AI Model Provider Boosted.ai Raises $35 Million

By Sanford Bragg January 19, 2022

Toronto-based Boosted.ai, a provider of outsourced AI-driven quantitative modeling, completed a Series B funding as it plans to double its staff. The firm has been expanding the use cases for its platform, diversifying beyond alternative data integration into stock selection and portfolio risk mitigation.   

The Series B raise was led by Darien-based Ten Coves Capital and San Francisco-based Spark Capital. RBC, a client of the firm, also invested in the funding.  The capital will be used to scale the firm’s platform, Boosted Insights.  The firm’s previous $8 million Series A was closed in August 2020.  Total funding to date has been $46 million.

Boosted Insights allows users to set parameters which reflect their investment style, such as the investment universe, investment goals (alpha or pure return), which features they use to pick stocks (i.e. P/E ratio, EPS, or more technical ones like Bollinger bands) and portfolio settings (how they want the model to trade).  The model then makes stock recommendations based on those parameters, disclosing the parameters used to select each stock.  Users can back-test the model results and optimize portfolios to reduce risk factors.

The primary use case is to screen for best ideas which are then subject to further qualitative analysis.  Long/short hedge funds are also using the models to identify potential short ideas. 

Recent enhancements to the platform allow users to visualize and manage factors such as momentum and volatility, similar to the way Barra’s risk models operate. Portfolio risk management has become an important use case for the Boosted Insights platform.

An early use case for Boosted.ai’s platform was to integrate alternative data such as credit card transaction or geolocation data.  The platform allows users to upload alternative data which is cross-referenced against their equity universe, flagging any problems with mapping the stocks.  The platform determines if the custom data is in fact predictive and generates trade ideas and rankings incorporating the alternative data. Users can combine different models and data sets or highlight which of the features of their alternative data are most predictive and when.

Boosted.ai was founded in 2017 by two former Bloomberg technologists — Joshua Pantony and Jon Dorando — and a former buy-sider, Nicholas Abe.  The firm says it has a total of 40 buy-side clients, which has increased 100% over the last year.  Staff registrations on LinkedIn currently number 39 employees, up 30% over the last twelve months.  Thanks to the recent capital raise, Boosted is recruiting for 15 open positions, based in Toronto and New York.

Our Take

Boosted.ai has evolved from integrating alternative data into a broader set of artificial intelligence use cases.  At this point, alternative data remains a feature but the company focuses more on stock screening and portfolio risk management.

Machine learning such as that offered by Boosted.ai seems to be displacing alpha capture systems which collect trade ideas from research providers.  One of the leading third-party alpha capture systems, TIM Group, has seen its LinkedIn-registered employees shrink 30% over the last two years.

Boosted.ai’s core value proposition is that it offers outsourced quantamental solutions to qualitative asset managers. The evolution of its use cases provides a window on the various ways artificial intelligence can support investment decisions. Customized stock screening and portfolio risk management are already resonating with clients and doubtless the list will grow.    

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