Question: what is significant about the dates February 1996, October 2015 and January 2017? 
Answer: these were the dates on which, for the first time, a computer beat the world’s best at chess, the Chinese board game ‘Go’, and poker, respectively. How long will it be before computers are beating the world’s best fund managers?
I have a general fascination with the artificial intelligence systems (computers) that lie behind these three momentous conquests. But I also have a particular interest in relation to whether my job as a fund manager will at some point be usurped by a computer.
I’m 51 and so do not worry too much about my own personal situation in this regard. Although the active management industry is under pressure and will continue to be, it is not going to disappear in the next few years. If anything, the relentless shift towards passive investing and, more recently, so-called smart beta, has helped to promote the value of truly active management. But artificial intelligence is to passive investing what the computer chip was to the internal combustion engine, and I do wonder how my younger colleagues will fare a decade or two from now in their battle with silicon.
To assess this question effectively, it is important first to consider how active management works. There are many well-renowned academics who believe that it is impossible to ‘beat the market’, and thus that active managers are in effect tossing coins with each other (and being paid by hapless customers to do so). This is also a position endorsed by practitioners in the passive investment industry (though presumably passive fund providers believe it is possible to beat their competitors).
Market inefficiency, or the lack thereof, is thus framed in objective terms, with consistent index-beating performance being beyond the reach of everyone. Herein lies my beef. I think market efficiency should be framed subjectively not objectively. In other words, there are some individuals who can beat the market; in the same way, there are individuals who tend to be good at poker.
If stocks and markets truly followed a random walk, I would be the first to hang up my boots. But they don’t.
Randomness in markets means that price movements are not dependent on previous price movements. Coin tossing is a good example of this – no matter how many consecutive heads are tossed, the probability of another head is still 50 per cent, biased coins excepted. Non-randomness, also known as ‘pattern’, means that there is dependence.
The two most common patterns in markets are ‘momentum’ and ‘mean reversion’. Momentum means that if the market price moves in a particular direction, the future price is more likely to move in the same direction. With mean reversion, the price is more likely to move in the opposite direction. These patterns exist in financial markets, over both short and longer timescales.
Furthermore, randomness and pattern can co-exist – there will always be a ‘noisy’ element to prices. In fact, randomness tends to predominate.
What I as a fund manager seek to do is to identify patterns that are bold enough for me to take advantage of, having assessed that in all likelihood they are patterns that will persist i.e. continue into the future.
In the case of asset allocation, the patterns that I believe I can take advantage of relate to the business cycle, namely the tendency of unemployment to rise and fall in a somewhat predictable manner. Take a look at the chart below of the unemployment rate in the US, then tell me that looks random.
US unemployment rate
Source: Bloomberg
I also suspect it is highly likely that unemployment will continue to rise and fall as it has in the past. In fact, the chart above suggests that the cycle has become more discernible in recent decades not less!
In their 2009 paper titled “Dynamic Strategic Asset Allocation – Risk and Return Across Economic Regimes”, Robeco’s David Blitz and Pim van Vliet set out a framework for using the business cycle to inform tactical asset allocation (what they call ‘dynamic strategic asset allocation’). They mapped the four phases of the cycle (expansion, peak, recession and recovery) to the performance of various asset classes, using data going back to 1948. Their study revealed some interesting results, which are set out in the table below.
Annualised returns in excess of cash (%)
Source: Dynamic Strategic Asset Allocation – Risk and Return Across Economic Regimes
Excess returns from equities ranged from 0.2 per cent per annum during ‘peak’ phases to 10.2 per cent during ‘recession’ phases. Bonds performed worst during ‘expansion’ phases. These are empirically derived results, but they are also logical. Expansion phases tend to see both inflation and central bank policy rates rising, which naturally is bad for bonds. Peak phases see tight monetary policy begin to impact economic growth, which is bad for equities, while recession phases see the opposite (readers may be interested to know that we will very likely continue to reduce our funds’ equity targets over the next two years, in anticipation of the onset of the next global recession in or around 2020).
The second part of this article can be found here.
Peter Elston is chief investment officer of Seneca Investment Managers. The views expressed above are his own and should not be taken as investment advice.