On a day when none other than the Financial Times runs a headline that highlights central bank “distortion” in Japanese stock markets, a related event is noted. Just before Bill Gross took the central banks to the woodshed, calling Janet Yellen a master of market manipulation and pointed to ugly results, UBS Global Quantitative Analyst Paul Winter wrote a research report titled “When is the stock market likely to correct?”
This is almost a click-bait type headline, because for a banker to infer discussion of a particular market crash timeframe is a hot topic being handicapped among analysts. How do the dots connect, particularly as Winter discusses “asset bubbles” but does not use the words “central bank” in the body of his 29-page analysis?
Bank analysis can be insightful despite being free speech constrained
Bank analysts are said to have certain topics that “are no go zones.” There is no written documentation on the topic, but certain hedge fund managers are known to track correlations of bank analysis on politically significant or revenue sensitive topics and correlate it with the lack of news coverage to generate trade signals. The constraint put on free speech can also be documented in the compliance department, which would likely frown on Winter predicting an exact date for a market crash.
A bank being critical of a central bank – its regulator – is often frowned on in economic elite company, where there is at times an unquestioned deference to authority and an unwillingness to challenge the status quo.
Given what are assumed constraints on free speech – and some topics that are of a national security concern are seldom discussed in public, which perpetuates the problem – it is often interesting to see how a bank analyst can let their true thoughts show. Winter, for instance, didn't mention the potential for central bank created market instability, an inside topic de jour, but his report nonetheless provided significant insight.
The importance economic difference between a correction and a crash
“Are equity prices in a bubble?” Winter questions in an August 29 report where he attempts to define what causes markets to crash and points to where to invest. His solution – invest in certain stocks – is about as innovative as considering asset bubbles without clearly discussing “artificial bids” in the market.
With the obvious omission of the current market environment clear, Winter nonetheless provides a framework for understanding market corrections and crashes.
His most significant category definition starts at the top, where he separates corrections from crashes. Understanding market structure is best done at a conceptual, performance driver level first and here Winter does not disappoint.
A correction involves economic cycles – which are documented to have occurred through the history of markets and are part of an investment cycle. A market “crash” is something different.
A crash is a liquidity-driven event. Here Winter touches on interesting detail but doesn’t clearly define that in an algorithmic world sometimes non-economic factors trigger crashes. This is most famously on display during the Flash Crash of May 2010 – and regulators and the Department of Justice typically take a dim view on activities that threaten market integrity.
Winter considers the causes of market crashes, but does not specifically mention algorithmic disruptions as one potential cause. He includes Exogenous shocks, policy uncertainty, systemic risk and macro factor risk as crash causation points.
On the correction side he includes credit and earnings cycle as top categories, with credit risk an ever present feature in the modeling.
Since 1929 markets have seen many stock market downturns but only four primary liquidity crashes, Winters asserts. These liquidity crashes were: 1) The start of the Great Depression (October 1929) 2) Black Monday (October 1987) 3) The Tech Wreck (2000 – 2003) 4) The Global Financial Crisis (2007-2009).
One could quibble with the limited crash date analysis, but Winter uses Kyle’s Lambda formula to measure liquidity risk and drive parts of the analysis. Using more general economic terms, all four points Winter cites occurred as a result of the “consensus” being surprised. In 2008 one surprise was that derivatives, largest exposure in the bank’s portfolio, were so much larger than loan exposure and could sink the world economy.
Explaining certain innards of a market crash: The link between stock and bond markets tied together by volatility
While they are separate events, at times crashes and corrections can be correlated, particularly when a surprise economic event may change the entire financial universe as was the case in 2008. Often times surprise at a change in predictable behavior causes crashes.
From the UBS standpoint, the report interestingly notes the correlation between crash and correction:
In order to predict corrections or 'crashes', we need to understand the investment cycle. Bubbles tend to form during periods of excess liquidity. As a consequence, valuations become stretched and perpetuated through ‘rational bubble riding’. However, eventually the competitive landscape increases and costs pressures build, placing pressure on margins and squeezing earnings. At this stage, credit becomes more challenging to raise as lenders (witnessing the tighter environment) increase lending standards. As a consequence, credit spreads increase and place further pressure on company earnings. The net result is the end of the earnings cycle. As earnings come under pressure, the stock market starts to drift lower and multiples begin to compress. It’s at this point that the risk of a market correction increases. We make the distinction between a market downturn or ‘Bear Market’ and a stock market ‘Crash’ as the two have different drivers. A market downturn is driven by the credit and earnings cycle, whilst a stock market crash is a liquidity event.
This explains the argument and Winter takes it further, pointing to correlations between stock and bond volatility that was first discussed by algorithmic fund manager Cliff Asness:
…following Cliff Asness’s (2000) paper ‘Explaining the Equity Risk Premium’ we show the relationship between ten year earnings growth rates and the ten year volatility differential between equities and bonds (Figure 3), and subsequently the ten year volatility differential and the ten year average equity risk premium (Figure 4). The thesis simply put is that earnings growth risk drives the volatility differential between equities and bonds, and it is this volatility differential that explains the equity risk premium. Why use ten year averages? Quite simply the short term relationships between these variables are not stable. However, Asness theorises that there is a long term generational phenomenon whereby investors frame their risk preferences and return expectations in terms of their prior experiences, as a consequence, using ten year averages (Asness uses 20 year averages) captures this effect.
The point here is regarding valuations. Winter notes a divergence that he says will correct itself:
Given that earnings growth rates are currently running at -1.5% per annum, we should expect a volatility differential of 11% and an equity risk premium of around 6% (assuming historical preferences hold true).
That simply points to growth rates being ahead of reality. In a free market