StockSnips, Inc

StockSnips is changing the investing paradigm by making advanced AI technologies and investor sentiment available to asset managers and investment advisors.

Building predictive models requires extensive quantitative research and signals that achieve Alpha. Adapting to changing market conditions and sector rotations requires continuous monitoring of key indicators and systemization. StockSnips enables investors to deliver better outcomes and downside protection to clients.


Leadership Team

Ravi Koka — CEO & Founder

Over 3 decades of technology and R&D experience with current focus in AI and Natural Language processing. He has successfully applied technology to solve business problems and his research on using NLP / ML for deriving News Sentiment has led to the successful launch of StockSnips solutions for the investment industry. Ravi has a Master’s Degree in Computer Science.

Rebecca Wilde — Managing Director

Originally from the United Kingdom, Rebecca draws from her experience in both the Wealth Management & Business Lending sectors to cater adeptly to the multifaceted requirements of financial advisors. As a licensed Financial Advisor, she spearheads business development, cultivates strategic partnerships, and leads growth and marketing blueprints. Before her role at StockSnips, Rebecca immersed herself in the FinTech startup arena, accumulating invaluable industry insights. A proud Florida International University graduate, she holds degrees in Economics and an MBA.


StockSnips AI Portfolios leverage our proprietary AI Platform, that is a sophisticated tool that uses artificial intelligence and machine learning algorithms to analyze & derive investor sentiment from news articles in order to make investment decisions without human intervention. This approach involves analyzing news articles to identify the sentiment expressed within them, such as whether they are positive or negative about a particular company. This platform uses a ranking algorithm based on sentiment and sentiment momentum and for some of our portfolios harnesses the power of Reinforcement Learning to poll the market environment and dynamically adapt the portfolio allocations in order to optimize returns.