Data that moves the markets
Combining Machine Readable News, Real-Time –Optimized and Tick History for the fullest picture

As the exclusive provider of Reuters news to the global financial marketplace, LSEG’s news service gives you access to Reuters’ marketmoving headlines, together with agenda-setting insight and commentary. With a focus on speed of delivery, providing stories as theybreak, you gain the advantage of accessing the news that matters, every day, for every major market sector. With its 170-year history andreputation for trust and accuracy, Reuters’ 2,000+ journalists, on the ground all over the world, are at the heart of our news service.
In turn, LSEG transforms all of the unstructured, real-time news from Reuters and multiple other news sources into a machine readable
feed. Our news analytics engine scores every story for sentiment, relevance and novelty, to better inform your research, trading andcompliance workflows. Combined with our real-time and historical market data solutions, Real-Time – Optimized and Tick History, you canbetter identify connections between events and market movements. Whether it’s conducting market research, calculating risk, monitoring
the markets or backtesting your trading strategies, these solutions complement each other to enable you to make decisions faster, withgreater ease and confidence.

Machine Readable News and Real-Time market data
Real-Time – Optimized, our powerful cloud-based data feeds solution, provides access to 90 million
instruments and exclusive datasets that are cleaned and standardised so teams can use the data
immediately. With Reuters’ global news coverage, and our ability to analyse and scan news across 35,000
companies in real time, you can research a large universe of companies and take advantage of trading
opportunities anywhere in the world, empowering you to uncover actionable insights for your strategies.

Machine Readable News and Historical market data
Our historical news archives give an even more comprehensive view of the markets when used with Tick
History. LSEG Tick History is our historical market data solution offering two distinct yet complementary
datasets. The LSEG Tick History dataset is normalised, and contains global intraday time and sales, quotes
and market depth dating back to January 1996. It contains in excess of 45PB of data covering both over thecounter (OTC) and exchange-traded instruments from over 500 exchanges and third-party contributors.
The LSEG Tick History – PCAP dataset captures granular, raw exchange data, that’s nanosecond
timestamped, for complete sets of lossless, top-of-book and full-depth data (Levels 1, 2 and 3). It is available
in both raw and normalised formats. Our historical news analytics scores, with millisecond time stamps,
dates back to 2003. Our datasets are complemented with RIC identifiers and Perm ID tags to make dataeasy to map and inter-operable. Combined with Machine Readable News we provide all the data youneed to take an idea from initial generation, to backtesting and analysis, and then onto production.

On 24 January 2024 Reuters
first reported that KPS CapitalPartners had recently exploreda takeover offer for UK specialty
chemicals maker, Elementis.
Shares in Elementis, whichmakes a range of chemical andpersonal care products, closed
at 125p on the day giving thecompany a market value of around
735 million pounds. Elementis
had previously rebuffed othertakeover bids, including in 2020
from Mineral Technologies and in
2021 from Innospec. It followed,
disappointment expressedby shareholders in the British
chemicals company around
its performance. In September
2023, Franklin Mutual Advisers
had urged the Board to find abuyer for the company. Elementis
shares rose as much as 10.5%
on the news, and it became thebiggest percentage gainer in theFTSE 350 index. The story also
prompted shareholder Franklin
Advisers to issue a public letterto the Board to call for a sale ofthe company.

Key use cases for Machine Readable News that are complementary to Real-Time –Optimized and Tick History data focus on three areas: research, backtesting andmarket surveillance.

ResearchMarket surveillance

Gaining insight: Analysis of news articles allows you togain insights into factors driving market prices. Using thisinformation can enrich research reports to support investment
decision-making.

Ensuring compliance: Better understand the relationship between
news data and market movement to develop resilient compliancestrategies by identifying potential conflicts of interest.

Analysing news: Discover which companies are mentioned in
the news more often, whether they’re discussed with a positiveor negative sentiment, which companies are talked abouttogether, as well as other company trends and patterns. This
information can be used alongside market data to identify potential
trading opportunities or potential risks such as possible M&As,
partnerships and changes to supply chain relationships, which caninfluence strategies.

Systematic trading
Identifying market-moving events: Correlating tick history andmarket data allows you to identify news events that have asignificant impact on market movements, enabling you to develop
trading strategies that take advantage of these.

Supporting machine translation: This enables translation of news
articles and prices from one language to another. Alongside market
data, this information can provide additional insight into markets,
including new and emerging markets.
Generating natural language outputs: Supports the creationof research reports tailored to your use cases, for example focusedon particular companies or securities.

Risk management

BacktestingManaging risk: By receiving notifications when certain eventsoccur, such as stock price changes, or when a certain company ismentioned, you can connect these events with relevant market
data to detect patterns and better understand the relationshipbetween news and market prices. This can enable the developmentof more robust risk management strategies to identify potential
risks, like supply chain, ESG, filings, regulatory and corporate actions.

Developing predictive models: Correlating tick history data withnews and other data, combined with machine learning forecastingmodels, enables development of more accurate predictive models
for trading and investment decisions.
Modeling topics: By identifying the topics that are being discussed
in news articles, you can identify themes driving market prices
alongside market data insights, and further develop your trading
strategies or investment decisions.

Measuring news impact on market sentiment: Correlate realtime and tick history data with news sentiment data to measure
the impact of news sentiment on sectors or specific RICs. Develop
strategies that take advantage of changes in market sentiment andpredict how blocks of the market may position their strategies so
you can stay ahead.

Useful links
Machine Readable News

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