When you increase landowners' costs, the value of land goes down

Last week, the New Zealand Herald reported:

As of October 19, landowners and foresters who participate in the Emissions Trading Scheme (ETS) will be charged an additional annual fee of $30.25 per hectare in perpetuity.

A Ministry for Primary Industries (MPI) spokesperson says the $29.8m it costs each year to run the forestry ETS has been 100 per cent taxpayer-funded until now.

But from October 19, 63 per cent of those “administrative costs” will instead fall to ETS participants, with the remaining 37 per cent still funded by the taxpayer.

It seems fair enough to me that the landowners who benefit from the ETS should pay the costs of administering the system that they benefit from. However, what caught my eye in the article was this bit:

To cover that cost, [New Zealand Institute of Forestry president James] Treadwell says foresters will have to pass the problem on to farmers.

“I’ve got one farmer who is wanting to sell off about 120 hectares of their farm to cover their interest rates. I had to tell him the price has just dropped by $500 a hectare. He was pretty upset,” said Treadwell.

Why would a small charge of $30.25 per hectare per year lead to a $500 reduction in land values? It's because the value of the land is based on how profitable it is to own land. With the new annual fee, land is less profitable by $30.25 per hectare per year. And that translates into $500 in land value. The Efficient Markets Hypothesis suggests that new information is incorporated into the value of an asset immediately. And this appears to be what has happened here.

It's interesting to consider what that implies about the discount rate for landowners (or land buyers). Using the formula for the present value of an annuity, setting the value of the annuity to $500, and the value of the annual payment to $30.25, and solving for the discount rate R, I get an implied discount rate of 6.05 percent. [*]

That implied discount rate is interesting for a few reasons. First, it's very similar to current interest rates for farm loans. For example, ANZ currently has business term loans (including for farms) with a floating rate of 7.05 percent. At a discount rate of 7.05 percent, that $30.25 annual fee should reduce land prices by $429 per hectare. So, landowners (or land buyers) are reducing land prices by slightly more than expected given current interest rates. That may be because, while the annual fee is currently proposed to be $30.25, there is little stopping the government from increasing that fee later (at a rate that is higher than the rate of inflation), which would increase the amount of compensation, in terms of lower land prices, required now in order to offset the annual fee. So, perhaps that additional risk is being priced into land prices, and being picked up in the annuity calculation as a lower discount rate. [**]

Second, the discount rate is far higher than the social discount rate that is used in climate change (and many other) applications, which is often 3 percent (see here for more on this). If we applied the 3 percent social discount rate, the $30.25 annual fee should be reducing land values by $1008 per hectare. That suggests that landowners (and land buyers) are more focused on the present than the social discount rate implies, which is consistent with the idea of present bias from behavioural economics. Alternatively, we could interpret this as implying that society as a whole is much more focused on the future (as captured by the social discount rate) than individual landowners (or land buyers) are. Short-term profit motives strike again?

Finally, and perhaps most surprisingly to me, the implied discount rate is not wildly high (or low), implying that landowners (and land buyers) are not seriously overreacting to the introduction of this fee. Not only is the Efficient Markets Hypothesis working, but it appears that the landowners (and land buyers) have been relatively rational in terms of their response (as picked up in the change in land value). I guess it's not always the case that asset markets are crazy.

*****

[*] Ok, I admit that I didn't solve this by hand. It's not possible to do so easily. I used the 'solver' function in Excel, and set the number of periods to 10,000 (so, it's really a solution to the question: what is the discount rate that equalises an annual value of $30.25, paid every year for 10,000 years, and a one-time payment today of $500?

[**] If we used $30.25 per year for the first five years, then increasing to $37.25 for every year after that, then the implied discount rate that equates the annuity and the decrease in land values is 7.05 percent, the same as the ANZ farm loan rate. So, perhaps it is some calculation like that which explains the decrease in land values being higher than implied by current interest rates.

If you work out how to beat the bookies, the bookies strike back

There is a famous quote in gambling circles, "if you bet the Super Bowl, you are a losing player". Most, if not all, regular gamblers probably believe they have some system that allows them to beat the bookies. [*] In reality, close to none of them do. That's because the bookmakers have pretty good models that make them fairly accurate at predicting the odds of various outcomes (if they didn't, then they wouldn't remain profitable bookies for long!).

