Uneasy Money

Back in 2009 or 2010, I became intrigued by what seemed to me to be a consistent correlation between the tendency of the stock market to rise on news of monetary easing and potentially inflationary news. I suspected that there might be such a correlation because of my work on the Great Depression inspired by Earl Thompson, from whom I first learned about a monetary theory of the Great Depression very different from Friedman’s monetary theory expounded in his Monetary History of the United States. Thompson’s theory focused on disturbances in the gold market associated with the demonetization of gold during World War I and the attempt to restore the gold standard in the 1920s, which, by increasing the world demand for gold, was the direct cause of the deflation that led to the Great Depression.

I later came to discover that Ralph Hawtrey had already propounded Thompson’s theory in the 1920s almost a decade before the Great Depression started, and my friend and fellow student of Thompson, Ron Batchelder made a similar discovery about Gustave Cassel. Our shared recognition that Thompson’s seemingly original theory of the Great Depression had been anticipated by Hawtrey and Cassel led us to collaborate on our paper about Hawtrey and Cassel. As I began to see parallels between the financial fragility of the 1920s and the financial fragility that followed the housing bubble, I began to suspect that deflationary tendencies were also critical to the financial crisis of 2008.

So I began following daily fluctuations in the principal market estimate of expected inflation: the breakeven TIPS spread. I pretty quickly became persuaded that the correlation was powerful and meaningful, and I then collected data about TIPS spreads from 2003, when the Treasury began offering TIPS securities, to see if the correlation between expected inflation and asset prices had been present 2003 or was a more recent phenomenon.

My hunch was that the correlation would not be observed under normal macroeconomic conditions, because it is only when the expected yield from holding money approaches or exceeds the yield from holding real assets that an increase in expected inflation, by reducing the expected yield from holding money, would induce people to switch from holding money to holding assets, thereby driving up the value of assets.

And that’s what the data showed; the correlation between expected inflation and asset prices only emerged after in 2008 in the period after a recession started at the end of 2007, even before the start of the financial crisis exactly 10 years in September 2008. When I wrote up the paper and posted it (“The Fisher Effect Under Deflationary Expectations“), Scott Sumner, who had encouraged me to write up the results after I told him about my results, wrote a blogpost about the paper. Paul Krugman picked up on Scott’s post and wrote about it on his blog, generating a lot of interest in the paper.

Although I was confident that the data showed a strong correlation between inflation and stock prices after 2008, I was less confident that I had done the econometrics right, so I didn’t try to publish the original 2011 version of the paper. With Scott’s encouragement, I have continued to collected more data as time passed, confirming that the correlation remained even after the start of a recovery while short-term interest rates remained at or near the zero lower bound. The Mercatus Center whose Program on Monetary Policy is directed by Scott has just released the new version of the paper as a Working Paper. The paper can also be downloaded from SSRN.

Aside from longer time span covered, the new version of the paper has refined and extended the theoretical account for when and why a correlation between expected inflation and asset prices is likely be observed and when and why it is unlikely to be observed. I have also done some additional econometric testing beyond the basic ordinary least square (OLS) regression estimates originally presented, and explained why I think it is unlikely that more sophisticated econometric techniques such as an error-correction model would generate more reliable results than those generated by simple OLS regrissions. Perhaps in further work, I will attempt to actually construct an explicit error-correction model and compare the results using OLS and an error-correction model.

Here is the abstract of the new version of the paper.