Technology Disruption and the Impact on Financial Analysts
Technology is a disruptive force that creates challenges for established business models in many industries. Job losses in manufacturing are largely attributable to advances in technology, but the impact of technology isn’t confined to manufacturing-related industries. A 2015 McKinsey study estimated that 45% of job activities could be automated through robots or other machines. Aspiring financial analysts enter a world in which technology will be a catalyst for significant changes. I think CFA® Program candidates face a good news/bad news environment given advances in artificial intelligence, big data, and machine-learning technology. The good news is that there will still be a role for humans in the profession; the bad news is that investment professionals will need to adapt to a world in which “routine” work will largely be automated away.
We Will Never Again Have a Middle Class Built on Routine Work
This blunt opinion was shared in a recent conference session about investing in a post-global world. Many trends that disrupted manufacturing are changing the investment industry as well. For example, entry-level analysts used to spend most of their time working on routine tasks. They built earnings models, with significant time devoted to gathering input data. The process was labor intensive, often requiring the analyst to manipulate data that wasn’t in standardized form or that was provided by multiple incompatible sources. The work was necessary, but often mind numbing in its tediousness! Model building is much easier today, helped by increased availability and standardization of data, as well as improvements in the databases used by investment firms.
Analysts also spent considerable time gathering subjective data to supplement reported data or to identify changes in business momentum. I remember a research trip to London during which I was impressed by a store visit to Zara (which at that point was newly public) while being absolutely depressed by a dismal visit to Marks & Spencer. Although I feel a sense of nostalgia when thinking back to those days, today there are systematic and more effective ways to gain insight about sales momentum, store traffic, and inventory levels.
Investment technology goes beyond eliminating routine data-gathering tasks. Machine-based systems answer quantifiable questions faster than a human, and they rapidly analyze multiple dimensions of a problem. Computers have replaced humans on trading floors, and investment managers increasingly use algorithms to identify securities to buy and sell.
Humans Are Not Obsolete!
Although technology is integral to automating routine tasks and for identifying patterns in large datasets, the investment industry continues to need workers capable of analyzing data, exercising judgment, and evaluating the effectiveness of quantitative algorithms. A due diligence meeting earlier in my career provided a lasting lesson on the importance of human judgment. In a discussion with a market-neutral hedge fund manager, I asked about a period of performance that deviated dramatically from our expectations. The portfolio manager responded with an explanation about how their risk model assumed that technology and industrial stocks would be highly correlated with each other. Consequently, the fund was long “cheap” industrial stocks, while being short “expensive” technology stocks. In assuming that the two sectors would be highly correlated, the risk model calssified the long–short positioning as market neutral in the aggregate. Unfortunately, that positioning turned out to be disastrous during the dot-com boom. The lasting lesson for me was to realize that seemingly sophisticated quantitative models require thoughtful human design.
Quantitative models are often superior to humans in looking through the rearview mirror at large amounts of data, but humans still may be better equipped to identify future trends. Artificial intelligence, big data, and machine learning may be less effective when outcomes are uncertain and subject to a high degree of randomness. There are numerous variables that influence the direction of markets, and it is easy to underestimate the human element of judgment. Investor sentiment, government policy, geopolitics, and “luck” (including weather and other random influences) all may play a significant part in explaining investment performance. In many cases, decisions must be made in a context of unexpected developments, infrequent in nature, and with limited historical data. Even the smartest artificial intelligence program might not envision the innovative destruction of Amazon, Apple, or Uber. Consequently, artificial intelligence may not ever be a replacement for the judgment of a Warren Buffett, George Soros, or Janet Yellen.
The key to thriving in a technology-enhanced investment industry is to develop strong analytical skills and demonstrate sound investment judgment. Developing and exercising investment judgment isn’t an easy or natural process. The automation of routine tasks is mostly a good thing, but it does eliminate some of the training benefits that came from some of data gathering and manipulation tasks that for decades were part of the rite of passage for entry-level analysts. A particular challenge is in going from an environment of certainty—academic settings, including the CFA Program—in which there are right and wrong answers, to an investment environment, in which most decisions are made with incomplete and often ambiguous information. That’s a tough transition for many, and requires conscious effort and a healthy dose of humility!
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About the Author
Daniel Kern, CFA, CFP, is chief investment officer for TFC Financial Management, a wholly independent, fee-only, financial advisory firm based in Boston. TFC's revenues are derived solely from the fees it charges for the services it provides. Prior to joining TFC Financial Management, Dan was president and CIO of Advisor Partners. He is also a former managing director and portfolio manager for Charles Schwab Investment Management, managing asset allocation funds and serving as CFO of the Laudus Funds, and was managing director and principal for Montgomery Asset Management. Dan graduated from Brandeis University and earned his MBA in finance from the University of California, Berkeley. He is a CFA charterholder and a former president of the CFA Society of San Francisco, and sits on the board of trustees for the Green Century Funds.