So, the sports odds we observe in the real world pretty accurately reflect the underlying probabilities (this is a much better example of the efficient markets hypothesis than share markets or asset prices!). However, not all bookies offer the same odds, since the bookies try to balance the financial risk that they face from any particular outcome. So, a bookie that receives a lot of bets on the underdog will adjust the underdog's odds downwards (and the favourite's odds upwards), in order to maintain some balance.

Is it possible to exploit those differences in different bookmakers' odds on the same event in order to make money? True arbitrage opportunities are rare, as that would require one bookie to have one outcome favoured, while at the same time another bookies has a different outcome favoured. However, it may nevertheless be possible to take advantage of the difference in odds. The method is explained in this 2017 paper by Lisandro Kaunitz (University of Tokyo), Shenjun Zhong (Monash University), and Javier Kreiner (CargoX). As they explain:

Our betting system differed from previous betting strategies in that, instead of trying to build a model to compete with bookmakers’ forecasting expertise, we used their publicly available odds as a proxy of the true probability of a game outcome. With these proxies we searched for mispricing opportunities, i.e., games with odds offered above the estimated fair value...

The 'fair value' of a bet was based on the average odds of at least three bookies (Kaunitz et al. followed the odds of 32 different bookmakers for domestic and international football (soccer) games. Their success was remarkable:

Our strategy returned sustained profits over years of simulated betting with historical data, and months of paper trading and betting with actual money...

Specifically, the simulated betting:

...reached an accuracy of 44.4% and yielded a 3.5% return over the analysis period. For example, for an imaginary stake of $50 per bet, this corresponds to an equivalent profit of $98,865 across 56,435 bets...

The paper trading:

...obtained an accuracy of 44.4% and a return of 5.5%, earning $1,128.50 across 407 bets for the case of $50 bets...

And finally, betting with real money:

...obtained an accuracy of 47.% and a profit of $957.50 across 265 bets, equivalent to a 8.5% return...

But, just when Kaunitz et al. were probably starting to plan their semi-retirement on the Costa Azul, the bookies struck back:

Although we played according to the sports betting industry rules, a few months after we began to place bets with actual money bookmakers started to severely limit our accounts. We had some of our bets limited in the stake amount we could lay and bookmakers sometimes required “manual inspection” of our wagers before accepting them. In most cases, bookmakers denied us the opportunity to bet or suggested a value lower than our fixed bet of $50...

Kaunitz et al. conclude that the whole system is rigged. They found a way to exploit the imperfections in bookmakers' odds, but that only succeeded in drawing attention to them. The bookies only want losing players to play.

[HT; Marginal Revolution, here (for the Dilan Esper Twitter thread), and here (for the Kaunitz et al. paper)]

*****

[*] I admit that I am not immune to this effect. Back when I was a PhD student, I had an extremely successful run betting on NHL games, making hundreds of dollars in winnings over a few months. I thought I had a good system. However, the betting strategy didn't transfer to NBA games, where I lost all of my NHL winnings and more in the space of a few weeks. I haven't gambled on sports since.

Music, mood and the sharemarket

Back in 2014, I wrote a post about this 2012 paper that showed a very weak correlation between song complexity (as measured by changes in tempo) and the S&P500 Index. The weak correlation meant that there wasn't much to see, which is somewhat surprising. Our musical tastes reflect our mood, and we already know that the sharemarket is affected by our mood - it goes up more in the summer, and on holidays, etc.

So, I was interested to read this new article by Alex Edmans (London Business School), Adrian Fernandez-Perez (AUT), Alexandre Garel (Audencia Business School), and Ivan Indriawan (AUT), forthcoming in the Journal of Financial Economics (ungated earlier version here, and there is a non-technical summary on The Conversation). Edmans et al. use data on the 'valence' of each song from Spotify. As they explain, valence is a measure of the song's positivity, derived from a Spotify algorithm and measured on a scale of 0-1, where:

Songs with high valence sound more positive (e.g., happy, cheerful, euphoric), while songs with low valence sound more negative (e.g., sad, depressed, angry).

