Boosted Insights augments the investment process by empowering investment professionals to source new ideas and create models based on their unique financial domain expertise.
Explore some of the many ways Boosted can empower financial firms and enhance the investment decision process.
Dynamic Stock Screening
Create a more dynamic tool to screen for ideas. Traditional screening tools can be backward looking and do not adapt to new regimes.
Boosted Insights continuously learns from your inputs to predictively rank stocks. It discovers and explains which combination of features are important in different periods.
Our Machine Learning provides dynamic and adaptive stock rankings. It compares each stock in a universe to each other (250,000 comparisons in S&P 500).
Machine Learning Portfolio Overlay
A portfolio manager wants to implement machine learning in their portfolio to reduce risk and augment returns but does not want to dramatically increase turnover.
They quickly and efficiently upload their existing portfolio to Boosted Insights by CSV or our custom API and apply our machine learning overlay.
The same portfolio – with its position weightings altered using our machine learning – optimized to offer better returns at similar (or often lower) turnover.
A portfolio manager can create a more targeted basket of longs or shorts within distinct sectors or with specific attributes.
Combine your own fundamental views and select your own variables to create a more targeted basket of stocks that the machine predicts will outperform/underperform the market.
A highly customized basket of stocks is selected for a specific purpose, allowing the portfolio manager to create additional alpha over using a broad market index.
Machine Learning Risk Mitigation
A portfolio manager looks to reduce their risk, both known (factors like momentum, volatility and trading activity) and unknown (including black swan events like Covid-19).
Using Boosted.ai’s machine learning to isolate and mitigate risk.
A backtested portfolio that highlights where the PM’s portfolio is most at risk, with concrete ways to reduce that risk.
Alternative Data Integration
A fund believes that their custom data is predictive and wants to generate maximum value from it.
The fund uploads their custom data (alternative data, like credit card, geolocation, insider transactions) to Boosted Insights and creates a model.
Boosted Insights determines if the custom data is in fact predictive and generates trade ideas, rankings and a full analysis on the strength of this data.
Actively Traded Models
A portfolio manager wants to operate on a fully quantitative basis using machine learning.
The portfolio manager creates a model (using their own unique variables), sets portfolio constraints and then has the model “go live” with current data. They then execute the model’s dynamic trading strategy.
A fully quantitative portfolio runs using technical variables and/or fundamental data.