Software never sleeps: You don’t have to worry about getting your broker on the phone if it’s a bot. And given conversational AI’s limited phone presence, you’re probably better off just texting with it. That was the sales pitch for robo-investing services a decade ago when they began going up against traditional advisors by applying algorithms to make and monitor investments.
But the rise of generative AI has enabled systems that can carry on open-ended conversations and at least appear to come up with original thoughts and creations. They are opening possibilities for more personalized financial services as AI evolves from a tool for fund managers to something that investors can interact with directly.
The bull case for that: AI will do everything robo-advising formulas did but with more nuance and greater awareness as it learns. “AI is capable of deep-learning algorithms, whereas robo advising was based on machine learning and algorithms,” writes Suchi Mishra, associate dean for faculty affairs and a professor in the finance department at Florida International University in Miami. Robo-advising will have to advance to the latest phase of AI, she says.
Investing AI in action
Q.ai, a new service from Jersey City, N.J.-based Quantalytics Holdings, pitches itself as a logical next step. It offers no-fee “investment kits” of four to 20 securities in a market sector. They are picked by an AI that assesses things like market metrics, news, Google search trends, and social-media sentiment.
As of July 7, Q.ai reported year-to-date returns for these kits that ranged from 52.36% for a cryptocurrency kit to negative 8.28% for a “Recession Resistance” offering.
ETF Managers Group’s AIEQ, launched in 2017, offers a longer history for comparison. The Summit, N.J. firm says it uses IBM’s Watson AI platform to analyze millions of data points from news, social media, industry, and analyst reports, plus financial statements on over 6,000 U.S. companies, and technical, macro, and market data, among others.
Over the last five years, the fund has returned 4.9%—trailing the 11.78% five-year return of Vanguard’s benchmark S&P 500 index fund. It also trails two large actively-managed funds: the American Funds Growth Fund of America, at 9.81%, and Fidelity’s Contrafund, at 11.04%.
Saying “research is still nascent in this area,” FIU’s Mishra pronounces herself unsure about whether AI-routed investing can beat the market. (In fact, any actively-traded fund, whether humans or bots click the “sell” buttons, can struggle to match index funds’ returns because equity sales in actively-managed funds incur capital gains taxes that don’t affect passively-managed index funds.)
Could widely distributed AI investing worsen market fluctuations? Pawan Jain, assistant professor of finance at West Virginia University in Morgantown, W.V., thinks we already live in that world.
“AI in investing has been in existence for a long period of time,” he says, pointing to how program trading (automated transactions triggered by preset conditions) accelerated the 1987 market crash, as well as the large role of high-frequency trading algorithms today.
However, the biggest fear many people evoke about AI is not the subpar performance and panicked trading that human managers already deliver. It’s the potential of new generative AI systems like ChatGPT to “hallucinate” or otherwise make stuff up.
AI investing and financial planning services often emphasize that they haven’t just handed over investor wallets to a machine-learning model.
AIEQ’s founders have noted that human employees monitor their AI output for signs of emerging bias. Art Amador, a partner in the fund, says that the company is developing a transparency tool that will allow banks and asset and wealth managers to check its data inputs and investment decisions. Q.ai, owned in part by Forbes Global Media Holdings Inc., makes similar points in its online FAQ.