Sources of Performance Decay
Above all, the greatest fear in empirical finance is that the out of sample results for a strategy under investigation will be materially weaker than the results derived from testing. There is absolutely no doubt that a meaningful portion of observed out-of-sample performance decay is the result of arbitrage; that is, others discovering and concurrently exploiting the same anomaly. However, I think the biggest source of decay between in-sample and out-of-sample performance has to do with the concept of ‘degrees of freedom’. The next few articles will explore these important challenges and offer some thoughts on how to improve results.
One of the most interesting phenomena observed over the centuries in science is ‘multiple discovery’. This phenomenon, so named by noted sociologist Robert K. Merton in 1963 (not to be confused with Robert C. Merton, who won the Nobel Prize in Economics for co-publishing the Black-Scholes-Merton option pricing model), occurs when two or more researchers stumble on the same discovery at nearly the same time, but without any prior collaboration or contact. Historically, these discoveries happened concurrently in completely different parts of the world, despite little shared scientific literature, and significant language barriers.
For example Newton, Fermat and Leibniz each independently discovered calculus within about 20 years of each other in the late 17th century. Within 15 years of each other in the 16th century, Ferro and Tartaglia independently discovered a method for solving cubic equations. Robert Boyle and Edme Mariotte independently discovered the fundamental basis for the Ideal Gas Law within 14 years of each other in the late 17th century. Carl Wilhelm Scheele discovered Oxygen in Uppsala, Sweden in 1773, just 1 year before Joseph Priestley discovered it in southern England. Both Laplace and Michell proposed the concept of ‘black holes’ just prior to the turn of the 18th century.
The 19th and 20th centuries also saw a wide variety of multiple discoveries, from electromagnetic induction (Faraday and Henry), the telegraph (Wheatstone and Morse in the same year!), evolution (Darwin and Wallace), and the periodic table of the elements (Mendeleev and Meyer). Alan Turing and Emil Post both proposed the ‘universal computing machine in 1936. Jonas Salk, Albert Sabin and Hilary Koprowski independently formulated a vaccine for polio between 1950 and 1963. Elisha Gray and Alexander Graham Bell filed independent patents for the telephone on the same day in 1876!
Altogether, Wikipedia has catalogued well over 100 instances of multiple discovery in just the past two centuries. If the frequency of multiple discovery is related to both the speed of communication and the number of linked nodes in a research community (a hypothesis for which I have no proof, but that is logically appealing), then the concept of ‘multiple discovery’ has important implications for current investors in the age of the Internet.
For us, there is a clear analog in quantitative finance: researchers operating independently, but sourcing ideas from a common reservoir will almost certainly stumble on similar discoveries at approximately the same time. This dynamic will almost certainly lead to some performance decay once these strategies are put to work out of sample, and with real money, as all of these investors will be attempting to draw from the same well of alpha. Indeed, in a recent paper Jing-Zhi Huang and Zhijian (James) Huang demonstrate that published anomalies do exhibit meaningful performance decay after publication, though they do in aggregate preserve some of their pre-publishing lustre out of sample. Interestingly, they also identify some simple filters that help to identify which anomalies are ‘working’ over time as they pop into and out of existence.
Note that the anomalies explored by Huang and Huang relate specifically to equity selection. We believe active approaches to global asset allocation have several advantages over strategies aimed at selecting securities within a specific asset class, and that they are less vulnerable to decay as a result.
For example, most investors have a strong home bias and are not open to approaches that stray too far from stocks and bonds of their country of residence. Strategies that propose to be agnostic to home bias, and spend substantial periods invested in unfamiliar assets are unlikely to gain mass adoption.
More importantly, major asset classes represent enormous pockets of capital, on the order of hundreds of billions, or even trillions, of dollars. Markets this deep require equally deep sources of capital to arbitrage. Yet the current large sources of capital in global markets – pensions, endowments, and other institutions – are constrained in their ability to take advantage of the opportunity in this space in three important ways:
- Many of these institutions are structured along asset class lines, with resources dedicated to each asset class silo individually. Dynamic asset allocation might see one silo receive very little capital allocation for many months or years; it is difficult to lay off employees and recall them when their asset class is back in favour.
- Where asset allocation is implemented through outside managers, dynamically shifting across asset classes would require frequent redemptions and reallocations which may not align with longer-term security selection strategies. Many more successful strategies would not accept such active rotation in and out of their funds, and may choose to limit access to institutions who frequently reallocate.
- Many, if not most, institutions are managed by large boards with diverse experience and skill-sets. These boards meet infrequently, but are responsible for approving large shifts in strategy. It would represent a large departure from convention for a board to approve a meaningful shift into such a novel approach, in which most if not all of the board members have little to no experience.
For these and other reasons, we feel global multi-asset active allocation strategies have many strong years ahead of them, in contrast to many other strategies which may live and die very quickly because they do not possess the above characteristics.
In the next article(s) we will explore the impact of overlooked sources of investment returns, paying special attention to the impact of interest rates, and examine the myriad ways in which quantitative researchers ignore sources of potential bias in their models. We will also offer some thoughts on how to address these shortcomings.