ML has begun to make inroads as asset managers realize that the ability to extract value from big data is going to be a key differentiator — and that traditional industry practices will struggle to stay afloat in this mounting flood of real-time data. Analytics using ML can be more robust than traditional financial modelling, as it can tap into the streams of "unstructured" (text-based) data that global digitalization and social media are creating, in addition to the millions of corporate press releases, conference-call transcripts and regulatory filings that are produced every year.
With the industry in a state of flux due to the rise in passive investing and the move away from commissions to level fees, many asset managers are investing heavily in technology to reduce operating costs and to comply with regulators' ever-increasing demands for transparency.
Leading asset managers are realizing that this also presents a solid opportunity to invest in advanced data analytics and ML capabilities, as well. With the fixed-income bull market coming to an end, fund managers need to begin to implement new investment strategies, reorganizing their operations around next-generation investment systems.
Asset managers are using predictive analytics to generate investment ideas or as an early warning system for assets at risk. At a minimum, A.I.-enhanced data analytics can complement traditional financial analysis by offering unique insights.
More from Portfolio Perspective:
Financial firms need tech to connect with younger clients
Amazon will be a trillion-dollar company: Advisor
Technology stocks can power up your portfolio
Merrill Lynch is experimenting with an A.I. stock-picking tool to help it identify value in small-cap stocks that conventional analysts might have missed. Because quant investment ideas are starting to have shorter expiration dates as trading signals get arbitraged away, BlackRock is steering its quant research toward ML and exploiting social media and web search information. After all, those quant ideas that do work can be turned into smart beta products, or even passive strategies that give exposure to specific return factors.
Still, apart from a few cases of smaller, leading-edge hedge funds, asset managers exhibit an understandable reluctance to place their portfolios and funds fully in the supervision of an ML-based robot. Recent work by researchers at the Wharton School of the University of Pennsylvania and the University of Chicago offer an explanation for this behavior, demonstrating a phenomenon they term algorithm aversion.
It was found that people will often refrain from employing algorithmic (computer-based) decisions even where such approaches demonstrate consistently better results than relying on human "gut" or "seat of the pants" estimations. Since many of the new ML algorithms yield answers that appear opaque, without an understandable explanation for their decisions, these systems feed into this distrust.