John Hussman strategic growth fund is getting absolutely killed in the current bull market but he remains one of the clearest economic thinkers I follow. His research is top draw here are some interesting insights:
“it’s tempting to believe that low interest rates “justify” elevated equity valuations. But as one can show with any straightforward discounting method, even another 5 years of zero short-term interest rates (compared with a more typical 4% short-term yield) would only justify valuations about 20% above historical norms – essentially 5 years x 4%. Instead, current U.S. equity valuations are about 112% above historical norms on reliable measures. To justify current equity market valuations, interest rates would need to be held at zero for the next quarter century. Understand that while suppressing short-term interest rates may encourage yield-seeking speculation that results in rich stock valuations, those rich valuations are still followed by dismal subsequent returns. Emphatically, low interest rates do not raise the future return on stocks – quite the contrary.”
Here are some further insights Hussman brings from Daniel Kahneman.
So why do policy makers so wildly overestimate the real economic effects of monetary policy (while vastly underestimating its effects in distorting financial markets)? In his book, Thinking, Fast and Slow, psychologist and Nobel laureate Daniel Kahneman describes the biases and rules-of-thumb that people often use to estimate the impact of one piece of information in explaining another. When presented with some piece of evidence, some judgements rely on precise calculations and historical estimates. Others, Kahneman writes, “arise from the operation of heuristics that often substitute an easy question for the harder one that was asked… As a result, intuitive predictions are almost completely insensitive to the actual predictive quality of the evidence.”
Kahneman describes the way that these intuitions give rise to inaccurate predictions. First, some piece of evidence – the stance of monetary policy – is provided. The associative memory quickly constructs a story that links the evidence to whatever is to be predicted – the most likely story being that easy monetary policy will boost the economy, while tight monetary policy will slow it. The next step, says Kahneman, is “intensity matching.” The flimsy evidence is ranked in intensity, and that same intensity is used to produce the forecast for the variable to be predicted. So regardless of whether monetary policy is actually correlated with the economy or not, we naturally assume that extreme monetary policy should have similarly extreme effects on the economy, and in the expected direction. As Kahneman writes, “Intensity matching yields predictions that are as extreme as the evidence on which they are based, leading people to give the same answer to two quite different questions.” In this case, one question is “how easy is monetary policy?”, while the other is “where is the economy headed?”
The problem here is that the quality of the evidence – the strength of the correlation – is not being considered. Kahneman offers a way to improve on these intuitive predictions. In the present context, that method would go something like this: 1) Start with an estimate of economic growth in the absence of any monetary intervention; 2) Estimate the rate of economic growth that best seems to match the intensity of monetary policy; 3) Estimate the actual correlation between monetary policy and economic growth (hint: about 0.15); 4) If the correlation is 0.15, move 15% of the distance from the baseline GDP growth to the GDP growth matching monetary policy.
[Geek’s note: You can show statistically that if Zy and Zx are standard normal variables (where, for example, Zy is just GDP growth minus its mean, divided by the standard deviation of GDP growth), Kahneman’s formula gives the best linear estimate of Y given X, since the beta in a regression of Zy on Zx is just the correlation between the two. To illustrate, the mean of quarterly real GDP growth is 3.2% at an annual rate, with a standard deviation of 3.9%. The historical mean of the federal funds rate is about 4.9%, with a standard deviation of 3.9%. So holding the fed funds rate at zero is a Z statistic of -1.25. With a correlation of -0.15 between fed funds and subsequent GDP growth, at best, this translates to a Z statistic for GDP of 0.19, and multiplying by the standard deviation of GDP suggests that holding fed funds at zero would be expected to provide a bump to real GDP growth no greater than about 0.7% annually. That figure strikes us as about right, though in practice, GDP growth in recent years has fallen short of even the baseline that one would have projected in the absence of monetary intervention].
How much impact should we expect a 0.25% increase in the fed funds rate to have on economic growth? 0.25% is only an increase of 0.06 standard deviations in the fed funds rate, which would reasonably be associated with -0.15 x 0.06 = -0.009 standard deviations in GDP growth. So based on the historical relationship between the fed funds rate and subsequent GDP growth, the impact of a quarter-point hike in the fed funds rate would be expected to be a reduction in GDP growth of just four one-hundredths of one percent below what would otherwise be expected in the absence of that change.