What Can Quant Firms Learn From Steve Jobs
I was very sad to hear the news that Steve Jobs- the former CEO of Apple Computer– passed away today. His life was an incredible story of innovation, and the ability to triumph in the face of constant adversity. The legacy that he left cannot possibly be missed– you can’t walk outside or inside for more than a minute without seeing someone use an Apple product. There is literally no other consumer brand in the world that enjoys the same degree of loyalty and enthusiasm as Apple Computer. There is a good reason for that: Steve Jobs was focused on creating the best products with no compromises—even at the expense of delaying a launch in a world driven by quarterly earnings. Furthermore, he furnished these products with functionality that consumers didn’t know they needed until it was available. Jobs did not focus on market research and on providing customers with what they wanted, instead he brought them products from his unique vision of the future. It was the act of daring to be different, and focusing on value instead of money that set Jobs and Apple far away from the competition.
This philosophy extends to all industries, and especially those that are driven by intense competition, technology and innovation. Of course, quantitative investing fits in perfectly within this category. So what can we learn from Steve Jobs? He was a man that wasn’t fond of rules or the status quo. Jobs would run his business based on his vision of how technology would evolve in the future rather than how it actually was in the present. He was willing to risk everything in order to best shape his products with this vision. If he were in finance he would quickly recognize that greatest rewards will accrue to those that can create innovative algorithms or technology. It is very easy to imagine him seeing the value in a quantitative approach to investing. Furthermore, Jobs would spend less time studying what others are doing in the field and focus on taking a completely novel approach. He would hire talent and promote risk-taking at every level of the organization. Furthermore he would invest extensively in research and development and avoid using a management accounting approach to evaluating its budget.
In the world of “quant” investing today,there are several major barriers to duplicate this approach: 1) a true lack of diverse and creative talent 2)a lack of consistent commitment to an ongoing research and development 3) embedded philosophical barriers to innovation . The first problem is driven by the type of “left-brained” people that are attracted to and consistently employed in finance: math, engineering and physics. This makes sense because of the high intellectual and technical barriers that exist in modern quantitative finance. However, the saturation of Phds and math wizards creates a talent pool that often lacks both diversity and creativity. While it is true that creativity can come from any background, it is certainly less likely to come from those that have cognitive wiring that is the polar opposite of those people that are typically artistic and creative.
Another challenge is that it is much more difficult to tangibly measure the value of financial innovation unless one is committed to the process. It is easy to measure the value of programmers or the mathematicians that create core programs. In contrast, adding alpha requires incrementally longer time frames to evaluate accurately as the trading frequency becomes lower. However, evaluation is a critical part of business decisions to increase R&D expenditures. That explains why hedge funds,high frequency firms and investment banks continue to hire top scientists while the rest of the investment industry fails to invest in talent.
A final achilles heel resides in the psychological, political, and philosophical barriers to innovation. In all areas of finance, the cost of miscalculation or being “wrong” is so high that human nature causes us to seek out those who are less likely to screw up the math. Having worked with many Phds and other bright individuals, the fear of being wrong or even doing math that is not conventional is so strong and pervasive that they are psychologically unwilling to break from convention. Of course, the very essence of the creative process requires risking being incredibly wrong in order to find a better way to do something. This can mean that a quant can lose their job for introducing a novel idea that doesn’t pan out, but will probably keep their job if they use GARCH or Fama-French and mess up. Clearly between the type of employee that gets hired, fear of math mistakes, and the obvious politics involved, innovation has many hurdles to overcome.
We need to stop worrying about being wrong— in a competitive game there is no alpha in being conventional and avoiding mistakes. There is no such thing as the “right” way to do anything anymore. In quantum physics we are continually exposed to how little concrete information we truly possess. Why should a vastly more unpredictable field be any different? Determinism and mathematical proofs are rapidly losing their value in finance. There is no fixed reality, and the highly dimensional nature financial problems today require both tremendous statistical skills but also the unique and artistic insights that drive the hypotheses to be tested. The new brand of quant firms that will succeed will integrate both approaches under one roof.