The combination of factor investing and new technology is dramatically changing active management, according to a new report from State Street Global Advisors.
“We expect the advent of new data, tools and technology will give rise to a new species of active managers, increasingly looking at investment opportunities and risks through a factor lens,” says SSGA’s Fall IQ report, titled “The New Active.”
SSGA believes these three trends will have a big effect on active managers today and in the future.
1. Factor Investing
“Factor investing is disrupting traditional active management and raising the bar on managers to show how much of their return is true, skill-based alpha,” the report states.
It predicts that the factor-based process of natural selection will likely weed out many traditional active managers as well as some high-priced hedge fund managers.
According to the report, “factor investing provides a powerful lens for understanding the drivers of risk and return beyond traditional asset class categories.”
SSGA thinks the industry is at an important inflection point as factor investing contributes to the extinction of certain kinds of active managers whose factor exposures can be captured more cost-efficiently through smart beta strategies.
With interest in factor investing as an alpha generator growing, investors are keen to know how quant managers specify and incorporate factor signals into their investment models.
Vladimir Zdorovtsov, managing director of Active Quantitative Equity at SSGA, considers a “factor” more broadly as “a systematic decision criterion” or “a way to compare multiple investment opportunities using the same rule.”
When it comes to active factor investing, Zdorovtsov said, the quality of the factors is far more important than the number of factors used in a model.
“The point is that managers can end up with more factors than they really ought to have if they are not thorough and methodical when looking for improvements to their process,” Zdorovtsov said. “We believe you need to have a properly high bar for including a factor, as well as a process for revisiting and refreshing what is in the existing model.”
The volumes of new data continue to “boggle the mind,” according to the Rick Lacaille, chief investment officer at SSGA.
Every minute, 7.8 million videos are viewed, more than 3.3 million searches are entered, 151 million e-mail messages are sent and more than 436,000 tweets are posted, according to Internet Live Stats (cited by World Wide Web Consortium) as of September 2016.
“With real-time data literally pulled from the ether, we may be able to assess companies and markets far more quickly and with more granularity than ever before,” Lacaille wrote in the report. “All of this could be turned into a new class of investable information, heralding a new golden age of quantdriven active management.”
What will this new golden age of active quant management mean for the industry? Lacaille said access to data and the tools to harness that data will be “more important than ever.”
“Those asset management firms that have already made the necessary investments in data and technology will have an edge,” Lacaille wrote.
The combined forces of big data and technology will favor data-driven active managers, the report concludes.
3. Artificial Intelligence
AI is driving millions of dollars of investment in startups and research into a range of possible applications from strengthening internet search engines to building self-driving cars, according to the report.
What does this mean for the industry? Jean-Sebastien Parent-Chartier, senior quantitative research analyst at SSGA, is particularly focused on the “deep learning” aspect of AI.
“Recent breakthroughs in artificial intelligence, particularly in the area of deep learning, suggest that the new AI technologies could be poised to revolutionize research across any number of fields, including investment management,” Parent-Chartier wrote in the report.
The report defines deep learning as a “relentless research assistant.” Deep learning can accomplish a wide variety of tasks without human supervision and learn to recognize patterns through the act of processing vast quantities of data, according to the report.
SSGA’s Active Quantitative Equity team has made machine learning an important part of its big data innovation strategy to improve how it assesses the quality of data used in its models.
“We believe deep learning can measurably improve our ability to detect data anomalies and strengthen our ability to assess data integrity quickly and at a scale that was previously unthinkable with manual processes,” Parent-Chartier wrote.
Previously, data anomalies—which SSGA defines as any instance where an erroneous data item prevents its investment models from functioning as expected—were spotted using manual, hard-coded rules that were derived from common sense or as a result of lessons learned from previous errors.
“In the future, we believe that the combination of growing data sources and improved machine learning technology may revolutionize an active managers’ ability to identify and harvest new sources of alpha and open up a whole new chapter in our industry’s evolution,” Parent-Chartier wrote.