Tuesday, 17 June 2014

Can music soothe the savage stock market?

I recently read this 2012 paper from the North American Journal of Economics and Finance by Philip Maymin of NYU-Polytechnic Institute (ungated earlier version here). The paper investigates whether the complexity of songs (as measured by the variability in beats) is related to stock market volatility. Specifically:
This paper investigates the question of whether more complexity in music on Top 100 Billboard songs leads to less future complexity in the market through lower subsequent realized market volatility, because the complexity of popular music reflects the mood and the choices made by economic actors in light of their cognitive load with respect to future activity...
When they tend to contemplate more complex possibilities, they will prefer simpler music; further, contemplating more complex possibilities is linked to an increase in future risky activity, thus leading to future market volatility. In this way, popular preference for simple music today predicts turbulent market activity in the future, while popular preference for complex music today predicts relatively calmer market activity in the future. Finally, musical preferences explain more than can be explained simply by mean reverting market volatility: popular music decisions contain additional information.
It's an interesting paper, and it's apparently been around in various forms for a while (see blog posts or articles talking about earlier versions of the paper here, here, and here). The author uses music data analysed by The Echo Nest on the average beat variance (which will be higher when there are lots of changes in speed or tempo), and compares it with the annual volatility of the S&P 500 index.

There are some interesting notes on artists in the results:
Among artists with at least five appearances on the charts, Ray Charles had the highest average beat variance of 0.1298, ranging from a low of 0.0390 to a high of 0.2860 in his eleven hits. Other artists who averaged a high beat variance are Barbra Streisand, Bobby Vinton, Alicia Keys, and Alice Cooper. These artists sang relatively volatile songs. At the other end of the list, Billy Idol had the lowest average beat variance of 0.0034, ranging from a low of 0.0020 to a high of 0.0050. Other artists who averaged a low beat variance are Ace of Base, Genesis, Al Green, and Bobby Brown. These artists sang relatively stable songs.
The lowest beat variance of any song was 0.0010. Sixty-five songs achieved this lowest level, including A-ha's 1985 hit "Take On Me."... A-ha's "Take On Me" occurred in the year with the most such low beat variance songs, two years before the high volatility market crash of 1987.
A-ha may have a lot to answer for in terms of boring repetitive music (if you don't know who A-ha are, this is the best summary), but this is the first time I've seen them linked to the 1987 stock market crash.

What's also great about this paper is that when you search for it online, you come across some really interesting things. Like this infographic that the author produced for the Boston Globe. Or this YouTube video which tracks the data over time (with musical accompaniment, of course).

The paper did make me wonder about what it means in the context of the Efficient Markets Hypothesis (EMH), which even in its weakest form suggests that asset prices (including stocks) incorporate all available public information about the stocks. Do we listen to simpler music when information about future returns is uncertain? Does this imply that future market volatility is also priced into asset prices? And of course, if you know what people are listening to now, can you use that to work out market volatility later? Unfortunately, if you read the paper carefully, you discover this point buried on the tenth page:
The only evidence of Granger-causality in either direction is a weak one (p-value of 8.9%) for a one-year lag that suggests that last year's average beat variance has some small predictive power about the future market volatility.
In other words, the entire paper is constructed around a weak correlation between the average beat variance in a given year and the market volatility in the following year. Moreover, while the paper shows some profit opportunities on the basis of predicting future market volatility, the profits become statistically insignificant when predicting out-of-sample.

So, it appears music may not soothe the savage stock market. The results don't establish cause (of course, and the author never claims they do). Damnit - I so wanted to be able to advocate a policy solution to excess market volatility that involved minimum content standards on radio for Tool, System of a Down, or Mastodon. [*]


[*] Yes, there are better bands I could have chosen as examples of song complexity, and probably more complex songs by these three. But these ones came immediately to mind.

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