And apparently:

The songs with the highest valence in our sample are September by Earth, Wind & Fire (valence of 0.982), Here Comes Santa Claus (Right Down Santa Claus Lane) by Gene Autry (0.976) and Little Saint Nick - 1991 Remix by The Beach Boys (0.971). The songs with the lowest valence are: RMP by Trippie Redd (valence of 0.0333), I'll see you in 40 by Joji (0.0321) and Legion Inoculant by TOOL (0.0262).

Edmans et al. calculate the 'stream-weighted' average valence of Spotify's top 200 most streamed songs on each day from January 2017 to December 2019, based on the number of streams and the valence of each song. They then correlate that overall valence measure with the returns from the S&P500 Index, controlling for seasonal effects, the day of the week, U.S. holidays, and an index of sunshine (all of which might affect mood). First, they find that:

Consistent with music sentiment being responsive to mood swings of individuals, we document a strong positive association with U.S. holidays and weekends and a strong negative association with post-holidays, winter seasons, and bad weather conditions.

So, it appears that their Spotify-based measure of mood is plausibly correlated with factors that are known to affect mood. They then look at the relationship with sharemarket returns, and find that:

...a one-unit increase in weekly music sentiment leads to a 0.146 decrease in the S&P 500 returns the following week (alternatively, a one standard deviation increase in the weekly music sentiment leads to a 20 bps decrease in the following week returns).

 I found this a little bit odd. They focused on the sharemarket returns the following week, rather than the contemporaneous sharemarket returns. The results for contemporaneous returns are available in the working paper version of the article, and show a positive correlation with sentiment. So, overall their results are consistent - a more positive mood (as picked up by musical tastes on Spotify) is associated with an increase in sharemarket returns this week, but then investors correct their overexuberance the following week, leading to a decrease in sharemarket returns the following week. And the reverse for a more negative mood.

Unfortunately, as with the 2012 paper, their results show correlations rather than causation. So, we can't say for sure that it is mood that is causing the sharemarket changes, even though we can tell a good story that explains it as a causal effect. The next step must be to try and find some exogenous change in mood or music tastes, to allow for a test of the causal relationship.

Read more:

• Can music soothe the savage stock market?

Don't bet on horses with fast-sounding names

In a new paper to be published shortly in the Journal of Behavioral and Experimental Economics (open access), Oliver Merz, Raphael Flepp, and Egon Franck (all University of Zurich) undertake an interesting test of behavioural economics. The affect heuristic suggests that our decisions are influenced by our emotions. In other words, the affect heuristic forms part of our System 1 thinking (to borrow from Nobel Prize winner Daniel Kahneman's excellent book Thinking, Fast and Slow), where our decision-making is fast, instinctive, and emotional.

However, the affect heuristic (and System 1 thinking more generally) is completely at odds with the Efficient Markets Hypothesis, which in its strongest form suggests that all relevant public and private information is incorporated into the price of an asset (such as a share price). The efficient markets hypothesis essentially suggests that in financial decisions we rely on System 2 thinking - slow, deliberative, and logical, and not influenced by our emotions.

Merz et al. look at the case of betting on horse racing, and in particular they look at whether the names of horses affect betting behaviour. The name of a horse should be reasonably uninformative about how fast the horse can run - there's no rule or law that says an owner can't name their slow nag Rocket Roger, for example. So, if betting behaviour is affected by the names of the horses, then that is likely to be the affect heuristic at work.

Using Betfair data from over 400,000 horse races in the UK, Ireland, the US, South Africa, and Australia, and involving nearly four million horses, they find that:

...a fast-sounding horse name has predictive power with regard to the race outcome beyond the winning probabilities implied in the odds. In particular, our results show that the winning probabilities of bets on horses with fast-sounding names are overstated, implying that the prices in betting exchange markets are not completely efficient, as prices become distorted by incorporating affective, misleading information from a horse’s fast-sounding name.

