Text Analytics in Finance: how it empowers data driven decisions

RavenPack | May 29, 2018. Last updated: September 8, 2023

Text analytics has become an essential tool in the financial sector, particularly in investment banking and finance. With over 80 percent of data in an unstructured form, the conversion of text into valuable data-driven analytics is now a priority.

Text analytics is about deriving high-quality structured data from unstructured text, which has become increasingly valuable to the financial sector. Widely used data sources for text analytics include social media, along with internal and external email, instant messages, news articles, online forums. Other used sources can include documents in a variety of formats such as PDF files or xml, and online forms such as applications containing text or forms containing structured data stored as text.

What is Text Analytics?

Text analytics is the process of deriving high-quality structured data from unstructured text, providing immense value to the financial industry. Sources commonly used for text analytics include social media platforms, internal and external emails, instant messages, news articles, and online forums. It also encompasses documents in various formats such as PDF files or XML and online forms containing structured data stored as text.

How Does Text Analytics Work?

Text analytics employs natural language processing (NLP) techniques to extract meaningful insights from textual content. It utilizes advanced algorithms, machine learning, and AI to process and analyze vast amounts of unstructured data.

Benefits of Text Analytics in Finance

By converting unstructured content into structured data, text analytics enables financial institutions to unlock the hidden potential of textual content. One prominent output of text analytics is sentiment analysis, which involves assigning granular sentiment scores to entities and events identified in text. These scores are widely utilized in various financial applications, allowing professionals to leverage the predictive power of sentiment analysis.

Here are a few examples:

Introducing RavenPack Analytics

RavenPack is a trusted leader in the field of text analytics for finance, pioneering innovative products since 2003. With a vast collection of white papers from our own Data Science team, top investment banks, Ivy League universities, and specialist research firms, we have consistently proven the value of our analytics.

RavenPack Analytics is one of our flagship products, highly regarded and trusted by top hedge funds and investment banks. Powered by a cutting-edge NLP engine, this tool empowers users to enhance efficiency, accuracy, and decision-making through data-driven insights.

Key Features and Capabilities of RavenPack Analytics

RavenPack Analytics consolidates various unstructured data sources commonly used by investors into a single, enriched format tailored specifically for financial applications. Some of its notable features include:

Structured Data

RavenPack Analytics turn plain text into structured text enriched with meta-data. A structured text is a representation of a text written for humans that can be easily understood by algorithms. In particular, structured texts articulate the following:

Centralized Search

building databases of billions of documents is only relevant if they can be efficiently searched. RavenPack Analytics delivers comprehensive search capabilities across all documents, including third-party content, research reports, meeting and conference notes, regulatory filings, newsletters, and RSS feeds. This enables users to quickly find specific information of interest.

Web APIs:

RavenPack also provides web APIs, allowing developers to request structured data for seamless incorporation into their own applications, thereby facilitating customization and integration.

Ravenpack Text Analytics in 3 steps

  • 1

    Choose your data

    Choose from a broad range of content to fit your business needs

  • 2

    Select your universe

    Create a list of the topics and entities that you care about

  • 3

    Pick your delivery

    Choose between our dataset builder, Web API, or Snowflake to receive data

Text Analytics”
Who Uses RavenPack Analytics:

Alpha Generation and Risk Management

RavenPack Analytics is a powerful tool embraced by various professionals for alpha generation and risk management purposes. Below are some key user groups who benefit from this innovative technology:

and Discretionary Investors

RavenPack Analytics caters to investors seeking a "quantamental" or data-driven approach to investing and trading. By leveraging advanced text analytics, these investors can uncover valuable insights, identify relevant signals, and make informed decisions based on a comprehensive analysis of regulatory filings, broker research, transcripts, news, blogs, investor notes, and more.

Research Analysts
and Portfolio Managers

RavenPack Analytics assists research analysts and portfolio managers in navigating through the noise in the market. By utilizing this tool, professionals can efficiently sort through vast amounts of unstructured text content to pinpoint relevant signals and extract valuable information. This allows them to uncover hidden opportunities, monitor market trends, and make well-informed investment decisions.

Data Scientists
and Quantitative Investors

RavenPack Analytics provides structured data derived from unstructured text content, directly applicable to investing and trading models. Data scientists and quantitative investors can incorporate this valuable information into their models, enhancing their ability to generate alpha and optimize trading strategies. By leveraging the power of text analytics, they gain a competitive edge in developing data-driven investment approaches.

and Risk Managers

They rely on RavenPack Analytics to automate the identification of false positives and to receive timely alerts on relevant internal conversations. This technology helps them efficiently sift through large volumes of data and pinpoint potential compliance risks. Additionally, RavenPack Analytics provides a robust framework for documenting and providing proof of conversations, ensuring compliance with regulatory requirements.

Insights From RavenPack’s CEO

Armando Gonzalez, CEO of RavenPack, emphasizes the significance of text analytics in the financial sector and highlights the challenges faced by clients:

Armando Gonzalez
Armando Gonzalez

CEO & Co-Founder


We have developed a comprehensive understanding of information flows, allowing us to transform raw data into actionable analytics.

"At RavenPack, we serve some of the most successful hedge funds and asset managers, as well as renowned investment banks. Our focus lies in providing advanced Text Analytics solutions tailored to the needs of these industry leaders, enabling them to navigate the complex landscape of financial data with ease.

The challenges faced by our clients are multifaceted, primarily revolving around the management of vast amounts of data and the subsequent extraction of meaningful insights. In response, we employ cutting-edge artificial intelligence techniques, leveraging machine learning and Natural Language Processing. We have developed a comprehensive understanding of information flows, allowing us to transform raw data into actionable analytics.

Looking towards the future, we are making substantial investments in data science, nurturing a dedicated team of experts committed to researching our data. Their aim is to assist our customers in identifying anomalies, uncovering valuable insights, and expediting the valuation process. By leveraging the scientific method, we combine innovative technologies, specialized expertise, and rigorous research to provide unparalleled solutions to our clients.”

Get Started with RavenPack Analytics

To learn more about RavenPackAnalytics or to request a free trial, reach out to info@ravenpack.com . Transform your financial operations with the power of text analytics today.

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