making across diverse sectors. A Compound Annual Growth Rate (CAGR) of 6.79% from 2025 to 2033 indicates a significant expansion of the market, reaching an estimated $300 billion by 2033. Key drivers include the rising adoption of advanced analytics, the growing need for real-time data insights, and the increasing regulatory compliance requirements across industries like BFSI (Banking, Financial Services, and Insurance), healthcare, and manufacturing. The BFSI sector remains a dominant end-user, leveraging business information for risk assessment, fraud detection, and customer relationship management. The healthcare and life sciences sector utilizes this data for drug discovery, clinical trials, and patient care optimization. Technological advancements, such as artificial intelligence (AI) and machine learning (ML), are further fueling market expansion by enabling more sophisticated data analysis and predictive capabilities. However, challenges such as data security concerns, the rising cost of data acquisition and processing, and the complexity of integrating disparate data sources represent potential restraints. The market is highly competitive, with established players like Bloomberg LP, Thomson Reuters, and S&P Global dominating. These companies employ various competitive strategies, including mergers and acquisitions, product innovation, and strategic partnerships, to maintain market share. Regional growth is expected to be diverse, with North America and Europe maintaining significant market share due to the presence of key players and developed data infrastructure. However, rapidly developing economies in Asia-Pacific, particularly China and India, are poised for substantial growth in the coming years. The competitive landscape is shaped by factors such as technological innovation, data accuracy and reliability, and the ability to provide comprehensive and customized solutions. Companies are continuously investing in research and development to enhance their product offerings and improve data analytics capabilities. This dynamic competitive environment is driving market innovation and expansion, benefiting both businesses and consumers alike. Strategic alliances and partnerships are also becoming increasingly important, enabling companies to expand their reach and offer more comprehensive services to clients. The long-term outlook for the Business Information market remains positive, with continued growth driven by the ever-increasing demand for actionable intelligence in a globalized and data-driven world. However, navigating the complexities of data security, privacy, and ethical considerations will be crucial for sustained success in this market.