Artificial intelligence (AI) is gaining widespread attention for its ability to be a disruptive technology that spans across a variety of sectors. In the financial space, AI can be used to perform risk-reward analysis, fraud detection and advisory services, but how does the technology specifically serve exchange-traded funds (ETFs)?
ETF Trends Publisher Tom Lydon spoke with Yasmin Dahya and Joe Staines, of J.P. Morgan Asset Management on how this transformative technology is being utilized by ETFs.
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Evolution Versus Revolution of AI
Staines cites two ways that AI can materially impact ETFs–the first being the innovations that currently appear in today’s technology, such as those implemented by tech giants like Google and Amazon, will make their way into ETFs. Second, the implementation of AI can be actively incorporated into daily activities with respect to portfolio management.
“Everything that technology has done as a whole to investing as a sector over the last few decades, we’re going to see the same sort of innovative impact from machine learning and AI,” said Staines.
Of course, when such a transformative technology like AI is introduced into a financial industry that can be reticent to change and stuck in tried-and-true ways, it can present a challenge. In fact, as Dahya points out, the first AI conference was in 1956 so the technology has been around for some time–with its slow adoption, Dahya cites that incorporating AI will be more an evolution as opposed to a revolution.
“What I think is happening right now if more of a focus and a dialogue,” said Dahya. “What that sort of means though is you have to separate the noise from the truth a little bit.”
AI Uses by Investors and Portfolio Managers
With the availability of AI and its capabilities now being filtered down to the masses, investors can use these tools to incorporate in their own research in order to filter out opportunities. Likewise, portfolio managers and advisors now have access to AI technology that can be built into the financial products, so that investment decisions are made easier by parsing out key points from complex data.
“You’re seeing more and more products right now using AI-driven processes,” said Staines. “Incorporating those can give you an aspect that extends what systematic investing is capable of.”
Of course, as the old adage goes–time is money–that can certainly prove to be beneficial with the incorporation of AI into financial products. By expediting the investment decision-making process even more, portfolio managers and investors can reap the benefits of the efficiency.
“Being able to do the thing you need to do, but faster, better and more precise,” said Dahya.
One area seeing innovation is machine learning. This innovation is able to learn the investment process of humans, harness their nuances and even adjust them over time as processes change–this helps to also remove any investment biases that may be preventing an investor from seeing through the noise.
“By employing a system such as AI, we can attempt to mitigate those types of biases,” said Staines.
“This idea that systematizing the investment process and taking human emotion out of the day-to-day decision making is supplemented by having access to data,” Dayha added.
JP Morgan’s Use of AI
JP Morgan has certainly been realizing the benefits of AI through their own processes. One way is through their quantitative and arbitrage teams that can filter through copious amounts of data to identify opportunities, such as mergers and acquisitions.
AI technology has also permeated other parts of JP Morgan’s business in the broader asset management areas. Moreover, the technology is making its way into the actual products themselves–ETFs.
“What’s most exciting to me about this is it’s no longer just used in people’s portfolios, it’s available in ETFs,” said Dahya.
“To me, it’s the truest form of democratization that this type of technology is finding its way into (investment) vehicles that anybody can buy with a click of a button,” said Dahya.
Furthermore, JP Morgan and the whole financial sector is barely scratching the surface in terms of incorporating AI into their business processes. As the technology continues to develop and fine-tune its strategy, more widespread adoption is on the horizon.
For Staines, who has a doctorate in machine learning, this has been a long time coming.
“As with many academic fields, theoreticians get way ahead over practitioners in this space, so what we’re seeing now is a realization of all the ideas we in academdia were proposing many decades ago,” said Staines.
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