In other words, bettors place bets on horses with fast-sounding names far more often than the horses' underlying win probabilities suggest that bettors should. But before you get carried away and rush off to bet against horses with fast-sounding names:

...we find significantly lower returns for horses classified as fast-sounding compared to other horses. A simple trading strategy of betting against all horses classified as fast-sounding yields a return of approximately 2.9% before the commission but a negative return of -1.6% after deducting the standard commission of 5% from Betfair.

Even though in theory you can profit from other bettors' irrational preference for betting on horses with fast-sounding names (in apparent violation of the Efficient Markets Hypothesis), when you take into account that Betfair takes a commission on every bet, there isn't a positive profit-making opportunity here. The best you can probably do is to avoid betting on horses with fast-sounding names, because the return is going to be significantly more negative than it should be.

[HT: Marginal Revolution]

You wouldn't want to have a company named 'Corona' right now

Over the last year, I've written a couple of posts about the naming of companies (see here and here). In both cases, those posts highlighted the effect of naming a company after 'blockchain', and the positive effects that has on share prices (albeit temporary). Now, we are seeing the opposite effect for companies with names related to 'corona'.

A new working paper by Shaen Corbet (Dublin City University) and co-authors (including my colleagues Greg Hou, Yang Hu, and Les Oxley) looks at the effect of the coronavirus pandemic on the share market returns for three companies: (1) Constellation Brands (the owner and importer of Corona beer in the US); (2) Corona Corp (a Japanese seller of air conditioners and other household items); and (3) Coronation Fund Managers (South African financial services firm). Using hourly share price data from March 2019 to March 2020, they find that:

...all of the analysed companies exhibit strong negative hourly returns in the period after the announcement of the existence of the COVID-2019 pandemic. Further, there is an exceptionally large significant increase in hourly volatility for each of the analysed companies... There is clear evidence that Constellation Brands (STZ) and Corona Corp (5909:JP) experienced a sharp and sustained deterioration in share prices outside of that expected through market-driven forces. The reasons for so would be deemed somewhat irrational, but mostly very unfortunately driven by name association.

As was the case for blockchain-named companies in the earlier research I blogged about, company names can have seemingly irrational effects on share prices (in this case, a negative effect). As I note in my ECONS101 class when we talk about the efficient markets hypothesis, asset prices (like share prices) can deviate substantially from what would be expected based on 'market fundamentals'. Those deviations can be persistent, if market participants believe that other market participants are going to continue to act irrationally. Eventually though, there will be a return to normality. Perversely then, this might actually be a good time to buy shares in Constellation Brands, if you believe that they can weather the coronavirus pandemic storm without bankruptcy.

[HT: Les Oxley was interviewed about this research on Newshub earlier this week]

Read more:

• The share market effects of naming companies after blockchain
• Cryptocurrency company name changes; and the energy costs of bitcoin mining

Cryptocurrency company name changes; and the energy costs of bitcoin mining

Back in September last year, I wrote a post about the effects on share prices of re-naming a company to include 'Blockchain' in its name:

In other words, the companies that changed name were not doing well (in terms of share price) in the lead up to their name change. They then saw a massive increase in their share price, up to 30 days after changing name, presumably as investors looking to jump on the cryptocurrency bandwagon bought into the companies. Then the share price started falling back to its original level.

The paper I referred to, by Jain and Jain, was published in the journal Economics Letters. The latest issue of the same journal has another article in a similar vein (sorry I don't see an ungated version online, but it appears that it is open access for now), this one authored by Prateek Sharma (IIM Udaipur), Samit Paul (IIM Calcutta), and Swati Sharma (Jawaharlal Nehru University). They build on the earlier Jain and Jain paper, but extend the analysis in several ways, most importantly by: (1) extending the sample from 10 company name changes to 39; and (2) considering a comparison group of crypto-currency-related companies that did not change names, and another comparison group of non-crypto-currency-related companies that did change names. The comparison groups were matched to the sample in terms of share price, market capitalisation and value.

Looking at share returns, they find that:

...the share price of the sample firms increases significantly following the name change announcement... The most dramatic price increase occurs in the first couple of days, as the average share price increases from $2.24 on day −1 to $3.26 on day +1. We find no evidence of a post-announcement negative drift in share prices. The average share price is $4.07 on day +10, $4.36 on day +30 and $4.65 on day +50.

