June 1st, 2021

Beating The Benchmark: How Our AI Powered Portfolios Have Outperformed IWB Since September 2020

Executive Summary Using AI can help asset managers find performance within their own portfolios Risk factor constraints and AI portfolio optimizers are one of the ways to utilize machine learning today Our portfolio with factor constraints has outperformed IWB by 29.33 percentage points since September 2020 Our AI portfolio without factor constraints has still beaten

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May 31st, 2021

Webinar Recap: How to Adapt Alternative Data to Asset Management

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

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May 19th, 2021

Boosted Insights Product Update: Feature Importance Offers Deeper Understanding of Your AI Asset Management Models

As the adoption of new technology in investment management continues to grow, AI based insights have proven invaluable for investment managers seeking to add additional alpha to their portfolios. However, one deterrent in fully embracing AI is the need to explain AI-based decisions to stakeholders. Fund managers cannot use black box algorithms that don’t give

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May 13th, 2021

Using Artificial Intelligence Optimizers To Reduce Turnover and Volatility For Institutional Investors

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

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April 28th, 2021

Recapping Machine Learning and AI: An Investment Perspective, Presented by Boosted.ai and Rise New York

Machine learning and artificial intelligence are transforming nearly every aspect of the financial services industry, with companies that offer data-centric and machine learning-related tools changing how trillions of dollars are managed worldwide. As investment models increasingly depend on data, key players in the industry are discovering the value of using artificial intelligence. The expansion of

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April 12th, 2021

ChinaAMC Integrates Machine Learning Solutions from Boosted.ai into Investment Process

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,

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March 29th, 2021

AI For Asset Management Is Interesting. But How Can I Use It?

The ever advancing push of technology is remarkable. The first iPhone only came out in 2007. iPads were even later to join the party, coming out in 2010. In 2009, people were still using Netflix (in Canada) to order DVDs. Things that people once thought were indispensable become relics (hello, Panasonic Shockwave!) and things that

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March 22nd, 2021

Another Predictive Use of AI for Investment Managers: Factor Timing

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

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February 24th, 2021

How Boosted.ai Uses Machine Learning to Help You Generate Stock Ideas

Curious about machine learning for investment management? Check out this infographic, which illustrates how Boosted.ai combines your capital markets expertise with big data, adds our proprietary, finance-specific machine learning algorithms and then generates ideas for your portfolio.  Our machine learning takes your inputs and works to create a dynamically ranked list of stocks that can

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February 12th, 2021

Driving AI Success In Investment Management With Factor Timing

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%

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February 3rd, 2021

What is Quantamental Investing and How Can it Make Me a Better Investor?

Cromulent. Embiggens. Quantamental. They all sound made up, right? Strangely, some investors might be more familiar with the first two terms (Simpsons references all the way from the 1990s) than the last. Quantamental is a portmanteau, combining quantitative and fundamental asset management styles, for the best that both strategies have to offer. Quantitative Challenges Quantitative

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January 29th, 2021

How To Avoid Short Blow-Ups Using Machine Learning Risk Factor Constraints

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

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December 7th, 2020

Recapping The Crux Summit Fall 2020: The Future of the Data Supply Chain

  Financial services companies have spent billions of dollars and countless hours reinforcing and protecting their data infrastructures in a costly and inefficient manner. As we enter an era that will continue this trend and rely on data more than ever before, we are seeing a new data supply chain emerge to help companies access,

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December 1st, 2020

Navigating the Quant Shock: How Machine Learning Can Help Portfolios Through Regime Shifts

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

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November 20th, 2020

Boosted.ai Featured in TabbForum on the Three Top Downfalls of Quant Investors

Why do some quant strategies fail? It has to do with all of the human decisions that go into setting up the machine.

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August 26th, 2020

Boosted.ai Featured in Waters Technology on Navigating COVID-19 Risks

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

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August 26th, 2020

Understanding the Machine: The Importance of Relative Valuation

The rise of the machines: not just the lukewarm third installment of the Terminator series, but also, the ominous headline nearly every publication uses when writing about machine learning.

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August 26th, 2020

Boosted.ai Raises $8 Million to Bring Artificial Intelligence To Investment Management

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

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August 26th, 2020

Portfolio Construction and the COVID Cluster

· While we do not have a crystal ball and cannot know which stocks will become most impacted by some future event, we offer a solution of how to mitigate this problem by using the example of the COVID cluster of stocks. · Using advanced machine learning techniques, we can create portfolios with increased resilience

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August 26th, 2020

Modelling The Coronavirus Risk Factor & Neutralizing It

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.

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