- The RavenPack Analytics platform is used by top banks, hedge funds, and asset managers
- The company will use the proceeds to expand into Asia and grow beyond financial services
- Investment led by GP Bullhound Fund IV, a €113 million growth technology fund focusing on software, digital media, fintech and marketplaces
Part of the proceeds from the funding round will be used to go beyond financial services, developing products aimed at corporate customers.
RavenPack’s data analytics platform is used by top hedge fund and asset managers, as well as Tier 1 banks to better manage risk and enhance returns. RavenPack became a pioneer in alternative data by creating sentiment indicators derived from news and social media that plugged systematically into financial trading applications. The firm has more than 100 team members and offices in New York City and Marbella, Spain.
“Following our success in North America and Europe, we are experiencing significant growth in demand from Asia for our unique analytical services,” said Armando Gonzalez, CEO and Co-founder of RavenPack. “Global finance has reached a critical inflection point as asset owners and money managers embrace tools like machine learning and big data analysis to navigate complex and turbulent markets.”
Per Roman, a managing partner of GP Bullhound, said:
“I have had the pleasure of getting to know Armando and his team in the last four years and have in that period seen RavenPack mature into possibly the most interesting natural language processing and machine learning company in the world. It is therefore a pleasure to join as a shareholder and board member in the next chapter of growth and expansion.”
GP Bullhound is investing in RavenPack through its Fund IV, which focuses on growth stage businesses in the software, digital media, marketplaces and fintech sectors. Recent investments include Klarna, Tradeshift, Glovo and LendInvest.
RavenPack received its first institutional funding in 2017, raising $5 million from U.K. based venture capital firm Draper Esprit PLC (GROW:LN).
“Since we first invested in RavenPack over two years ago, the company has passed a number of significant milestones,” said Jonathan Sibilia, Partner at Draper Esprit. “Given their success in the UK and US markets, now is the right time for the company to move into Asia and scale its product offering to enable the next stage of growth. We are excited for the next phase of the company’s development and welcome GP Bullhound to the team.”
For RavenPack Geoffroy Dallennes email@example.com +34 952 907 390
For GP Bullhound Iman Crisby firstname.lastname@example.org
For Draper Esprit Isabella Cookson Isabella.email@example.com +44 (0) 207 932 8958
RavenPack is the leading big data analytics provider for financial services. The company’s products allow clients to enhance returns, reduce risk and increase efficiency by systematically incorporating the effects of public information in their models or workflows. RavenPack’s clients include the most successful hedge funds, banks, and asset managers in the world. For more information, please visit www.ravenpack.com.
About GP Bullhound
GP Bullhound is a leading technology advisory and investment firm, providing transaction advice and capital to the world’s best entrepreneurs and founders. Founded in 1999, the firm today has offices in London, San Francisco, Stockholm, Berlin, Manchester, Paris, Hong Kong, Madrid and New York. For more information, please visit www.gpbullhound.com..
About Draper Esprit
Draper Esprit is one of the most active venture capital firms in Europe, developing and investing in disruptive, high growth technology companies. We believe the best entrepreneurs in Europe are capable of building the global businesses of the future. We fuel their growth with long term capital, access to international networks and decades of experience building businesses. Currently, Draper Esprit is a shareholder in a diverse portfolio of companies including Trustpilot, UiPath, Transferwise, and Graphcore. For more information please visit www.draperesprit.com.