Comparing with the comparison groups, they find that:

These abnormal returns cannot be explained by industry factors, as the sample firms generate significantly higher abnormal returns than those generated by the sample of matched cryptocurrency firms that did not change their names... the sample firms experience larger and more permanent changes in value compared to similar noncryptocurrency firms that changed their corporate names during the sample period. 

A rose by any other name may smell as sweet, but a company that includes blockchain in its name is clearly sweeter (apologies to The Bard).

Another article in the same issue of that journal also caught my attention, because it also relates to bitcoin. Debojyoti Das (Woxsen School of Business in India) and Anupam Dutta (University of Vaasa in Finland) look at the correlation between bitcoin miners' total revenue (not individual-level data, but revenue in total for all bitcoin miners) and energy usage, using data from February 2017 to March 2019. This is a low-quality paper, not least because they use quantile regression when it is totally unnecessary, so I won't dwell on it too much. Moreover, the results are surprising and under-explained, which also makes me wonder why this study was published in this form.

Das and Dutta find a negative correlation between miners' revenue and energy usage. In other words, total revenue (for all miners collectively) is high when their energy usage is low, and total revenue is low when energy usage is high. I would have thought that, when miners were engaged in more activity, revenue would be high and so would energy usage. That would suggest the correlation should be positive, not negative. However, that assumes that we are holding the price of bitcoin constant. These results basically suggest that the price of bitcoin is negatively correlated with energy usage, and it is hard to see why that would be. This is definitely a study that requires revisiting, and with more appropriate quantitative methods.

Read more:

• The share market effects of naming companies after blockchain

The share market effects of naming companies after blockchain

This past week in my ECONS101 class, we discussed financial markets. In particular, we discussed the efficient markets hypothesis - the idea that all publicly available information (good and bad) about an asset's future cash flows is already captured in the asset's price (in the strongest form of the efficient markets hypothesis, all private information is also captured in the asset's price). So, it was timely that the Economics Discussion Group discussed this article by Archana Jain (Rochester Institute of Technology) and Chinmay Jain (Ontario Tech University), published in the journal Economics Letters (sorry, I don't see an ungated version). The authors look at the impacts on the share price when a company adds changes its name to include "bitcoin" or "blockchain".

I'd already followed the news about one of these companies in late 2017 - Long Blockchain Corp, formerly Long Island Iced Tea - which has been in the news again recently. Jain and Jain looked at ten such companies, and compared their performance with the performance of other companies that are included in blockchain exchange-traded funds. If there was something important going on with blockchain, you'd expect it to be picked up in the share prices of these other companies as well. Instead, they found that:

...the firms that change their name to include blockchain in it had a negative return of 13.55% from day −14 to day −2. We see an increase in the return of these firms from day 0 to 1 at 34.29%. Over the five-day period from day −2 to day +2, all firms earn a strongly statistically significant abnormal return of 100.89 percent. Over a 30-day (60-day) period from +1 to +30 (+60), all firms earn a 81.47% (72.84%) percent return. However, over a 60-day period from +61 to +120, all firms earn a −56.32 percent return.

In other words, the companies that changed name were not doing well (in terms of share price) in the lead up to their name change. They then saw a massive increase in their share price, up to 30 days after changing name, presumably as investors looking to jump on the cryptocurrency bandwagon bought into the companies. Then the share price started falling back to its original level. In the meantime, it's likely that the original company owners cashed out a big payday, while the stupid investors who failed the due diligence test were left licking their wounds.

Jain and Jain's article is a brief "how-to" guide for making money from a vapourware holding company. Just add the latest buzzword to your company name, and cash out big (but make sure to do so before the SEC figures it out). It's also proof that the efficient markets hypothesis doesn't hold in the short run. Otherwise, investors wouldn't get suckered into this.

Why divestment doesn't punish firms' share price (and why you can't reward ethical firms that way either)

Some of you will have been following the campaign to urge the University of Otago to adopt a policy of not investing in fossil fuels (following similar policy by Victoria University and other organisations). The University has deferred the decision for now.

