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.
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.
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.
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:
Sentiment scores, derived from advanced news analytics, can be incorporated into quantitative trading models. By analyzing sentiment, relevance, and other key factors, financial professionals can develop systematic trading strategies that exploit market inefficiencies, identify trends, and generate alpha.
Sentiment scores provide valuable insights into market sentiment and risk perception. By incorporating sentiment analysis into risk models, finance professionals can better understand market sentiment shifts and potential impact on portfolios.
Sentiment analytics help financial professionals identify and monitor significant news events that can impact specific stocks, sectors, or the overall market. By tracking sentiment scores in real-time, they can identify news-driven market movements and respond swiftly to capitalize on opportunities or manage risk exposure.
By analyzing sentiment and relevance scores associated with specific companies or industries, financial professionals can gain valuable insights into market perceptions, gauge market sentiment, and identify potential investment opportunities or risks.
Rave Financial professionals can assess the overall sentiment of the market, specific sectors, or individual stocks to gauge investor sentiment and market expectations. This information can be valuable in understanding market trends, sentiment-driven price movements, and investor sentiment shifts.
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.
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:
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:
using an evolved dictionary that understands not only words but the way they relate together as concepts, as in ‘Apple’ is a ‘fruit’ and ‘Fuji’ and ‘Honeycrisp’ are apples, but also ‘Apple’ designates ‘Apple Inc’, a company founded in 1976 in California, depending on the context, RavenPack Analytics identifies what texts are about.
using millions of rules, RavenPack Analytics also aims to understand what event is described in each sentence. For instance a sentence in an article may describe how a company is entering bankruptcy proceedings, because it announces that it has filed for “Chapter 11”. When dealing with news, this step essentially captures the nature of the news.
Whether a company is mentioned in passing, or if it is the subject of an article, makes a big difference for algorithms. Quantifying the centrality of a particular subject in a story is what structured data encodes as relevance. Additionally, whether the story is breaking news, or relayed news, matters if you aim to modelize market reaction to a story, so structured data also needs to quantify what natural language models call “novelty”. Imagine checking every story in the news to determine whether the story has been reported at any time over the past year, across millions of articles: that’s what RavenPack Analytics does, for every story.
All in all, the purpose of understanding texts is usually to figure out if it’s good news or bad news. It can be both: for instance, one company winning a lawsuit against another may see the news of the verdict as good news, whereas the other party may not. To address this, RavenPack Analytics produces sentiment scores for each entity and event identified, and for each part of the text – from the entire document to individual sentences.
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.
RavenPack also provides web APIs, allowing developers to request structured data for seamless incorporation into their own applications, thereby facilitating customization and integration.
Choose from a broad range
of content to fit your business needs
Create a list of the topics and entities
that you care about
Choose between our dataset builder,
Web API, or Snowflake to receive data
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:
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.
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.
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.
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.
Armando Gonzalez, CEO of RavenPack, emphasizes the significance of text analytics in the financial sector and highlights the challenges faced by clients:
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.”
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