You Don’t Have Alpha
Gappy claimed in a thoughtful comment to P-Q Convergence that and is a “false dichotomy” and referenced as justification, among others, the standard finance doctoral textbooks on modern asset pricing (e.g. Cochrane, Singleton, and Duffie). This claim motivated Quantivity to revisit the Chicago school, for which the academically-trained practitioner inside had developed a fairly strong pragmatic aversion to many years ago (e.g. does anyone grounded in the real world seriously believe discounted dividend / cashflow models, or “factor” and “styles” given the anomalies?).
This revisiting led to Cochrane’s recent AFA 2011 Presidential Address on Discount Rates (also available on video). This address is particularly remarkable juxtaposed against French’s 2008 Presidential Address on The Cost of Active Investing which echoed the previous generation of the Chicago school by ardently defending the passive investment grail (imagine having to defend that in the middle of financial meltdown).
Cochrane’s address includes one of the best quotes in the history of modern finance, worthy of reading in its entirety by every serious practitioner (p. 51):
I tried telling a hedge fund manager, “You don’t have alpha. I can replicate your returns with a value-growth, momentum, currency and term carry, and short-vol strategy.” He said, “‘Exotic beta’ is my alpha. I understand those systematic factors and know how to trade them. You don’t.” He has a point. How many investors have even thought through their exposures to carry-trade or short-volatility “systematic risks,” let alone have the ability to program computers to execute such strategies as “passive,” mechanical investments? To an investor who has not heard of it and holds the market index, a new factor is alpha. And that alpha has nothing to do with informational inefficiency.
Most active management and performance evaluation just is not well described by the alpha-beta, information-systematic, selection-style split anymore. There is no “alpha.” There is just beta you understand and beta you don’t understand, and beta you are positioned to buy vs. beta you are already exposed to and should sell.
While Quantivity may quibble with Cochrane’s terminology, this sentiment is not far off from the views of hedgie friends. In fewer words: abnormal returns reward regime-sensitive risk premia traded via established systematic trading methodologies.
Although Cochrane’s purpose in his Address is to set forth a proposed research agenda for the field, a more interesting way to read his remarks is with a forward-looking quant practitioner lens. Rather than spoil the fun for readers wanting to interpret this Address for themselves, commentary here is limited to a few observations:
- Cyclicity: profitable trading can be taxonomized into unified logical frameworks which follow a cyclic knowledge diffusion curve (risk premia being the most recent), where new ideas evolve from highly profitable to commodities to eventual theoretical reconciliation by academia (and potentially reborn, per Gappy’s comment claiming recent resurrection of fundamental-based returns)
- Incongruence: financial industry apparatus built to support the Fama-French world is increasingly misaligned with this evolution in framework
- Crowding: deep quant analysis of strategy crowding (in contrast to classic behavioral herding) is arguably most valuable in the present era of commoditized risk premia strategies
- Blogosphere evolution: many excellent finance blogs (see blogroll) are dedicated to a single strategy from the current risk premia framework, whose profitability will continue to fall as the intellectual returns to scale decrease due to further strategy commoditization and more disgruntled buy-and-hold investors transition to be noise traders
Finally, all these points echo Quantivity’s belief that finance is ultimately a self-fulfilling prophesy: what trades with edge is far more a derivative of what the masses believe than any intrinsic econometric truth. Perhaps this also speaks to econophysics: there are normative mathematical models, but they are not time-invariant (but, we already knew this from Naïve Backtesting is Bogus).
Depending on reader interest, subsequent posts may discuss implications in further detail.