Can artificial intelligence replace portfolio managers? That’s the latest question facing an institutional asset management industry grappling with the rise of passive strategies and a wave of consolidation, factors already disrupting traditional fee structures.

Now, AI is adding a new challenge: Learn how to harness this technology to boost investment performance or risk being left behind. Our research suggests that AI is not a silver bullet that can replace a portfolio manager anytime soon. What it can do, however, is improve existing strategies.

AI is an umbrella term for a group of interconnected technologies, including machine learning, neural networks and deep-learning tools that can make sense of large and growing pools of data. Our experiments suggest that AI is a tool that can make a good portfolio manager better, particularly with portfolio construction.

In one experiment, we analyzed 70,000 news articles published between 2004 and 2018 on 740 companies and used natural-language processing to calculate a net sentiment ratio for each firm. For several years, short-term positive news sentiment was associated with investment outperformance. However, outperformance evaporated around the year 2011, perhaps because the opportunity was arbitraged away as a result of machine readable news products becoming relatively commonplace.

Neural networks

In another experiment, we “trained” a neural network to augment a traditional momentum investing strategy, by feeding it five years of data on Russell 3000 stocks in order to identify patterns. The resulting AI-enhanced strategy produced returns that closely tracked a traditional momentum strategy. However, it generated a significantly superior information ratio, a measure of the risk-adjusted return of a portfolio: The experiment produced an information ratio of 2.6 versus 1.25 for a traditional strategy.

Why can AI improve and enhance an existing investment approach, but struggle to create entirely new strategies? First, financial markets, surprisingly, don’t produce enough data to get the most out of AI and machine learning: AI functions best on billions of data points rather than millions, but three decades of daily share-price data for the benchmark S&P 500 Index SPX, +0.29% would only produce fewer than 4 million data points (and that would assume all constituent stocks remained the same, which they have not). In addition, humans are better at spotting patterns in very small data sets than any machine learning tool.

That’s because humans are better at a process called transfer learning; using their real-life intuition, experience and context to apply knowledge from one circumstance to another. For example, a machine could generate a signal to buy or sell shares of a given company, based on years of stock-price data. However, what happens when that company names a new chief executive? For an algorithm, the leadership change is factored into the price as just one more data point. For a human portfolio manager, it can change the entire equation.

Tech spending

There is an urgency for asset managers to invest now in AI because there is a strong correlation between investing in technology and profitability: Between 2014 and 2017, asset managers with profitable growth increased technology spending 7% annually, focusing that investment in proprietary technology, compared to only a 2% annual increase for the average firm, according to a study by Casey Quirk. The report notes that data today remains “probably the least optimized resource across most asset management firms.” That’s why Boston Consulting Group warns that firms must invest now to harness AI and big data or “they will fall even further behind the front-runners.”

As a practitioner of quantamental investing (quantitative research paired with fundamental analysis), more than two decades experience with AI has taught me that the best use for this technology in asset management is to incrementally improve existing investment processes and methodologies.

That starts by accepting that AI is not designed to hit stock-picking home runs, find needles in haystacks or to work independently. Instead, it works best when humans develop an investment thesis and machines test that theory. For example, AI could help identify a cohort of 100 stocks that statistically exhibit certain characteristics and a human portfolio manager could improve performance by excluding potential bad apples among that cohort, based on context and experience. The end result is tilting a portfolio to favor stocks with the highest statistical probability of outperformance while underweighting stocks with a lower probability. The goal is not a “eureka” moment but consistent, diversified, additional gains.

What’s more, asset managers must avoid the temptation to look for short cuts by hiring external firms for this sort of work. Building AI capabilities is better done in-house because AI-driven algorithms need constant human supervision, feedback and adjustment. At the same time, incentives between investment teams and external vendors are never correctly aligned. Vendors have an incentive to develop solutions that look good on paper but may be short-lived or the product of problematic data mining. Similarly, to encourage collaborative and agile progress, it’s better for AI teams to work in close proximity with investment professionals, not housed in far-away centers of excellence in remote locations.

Data scientists

AI may speed industry consolidation because building AI solutions requires a sizable team, perhaps two dozen or more data scientists and PhDs. The costs of such hiring naturally favors asset managers that enjoy the economies of scale that come with a certain size, perhaps $200 billion or more of assets under management. As larger firms staff up and gain an AI-driven edge, smaller boutiques may struggle, possibly leading some to seek mergers and others to evolve in an effort to survive.

The good news is that when building AI systems, asset managers don’t have to reinvent the wheel. Firms can take advantage of myriad off-the-shelf platforms, adding an overlay of proprietary customization and algorithms. Smart firms can also harness the energy and ideas of startups by hosting hackathons or other similar events. While generic technology will produce generic results, technology tailored in the right way for modelling and portfolio construction can help produce performance that will stand apart from the pack.

Michael Heldmann, PhD, CFA, is chief investment officer for the U.S. Systematic Equity team that represents the Best Styles factor investing strategies at Allianz Global Investors for the U.S. market. As of March 31, $45 billion of assets under management were managed under the Best Styles strategy platform globally.