Stock markets have a long history of being abnormally risky in September and October. Out of 10 months in which the S&P500 ended at least 15% lower than when it started, 3 were in October. Out of 31 months in which it ended at least 10% lower, 12 were in September or October.

I used to guess that this was due to the onset of seasonal affective disorder. That explanation was a bit unsatisfying, because SAD seems likely to be predictable enough that the effects could be mostly smoothed out by smart investors.

After looking at the 1957 pandemic and its possible effect on the stock market, I wondered whether infectious diseases was a better explanation.

I did a crude analysis of the correlations between flu deaths and stock market changes. I didn’t manage to get as good a dataset as I’d hoped for, and ended up settling for the monthly US data for selected seasons (12 in the period 1941-1976) in table 1 of Trends in Recorded Influenza Mortality: United States, 1900–2004.

I looked at correlations between monthly increases in flu deaths per 100,000 people and the monthly change in the S&P500. I was able to find a large effect, but it disappeared when I left out the 1943-1944 season (which was by far the worst season in that time period, yet wasn’t labeled as a pandemic).

Either there’s no effect in that time period, I don’t have detailed enough data, or the effects precede deaths by enough that the death data aren’t helpful.

I was mostly thinking that diseases might have affected the market via effects on investors moods or liquidity preferences, so I wasn’t assuming there would be much discussion of the topic. The paper The Unprecedented Stock Market Reaction to COVID-19 investigated whether newspapers mentioned the topic, and concluded:

In the period before 24 February 2020 – spanning 120 years and more than 1,100 jumps – contemporary journalistic accounts attributed not a single daily stock market jump to infectious disease outbreaks or policy responses to such outbreaks. Perhaps surprisingly, even the Spanish Flu fails to register in next-day journalistic explanations for large daily stock market moves.

So, after a fair amount of research, I still don’t have good evidence about what’s causing the September / October volatility.

P.S. For some strange reason, January is an unusually safe month, with no declines of more than 9% in the S&P 500.

P.P.S. VIX futures are saying that the S&P500’s volatility around late October will be 3.6 points higher the average of August and December volatilities. That compares to an average of 0.86 points higher (and a maximum of 2.1) over the prior 11 years in which VIX futures have been available (all of these numbers come from prices near July 20 of the relevant year).

So the markets expect something unusual this October. Something more surprising than they expected during the prior two presidential election years. Does anyone know whether this risk is due to weather-related pandemic risk or due to political risk?

New infections have been declining at an almost adequate pace (10% per week?) in most parts of the US, and probably the rest of the developed world.

The overall reported new cases look more discouraging, for two reasons.

One reason is the increase in testing. I estimate that two months ago, a bit less than 10% of new infections were being confirmed by tests, and I estimate that now it’s above 20%, maybe getting close to 25%. That means that if the new infection rate were unchanged, we’d be seeing a roughly 10% per week increase in reported cases.

Nearly all parts of the country have done a good deal better than that.

I estimate the change in new infections since the early April peak by multiplying the early April confirmed daily cases by 10 or 12, and the June ones by 4 or 5, and I get a current rate that’s about 1/4 to 1/3 of the peak.

The bad news is that there are some heavily populated areas for which the trend doesn’t look very good over the past few weeks. When the rate of new infections remains constant in some areas, but declines at exponential rates in others, the exponential declines stop affecting the total numbers before too long. E.g. much of California has suppressed the pandemic, but a few cities, such as Los Angeles and Oakland, are enough to keep the state’s total count of new infections steady.

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Long-term investors should stay away from most bonds for the foreseeable future.

Last summer, Colby Davis explained why portfolio managers might want to buy bonds that yield 2%. At the time, I was suspicious, but it felt premature to argue against it.

Since then, yields on 30-year government bonds have dropped to 1.44%. That means bond prices went up. His advice worked well as a hedge against pandemics.

Inflation expectations have dropped along with those yields.

30-year TIPS have an expected yield of -0.045%.

That implies expected CPI inflation of less than 1.5% over the next 30 years, and that, adjusted for inflation as measured by the CPI, investors who buy 30-year government bonds now should expect to lose wealth if they hold to maturity.

So why are investors buying bonds at these prices?

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In this post, I make some conjectures about U.S. economic growth over the next year or two.

Many people expect a depression, due to the current high unemployment numbers. But depressions aren’t caused by unemployment – that’s a symptom, with little predictive power.

The main cause of poor economic growth has been an inability to alter wages so that the supply of and demand for labor are in balance. That typically means deflation, or a large, unexpected decline in the inflation rate, combined with sticky wages.

So I’ll mostly focus on guessing whether we’ll have inflation or deflation.

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The stock market crash of the past two weeks looks like an over-reaction to COVID-19.

Is COVID-19 really the reason for the crash? I can’t find any other news that would explain the timing and which stocks were hit hardest.

Here’s a sample of some of the harder hit stocks, all travel-related (Friday’s close compared to the highest close in February):

  • -37% Hertz (HTZ)
  • -36% Avis (CAR)
  • -29% World Fuel Services Corp (INT)
  • -24% Carnival (cruise line) (CCL)
  • -22% Delta Air Lines (DAL)
  • (compare to the S&P 500: -12.4%)

It is, of course, possible that the market was in a mild bubble in early February, and the virus merely triggered a return to sanity. There were enough high-priced stocks that I’ll guess that’s explains a little of what happened. Hertz and Avis are maybe high-risk stocks due to the risks associated with the upcoming transition to robocars. But the others that I listed did not at all fit my stereotype of overpriced stocks.

