Industry faces 3 challenges to AI integration

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Welcome to Advisor to Go, brought to you by CIBC Asset Management, a podcast bringing advisors the latest financial insights and developments from our subject-matter experts themselves. 

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Greg Gipson, managing director and head of exchange-traded funds at CIBC Global Asset Management 

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AI has the power to transform ETF strategy. It’s really along three main dimensions. First, what goes into the ETF or the security selection. Two, how do you put it together, or portfolio construction. And three, how to monitor the ETF, or risk management. 

On the first part, in regards to what goes into an ETF, it’s important to remember that many AI processes, machine learning, etc., are really focused on pattern recognition and data analysis. So AI is able to process large amounts of data, unstructured data and alternative data sources, which just really enables a richer and more contextual approach to selecting the components that go into an ETF. 

On the portfolio construction side, there’s many existing approaches around portfolio optimization, risk-based optimization. Machine learning models can really add value to how you put things together or how you build a portfolio, and enables you to really have a real-time adaptation to what’s going on in the market, and is able to improve the responsiveness to which portfolio managers can adjust their holdings. 

On the risk management side, this is another important aspect for ETFs, and really for all funds in that, again, AI focused on that ability to recognize patterns, identify correlations, identify anomalies in data, and really act as an early warning sign for the human analyst or the risk department to understand what underlying risks are in the ETF, what underlying risks exist in the market, and the negative impact that that could have on the performance of the ETF itself. 

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Innovation in asset management, as it relates to incorporating artificial intelligence or machine learning, really has the ability to transform the way that businesses are run. 

So, first and foremost, if you think about the types of techniques that AI encompasses, many of them have been around for decades. 

The difference now really is twofold. One is the massive, massive increase in the amount of data or information that is available to investors. And two is the computing power, so the ability to process those large amounts of data. And what that enables asset managers, portfolio managers to do is really have a much richer or more thoughtful, more inclusive approach to predictive analytics, and really focusing on developing and designing customized solutions for investors. 

So that ability to really focus on the areas of the market that are driving performance, the ability to stratify investments, to really allow that targeted approach, targeted exposure, for investors to gain access to. And from an overall business perspective, obviously there’s the cost efficiency output, where really allowing the automation of processes, automation of data, automation of trade execution, automation, automation! Automation as the ability for business to scale systematically is more efficient than scaling through human capital.  

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The opportunities in ETFs for investors as it relates to AI are along a number of dimensions. So clearly, the easiest one would be investing in an ETF that is focused on investing in companies that would benefit from the AI revolution. 

But maybe a bit more detail on that is really the ability for AI to discern or distill the areas of the market that are driving performance. So think of these as thematic ETFs, where an AI or machine learning-based process is able to determine those securities that have a particular correlation or particular exposure to something like AI or something like data centers. And then that understanding allows the ETF manufacturer, ETF manager, to create a vehicle — an ETF — that then is offered to investors to gain exposure to an area of the market that they may otherwise not be aware of. 

Another opportunity in ETFs for investors is around the processing of data. So, as we mentioned earlier, there are massive, massive increase in the amounts of data. It’s often fragmented, it’s unstructured, it’s alternative, it’s sitting in spreadsheets or PDFs. And really what AI allows is that automation of data consumption, and then also clean and efficient and structured way of analyzing fragmented data. And this is particularly important in areas where information is more sparse. So if you think of areas like emerging markets, if you think of areas like commodities, right? Because often this data sits in an environment that’s not necessarily conducive to a systematic review or incorporation of the data into a process. 

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Integrating AI into ETF portfolios, or really any investment management process faces a number of unique and specific issues that need to be addressed. First and foremost is what goes into the model. So think of this as data quality. So the old adage “garbage in, garbage out” would apply to AI model use as well. 

So as important as the machine learning or AI-based approach is, I would argue that even more important is data acquisition, data cleansing and data storage. So just having a structured database that the machine-learning techniques or AI techniques can then access is paramount to success, and paramount to gaining recognition and appreciation by the end user. 

Another challenge that AI faces is in how it analyzes data. This is really focused around that black-box mentality. And one of the main challenges that these types of techniques have — or really any quantitative process can have — is around transparency. So there’s a field of AI called XAI, or explainable AI, super interesting, and it really seeks to provide more of an explanation as to what the model actually does. And this is important, because without understanding what the model does, interpreting the output can both be challenging, and also lead to incorrect assumptions about what is being recommended. 

A third challenge that any business faces is around implementation costs. When implementing artificial intelligence into an investment management process or ETF portfolio construction, one should not underestimate the cost associated with acquiring data and structuring data. I would again say that this is the single most important facet of a successful artificial intelligence or machine learning process is in data, data, data. 

The actual mechanics, the actual software or processes to run these types of analysis is increasingly commoditized, but the costs upfront to be ready to use those types of techniques should not be underestimated by any business or any user. 

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In my opinion, the outlook for integrating artificial intelligence into ETF portfolio construction is truly exciting. I see that the next wave of really building and developing, curating unique solutions for investors lies in the ability to leverage artificial intelligence, leverage the power of machine learning to create a more customized solution that meets individual investor needs.

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