I haven't been following the Otago debate very closely, but a recent article in The New Yorker by William Macaskill caught my attention. In the article, Macaskill lays out the argument against divestment, if the purpose of the divestment is to punish the companies you are divesting from:

if the aim of divestment campaigns is to reduce companies’ profitability by directly reducing their share prices, then these campaigns are misguided. An example: suppose that the market price for a share in ExxonMobil is ten dollars, and that, as a result of a divestment campaign, a university decides to divest from ExxonMobil, and it sells the shares for nine dollars each. What happens then?

Well, what happens is that someone who doesn’t have ethical concerns will snap up the bargain. They’ll buy the shares for nine dollars apiece, and then sell them for ten dollars to one of the other thousands of investors who don’t share the university’s moral scruples. The market price stays the same; the company loses no money and notices no difference. As long as there are economic incentives to invest in a certain stock, there will be individuals and groups—most of whom are not under any pressure to act in a socially responsible way—willing to jump on the opportunity. These people will undo the good that socially conscious investors are trying to do.

Consider the efficient markets hypothesis - all information (good and bad) about the firm's future cash flows is already captured in the firm's share price. Essentially, the firm's market capitalisation (the value of all of its shares) should be the discounted value of all of its future cash flows (in most cases this isn't true, but for firms in Western share markets it usually isn't too far from the case). So, when the Fossil Free petitioners try to argue:

We urge the University of Otago and associated Foundation Trust to avoid fossil fuel extraction investments as they are economically insecure in the long-term.

That information is already captured in the share prices of fossil fuel extraction firms. This information is not a big secret. Holders of shares in those firms are already aware of this information, and how it will impact future cash flows for the firms, and have evaluated whether it makes sense to hold onto those shares.

If a fund divests itself of fossil fuel shares, then that increases the supply of shares to the market, and the price will fall slightly. Given that the previous price had already accounted for the future decreases in cash flows, the shares are now under-priced relative to their 'true' value. Buyers will swoop in to buy those shares, increasing demand and the share price, until it returns to its original share price. So, we shouldn't expect any impact of divestment on the share price. Which is indeed what research has found, as Macaskill notes in the article:

Studies of divestment campaigns in other industries, such as weapons, gambling, pornography, and tobacco, suggest that they have little or no direct impact on share prices. For example, the author of a study on divestment from oil companies in Sudan wrote, “Thanks to China and a trio of Asian national oil companies, oil still flows in Sudan.” The divestment campaign served to benefit certain unethical shareholders while failing to alter the price of the stock.

This argument could equally apply (albeit in reverse) for "ethical investment". It's not possible to reward green firms through a higher share price. Since the price already reflects the true value of the firm, then if a fund wants to buy shares in Corporation Green-Yay, then that will slightly increase the demand for shares in Corporation Green-Yay, and slightly increase the share price. Other holders of those shares will recognise they are now overvalued compared to their 'true' value, and sell them. This increases the supply of those shares and lowers the price back to where it was before. Again, no impact on share price.

Finally, Macaskill notes that divestment may have a positive effect in the long run:

Campaigns can use divestment as a media hook to generate stigma around certain industries, such as fossil fuel. In the long run, such stigma might lead to fewer people wanting to work at fossil-fuel companies, driving up the cost of labor for those corporations, and perhaps to greater popular support for better climate policies.

This is a much better argument in favor of divestment than the assertion that you’re directly reducing companies’ share price. If divestment campaigns are run, it should be with the aim of stigmatization in mind.

And similarly, there may be long-run benefits for green firms if campaigns to invest in them are used as a media hook to encourage more people to work at those firms (thus lowering their labour costs). Essentially, this would create a compensating differential between the wage paid to workers (in the same job) at the green firm, and those at the fossil fuel extraction firm. Because working at the fossil fuel extraction firm is less attractive than working at the green firm, workers must be compensated (through a higher wage) for working at the fossil fuel extraction firm. One might argue that this compensating differential already exists, but perhaps divestment campaigns might increase its effect?

[HT: Marginal Revolution]

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.