Our Blog

January 14th, 2022

What investment manager wouldn’t love a crystal ball? Something to warn them when black swan events like Covid-19 hit, and one that can alert them when things like GME or vaccine news can shift the market. Harnessing the power of artificial intelligence is not a magic wand, but making efficient use of the vast swaths

December 2nd, 2021

Here at Boosted.ai, we speak to a variety of potential users of our artificial intelligence investment management software and many traditional investor’s first statement is something along the lines of “Sure, artificial intelligence is interesting, but how would I use it?”. It is a topic we covered that in a previous post, but to recap,

November 10th, 2021

We know that institutional investors are short on time and need their data-driven insights to be understandable both from a high-level view and in deeper detail. As a complement to our Portfolio One-Pagers, we are proud to release Stock One-Pagers. These fact-filled pages illustrate how the asset manager’s machine learning model thinks of every equity

September 30th, 2021

In a recent webinar, Erik McBain, Director of Growth at Boosted.ai, shared his latest insights into the shifting investment landscape and how AI will continue to impact the institutional investment and asset management industry. Changing Industries and the Universal Adoption of AI As artificial intelligence evolves and becomes increasingly efficient, various industries have begun to

September 17th, 2021

Machine learning for asset management has a lot of moving pieces: portfolio allocation, risk management, and ensuring that any predictions the machine makes are explainable to different stakeholders are just a few things investment managers have to think of when creating their ML portfolio models. Using statistical techniques to summarize and explain complex market movements

August 9th, 2021

Something we have heard from fundamental asset managers is that artificial intelligence is interesting, but it is difficult to incorporate it into their process. We wrote a post about different ways investment managers can begin implementing AI here, but we’re also always trying to make our product, Boosted Insights, better and more easily consolidated into

July 28th, 2021

Something we at Boosted.ai have learned from our clients is that for institutional investors, explainable AI is not a nice-to-have, it is a must. In order to pass muster for asset managers, any reason our machine learning algorithms make decisions must be transparent to their stakeholders. We like to think of our machine learning algorithms

June 18th, 2021

AI for asset management is a new and exciting field. It’s also pretty complex to understand. At Boosted.ai, we endeavour to make artificial intelligence for investment management as simple and point-and-click as possible, while still offering the powerful machine learning that institutional investors require to succeed. However, we have found that “show, don’t tell” tends

May 31st, 2021

Institutional asset management is under pressure – fee compression, shifting investor sentiment and technological advances are placing demands on portfolio managers to find new ways to deliver value to investors. A massive explosion of alternative data has started to appear over the last several years and is likely to continue to accelerate over the next

May 13th, 2021

Executive Summary: Artificial intelligence portfolio optimizations can help institutional investors with volatility and turnover portfolio needs Significant reductions in volatility and turnover can come at only minor reductions to return (and sometimes improvements in other metrics like Sharpe and alpha) Our optimizers led to a 576 bps drop in volatility, a 92.26 percentage point drop

April 12th, 2021

Implementation brings performant, explainable AI to one of China’s largest asset managers Boosted.ai, the leading distributed machine learning platform for global investment professionals, today announced it will provide its machine learning platform, Boosted Insights, to China Asset Management Co., Ltd. (ChinaAMC), one of the largest fund management firms in China ($245.5B AUM as of December 31,

March 22nd, 2021

Executive Summary Factor timing is a very predictive use of machine learning for investment managers Utilizing factor timing can help flag signals on momentum, value, volatility and other factors Our machine learning surfaced the switch to Value over Momentum in early 2021 Capital markets expertise is still needed to glean the most value from the

February 12th, 2021

Executive Summary Machine learning in investment management works very well on bottom-up fundamental analysis Using artificial intelligence to practice factor timing is also predictive Factor timing aggregates the star rankings and factor scores the machine learning assigns to each equity in a user’s stock universe Our AI factor timing led to average returns of +1.42%

January 29th, 2021

It’s no secret trends come in waves. Right now, the 90’s are back in a big way. Fresh Prince of Bel-Air, Saved by the Bell and Sex and the City have all gotten reboots, kids are dressing like they’re en route to a Counting Crows concert, and GameStop (GME) – that place actual 90’s kids

December 1st, 2020

Machine learning can be useful for a lot of things – picking out movies to suggest, performing live translation, even (one day) fully self-driving cars. In investment and finance, the application of machine learning is still relatively new, but it offers exciting results, especially in the age of uncertainty we find ourselves in, here in

August 26th, 2020

How can you measure and predict the effects of something that hasn’t happened in a century?

August 26th, 2020

We are thrilled to announce that we recently closed an $8 million USD Series A financing round, led by Portag3 Ventures.

August 26th, 2020

Nobody can tell the future. Six months ago, I would have never guessed (even for April Fool’s Day) that we would find ourselves here, in a real-life version of the film Contagion. Modelling a black swan event like COVID-19 with traditional quantitative techniques is extremely difficult, due precisely to any black swan event’s inherent unpredictability.