SPOTLIGHT
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Boosted.ai: Bringing Advanced Machine Learning to ‘Quantamental’ Investing

As technology develops it can be applied to more and more aspects of the financial world. AI and machine learning are key in making sure various sectors evolve – investing is no different. 

Whether it be investing in ESG compliant, making sure you have the latest data to inform an investment, technology can be applied to help make the process more efficient. The notion of “quantamental investing” is one of the fastest-growing trends within asset management right now – it refers to traditional, fundamental portfolio managers implementing new technology, data and AI into their workflow to improve returns. 

The Fintech Times spoke to Joshua Pantony, CEO and co-founder of Toronto based, Boosted.ai to learn about how the company is using technology to upgrade the investing sphere. Having previously worked at Bloomberg LP, Pantony was able to bring his expertise in machine learning and apply it to a new platform, Boosted Insights, to bring advanced quantitative investing techniques to portfolio managers, in a clean easy to use UI. 

What do you think makes Boosted.ai stand out and unique?

Boosted.ai combines investors’ financial domain expertise with big data and finance-specific machine learning algorithms. Our proprietary platform, Boosted Insights, is a plug-and-play AI tool for investment managers seeking to incorporate machine learning and alternative data into their investment process. What’s most critical is that all of this is explainable and understandable no matter what type of investor you are.

Boosted Insights empowers fundamental and quantitative institutional investors to create customised models tailored to their strategy that learn and identify patterns, helping to generate alpha or mitigate risk. The platform supports idea generation, portfolio construction and portfolio monitoring without requiring users to be coders, software engineers or data scientists.

What are some of Boosted.ai’s unique features?

Our clients can create models by utilising the included datasets (North American, Europe and APAC region equities, Federal Reserve data, ESG and options volatility data) or uploading their own (including alternative data). They then select the features they think drive a stock’s performance based on their own financial expertise, including fundamental, macro or other variables. This creates the basis for their model, which they can then back-test to see how it might have performed. Once a user is comfortable with their model, they can take it live to see how the results would look today. These models can be used as a standalone portfolio of stocks or as a machine learning overlay to existing portfolios.

One of Boosted.ai’s unique features relates to ESG data. Boosted.ai has incorporated ESG data from OWL Analytics so users can conduct research upon a broad range of ESG metrics, including point-in-time ESG scores ranked by sector and geography. We have also expanded the geographies we serve, integrating APAC market data into the platform so investors can apply machine learning tools to all developed markets globally.

How have customers responded to these unique features?  

Asset managers that attempt to integrate machine learning into portfolios on their own almost always find it difficult, time-consuming and costly. Using Boosted Insights, investment managers can seamlessly create models to generate trade ideas, find and isolate risk factors and add machine learning overlays.

Our clients have told us that Boosted.ai has both improved efficiency and profitability for their firms. Our human-plus-machine approach allows users to seize on a first-mover advantage, as the platform surfaces opportunities that may not have been otherwise intuitive.

What do Boosted.ai’s road map and growth plan look like? How do you plan to continue to differentiate the company from the rest of the competition?

Over the past year, our machine learning models helped navigate the quant shock, flag risk in short positions like GME and pilot through black swan events like covid-19. The platform likewise helps to constrain risk factors such as momentum, value and size.

As even the most sophisticated quant investors struggled with their historical-based models and returns in a volatile market, Boosted Insights helped clients develop explainable models to find new alpha and focus on returns.

We will continue to adapt to market conditions as well as client needs to deliver innovative solutions in the quant investing space. Over the next year, we plan to add new functionality to our platform, integrate additional datasets for native use by users and explore the use of machine learning within fixed income.

The pandemic has been a mixed blessing for companies in the financial sector. Some have been able to digitise and prosper whereas some have failed miserably. How has the pandemic affected Boosted.ai?

The pandemic has been a test for everyone. It has also been a test of the strength of our software. It gave our models the chance to prove the ability to respond to adversity in the market. Altering models to understand the risk factors associated with certain investments within the pandemic was a massive point of concern when the world shut down. However, our platform has persevered and grown stronger as a result.

For example, we used advanced machine learning techniques to model the pandemic risk factor, which allowed us to create portfolios with increased resilience to external shocks like covid-19. We found a cluster of stocks most heavily related to covid-19 and identified those stocks’ relationship to the pandemic. With that information, we adjusted the weights of those stocks through portfolio optimisations and constraints and improved portfolio return by over 10% during the crisis period.

What have been the greatest innovations in AI and ML that you have seen in the last decade and where does Boosted.ai fit in here?

The pace of innovation in AI and ML has increased dramatically across all industries over the last decade, from healthcare and finance to travel and entertainment. One of the greatest innovations we’ve seen has been the explosion in the quantity of accessible alternative data. While the increase in quantity gives the machine the ability to make better decisions, it’s critical to ensure the data is of high quality as well.

At Boosted.ai, we take a human-plus-machine approach to AI, which allows us to understand exactly where data comes from and to identify any inherent bias. Our insights allow us to take alternative data, make it valuable and present it in a way that is easy-to-understand and explainable.

What problems have these innovations solved?

Innovations in AI and ML have transformed the way we address challenges in the investment industry. They’ve given us access to information that a human would not be able to uncover independently. When we take these learnings and combine them with human expertise, we’re able to discover unique solutions to help us reduce risk and optimise returns.

What do you believe still needs to be done in the field?

The industry as a whole needs to continue training models on newer and better data to ensure that they don’t learn any bias and can make critical decisions. As any machine learns more and has access to more data, it will become better at uncovering accurate findings. To get to that point, we need to enhance our AI and ML solutions with continuous learning so that it can eventually begin to learn and incorporate information the same way a human does – and maybe even better.

Final Thoughts

AI has become a terrible buzzword, especially within finance, and especially because it over-promises and under-delivers. Because of that, we are not here to overhaul strategies, and we don’t bite off more than we can chew. The Boosted Insights platform is laser focused on helping human portfolio managers run better strategies, seek new alpha and reduce risks. It natively allows investment teams to use alternative data or ESG metrics to shine a new light on hypotheses. We firmly believe that AI should be an additive force to organizational success.

Author

  • Francis is a junior journalist with a BA in Classical Civilization, he has a specialist interest in North and South America.

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