A-Team Insight Blogs

RavenPack Offers Portfolio Ranking Tool Based on Sentiment

Share article

RavenPack, a provider of big data analytics to financial institutions, has released a portfolio ranking tool based on sentiment factors. Users can upload a stock portfolio and tailor their analysis to address risks detected across 19,000 sources including news, social media, regulatory filings, and transcripts. The portfolio ranking tool uses stock rankings to monitor users’ headline risk, identify sentiment indicators, or flag companies that are no longer aligned with users’ strategies. Ranking stocks using RavenPack’s sentiment factors can provide a way to incorporate the value of big data in a quantifiable manner.

RavenPack CEO Armando Gonzalez says: “RavenPack’s sentiment analysis is performed using a proprietary Natural Language Processing (NLP) engine designed specifically to process finance-related content. Most NLP services are trained with general language corpuses using machine learning techniques, which yield low precision and recall levels, and are insignificant to yielding alpha or excess returns. These naïve approaches lack the understanding of business terms or financial lingo or the implication of business, macroeconomic and geopolitical announcements on stock prices, volatility, and market liquidity.”

Users of the ranking tool are able to rank high those companies that have positive earnings and product sentiment, and view whether a company has experienced a negative environmental, social, or government (ESG) event. The tool also provides a means of ranking stocks by sustainable corporate behaviour and measuring how well firms manage idiosyncratic and systemic risks.

Gonzalez concludes: “Investors seeking value need to be informed of how ESG events impact both the short-term and long-term value of their investments. By systematically analysing thousands of news and social media sources, RavenPack provides awareness of ESG factors that can improve or erode the value of securities.”

Leave a comment

Your email address will not be published. Required fields are marked *

*

Related content

WEBINAR

Recorded Webinar: How to exploit the opportunities of alternative data

Alternative data is emerging as a key component of buy-side firms’ efforts to seek out new investment opportunities, for many filling the gap left by the unbundling of sell-side research from execution. By tapping into unique, non-traditional data sets, hedge funds and quantitative fund managers hope to exploit unfound opportunities before they hit the mainstream....

BLOG

Refinitiv Outlines Development of Machine Learning and Data Science

Machine learning and artificial intelligence (AI) are finding key use cases in risk management, performance analysis and trade idea generation, suggesting the technologies have moved beyond the experimental stage to become core components of business strategy and investment. Development is being driven by growing numbers of data scientists employed by financial organisations. The barriers to...

GUIDE

Data Lineage Handbook

Data lineage has become a critical concern for data managers in capital markets as it is key to both regulatory compliance and business opportunity. The regulatory requirement for data lineage kicked in with BCBS 239 in 2016 and has since been extended to many other regulations that oblige firms to provide transparency and a data...