Back in 2013 two economists Rakesh Sarin and Manel Baucells published a book entitled Engineering Happiness, which included the solution to the “fundamental question of wellbeing, an incredibly simple equation”; a rather grandiose proposition if ever there was one.
Happiness = Reality – Expectation
Anyone who has been dragged out to a dinner party with very low expectations that it will be fun only to be pleasantly surprised at the end of the night can attest to the simple, yet powerful, truth of the equation. However, the difference between reality and expectation is not only the key to happiness, but it is also the key to profitable investing.
In elementary economics classes, we are all taught that at its most basic level economics is about understanding how scarce resources are allocated. To this end, the first task students are invariably set is to derive, from first principles, downward sloping demand and upward sloping supply curves. The result: a unique and, importantly, stable equilibrium – a huge positive for a profession that is keen to show its mathematical rigour in comparison with the other social sciences.
There are considered to be few real world instances where these demand and supply assumptions are invalidated. In fact, the only examples cited in textbooks are so-called Giffen goods, which have upward sloping demand curves due to a highly unusual – therefore rare – set of circumstances.
Unfortunately, while this model provides a useful framework for analysing most markets for goods and services, it is woefully inadequate for the analysis of financial markets. The reason: investment is a speculative activity, and hence investors are not concerned with a given asset’s price today per se, but on its future expected return (which is only partially a function of current price).
In an ideal world, fundamental analysis would be both a necessary and sufficient condition for successful investing. However, as we have pointed out previously valuation is a poor timing tool given the repeated instances of prolonged market overshoots or undershoots. What’s more, because investors operate in a world of uncertainty and incomplete information there is, necessarily, a large subjective element in any investment process – even for those investors that purport to be entirely quantitative.
It is a crucial point because it means that, just like happiness, what is important for investors is not what happens (reality) but what happens ‘relative to’ what most people thought was going to happen (expectation). It is the “surprise” ‘relative to’ the consensus that moves markets. After all, if an economic release is predicted accurately what additional new information would be revealed potentially prompting investors to alter their positions? None.
In an attempt to deal with these problems most asset managers adopt a multi-pillared investment process, incorporating various inputs including, but by no means limited to,: valuation/fundamentals, risk regime identification, price momentum, technical analysis, investor flows and positioning.
Of all these inputs, price momentum is the most pernicious because it has the tendency to create feedback loops, or as Cullen Roche wittily observed:
“The stock market is the only market where things go on sale, and all the customers run out of the store.”
For investors having to make real-time decisions in the face of a lack of timely fundamental information (for example earnings reports, macroeconomic releases, etc. which tend to come out only monthly or quarterly at best) is frankly difficult. Therefore, it is hardly surprising asset price trends – available in real-time – are used to try and fill the information void. After all, a rising price is indicative of an excess of demand over supply implying it could also be indicative of a fundamental change not yet evident in publically available non-price data.
That said, the major drawback with momentum investing is that it is tough to exit before the prevailing trend (either bullish or bearish) reverses. In the absence of alternatives torturing price data to generate second or even third order derivatives to look for signs of a trend, exhaustion is one method that has been used to attempt to alleviate this problem. Now thanks to technological innovation new alternatives exist. One of the most obvious being sentiment data extracted via automated processes from the millions of financial articles and social media comments published daily.
A key output from sentiment analysis is a quantitative measure of crowd optimism. Crowd optimism can be readily used to measure a price trend’s vulnerability on the observation that high optimism (euphoria in the graphic below) suggests that a lot of good news is “in the price already” and hence the uptrend is vulnerable and vice versa.
Sentiment data can also be a useful complementary input into an investment process in other ways such as providing high-frequency insights into an economy’s fundamentals. Not so long ago the only way to get gauge growth expectations was from consensus estimates derived from monthly surveys of individual economic forecasters. Now, by contrast, it is possible for investors to monitor public perceptions towards economic growth (and numerous other topics such as inflation, monetary policy, fiscal policy, etc. ) on a daily basis.
Recalling Keynesian “animal spirits”, sentiment can influence future economic growth trend, but it can also be affected by past economic growth trends given the potential for historical “surprises” (reality minus expectation once more!) to prompt a reassessment of future growth perceptions. So, rather obviously, sentiment data is much more useful than just providing a simple GDP growth proxy and examining the interactions between the two will be a rich area for future research. That said, what is readily apparent from the chart is that the issues with Q1/Q2 seasonal patterns that has plagued the official US GDP data releases, and which has prompted economists to focus on two-quarter averages to smooth out this effect to gauge underlying momentum in the economy, does not show up in the sentiment data. More significantly, the US sentiment data showed a consistent deterioration in economic growth over the past couple of years – a deceleration subsequently confirmed by the official GDP growth releases (excluding the seasonal quirk just mentioned); a trend many investors would, no doubt, have appreciated an early read on.