“How iTunes Crushed Music Sales” (CNNMoney)
“Five Ways That Amazon Is Shaking Up Retail” (Forbes)
“Uber has pretty much destroyed regular taxis in San Francisco” (Time)
“Extreme Moneyball: The Houston Astros Go All In On Data Analysis” (Bloomberg)
The narrative is familiar: Technology innovation disrupts legacy business models, creating opportunities for insurgent competitors and existential threats for less nimble incumbents. The threat to investment management may be less obvious than to music stores, retailers and taxicabs, but the impact of technology is notable in a wide variety of investment activities.
Active stock-pickers dominated equity markets for decades, with star managers such as Peter Lynch, John Templeton, Michael Price and Bill Miller dominating headlines and asset flows. In recent years, however, active managers have been attacked from all sides, losing market share to index funds as well as quantitatively based strategies benefiting from advances in computing power.
‘Quant’ strategies may be loosely defined to include smart beta, factor-based and quantitative alpha strategies. Smart beta strategies use measures such as revenues or volatility to create rules-based alternatives to conventional capitalization-weighted indexes. Factor-based strategies draw from academic research, emphasizing factors that have historically provided performance “premiums” relative to the market, such as style (value stocks), size (small company stocks), quality (high profitability stocks), and momentum. Alpha-oriented quant strategies resemble active stock-picking approaches, but replace human analysts with computer algorithms.
Quantitative Investment Strategies
I’ve seen the good, bad and ugly of quantitative strategies over the past two decades. The good is represented by firms that demonstrate thought leadership and have delivered strong long-term track records. Quantitative strategies come in all shapes and sizes, and the best I’ve seen constantly test their assumptions, investment processes and rationale behind their investment strategy. Renaissance, Arrowstreet Capital, AQR, Dimensional Fund Advisors and Research Affiliates are among the most-admired firms in the quant world.
The bad was on display during the ‘Quant Quake’ of August 2007, when many alpha-oriented quant funds fell sharply. Quant funds were using common metrics of value and momentum to identify opportunities, leading to considerable ‘crowding’ of investments. As a liquidity driven selloff began, the race to the exit by quant funds started a slump for quant that lasted through 2009.
The ugly is represented by the near-collapse of Axa Rosenberg, a firm that shrank dramatically due to the combination of disappointing performance and the cover-up of a ‘coding error’ that compromised the firm’s investment model.
I recall another revealing episode from a due diligence meeting with a large market-neutral hedge fund in 2001. The fund had performed poorly in the late 1990s, and the portfolio managers blamed the poor performance on shortcomings in the firm’s risk model. The risk model considered technology stocks and traditional cyclical stocks to be equivalent in risk to one another.
Consequently, when the fund’s investment selection model “loved” cheap cyclical stocks and “hated” expensive tech stocks, the risk model considered the long position in cyclicals to offset the short position in technology.
In reality, this positioning was far from market neutral and proved to be disastrous in 1998 and 1999 — serving as a reminder that analytical models (and the humans that design them) aren’t infallible.
Moneyball’s Relevance to Quant Investing
A book about baseball illustrates how innovators can disrupt established business practices; subsequent events in baseball show that gains from innovation may be short-lived as competitors copy successful strategies.