And the stocks that I had been thinking were overpriced, in industries that don’t look to be especially hurt by the virus, declined roughly in line with the market.

Outside of travel-related stocks, it mostly looks like a general shift in preferences to more cash, and away from stock. I.e. a general increase in risk aversion.

The gold market is confused as to which direction a pandemic should move it. I agree. I’m confused as to how gold should react.

What scenario could explain the decline? Maybe a two month shutdown of 90+% of U.S. air travel? A multi-year reduction in travel of 10%? It would take something like that for the market reaction to make much sense. Yet I’d bet at roughly 10:1 odds against any one of those scenarios happening.

Metaculus is currently predicting 195k COVID-19 deaths this year.

Metaculus forecast trends ought to look a good deal like random walks, yet the charts I see there look more like exponential growth.

Metaculus is likely to be a more objective source of information than the news media storyteller industry or social media. But it’s likely more susceptible to selection effects and hype than are markets that have lots of money at stake. (Metaculus has token prizes, structured in a way that may encourage more extreme bets than a regular market would).

None of this implies much about where other reactions to the virus are sensible. There’s a much different asymmetry between getting sick versus being paranoid than there is between losing money due to a pandemic versus losing money due to selling on a false alarm.

I’ve got about a month’s supply of food, but that’s my normal preparation for a variety of disasters. I have no special insights about whether the current risks justify staying home.

P.S. Chinese stocks are supporting the view that the situation in China has improved over the past month.

There are a number of investment ideas that pop up about once per generation, work well for years, and then investors get reminded of why they’re not so good, and they get ignored for long enough that the average investor doesn’t remember that the idea has been tried.

The idea I’m remembering this month is known by the phrase Nifty Fifty, meaning that there were about 50 stocks that were considered safe investments, whose reliable growth enabled investors to ignore standard valuation measures such as price/earnings ratios, dividend yields, and price to book value.

The spirit behind the Nifty Fifty was characterized by this line from a cryonaut in Woody Allen’s Sleeper (1973): “I bought Polaroid at seven, it’s probably up millions by now!”.

There was nothing particularly wrong with the belief that those were good companies. The main mistakes were to believe that their earnings would grow forever, and/or that growing earnings would imply growing stock prices, no matter how high the current stock price is.

I’ve seen a number of stocks recently that seem to fit this pattern, with Amazon and Salesforce mostly clearly fitting the stereotype. I also ran into one person a few months ago who believed that Amazon was a good investment because it’s a reliable source of 15+% growth. I also visited Salesforce Park last month, and the wealth that it radiated weakly suggests the kind of overconfidence that’s associated with an overpriced stock market.

I took a stab at quantifying my intuitions, and came up with a list of 50 companies (shown below) based on data from SI Pro as of 2019-09-20, filtered by these criteria:

  • pe_ey1 > 30 (price more that 30 times next year’s forecast earnings)
  • mktcap > 5000 (market capitalization more than $5 billion)
  • prp_2yh > 75 (price more than 75% of its 2 year high)
  • rsales_g5f > 50 (5 year sales growth above the median stock in the database)
  • sales_y1 < 0.33333*mktcap (market capitalization more than 3 times last year’s sales)
  • yield < 3 (dividend yield less than 3%)
  • pbvps > 5 (price more than 5 times book value)
  • epsdc_y2 > 0 (it earned money the year before last)

I did a half-assed search over the past 20 years, and it looks like there were more companies meeting these criteria in the dot com bubble (my data for that period isn’t fully comparable), but during 2005-2015 there were generally less than a dozen companies meeting these criteria.

The companies on this list aren’t as widely known as I’d expected, which weakens the stereotype a bit, but otherwise they fit the Nifty Fifty pattern of the market seeming confident that their earnings will grow something like 20% per year for the next decade.

There were some other companies that arguably belonged on the list, but which the filter excluded mainly due to their forward price/earnings ratio being less than 30: BABA (Alibaba Group Holding Ltd), FB (Facebook), and GOOGL (Alphabet). Maybe I should have used a threshold less than 30, or maybe I should take their price/earnings ratio as evidence that the market is evaluating them sensibly.

This looks like a stock market bubble, but a significantly less dramatic one than the dot com bubble. The market is doing a decent job of distinguishing good companies from bad ones (much more so than in the dot com era), and is merely getting a bit overconfident about how long the good ones will be able to maintain their relative quality.

How much longer will these stocks rise? I’m guessing until the next major bear market. No, I’m sorry, I don’t have any prediction for when that bear market will occur or what will trigger it. It will likely be triggered by something that’s not specific to the new nifty fifty.

I’m currently short EQIX. I expect to short more of these stocks someday, but probably not this year.

ticker company pe_ey1 mktcap sales_y1 yield pbvps
AMT American Tower Corp 52 100520 7440.1 1.7 18.21
AMZN, Inc. 54 901016 232887 0 16.67
ANSS ANSYS, Inc. 31.8 18422.3 1293.6 0 6.46
AZPN Aspen Technology, Inc. 30.5 8820.8 598.3 0 22.2
BFAM Bright Horizons Family Solutio 37.2 9181.8 1903.2 0 10.21
CMG Chipotle Mexican Grill, Inc. 48 23067.9 4865 0 15.04
CRM, inc. 50.1 134707 13282 0 7.02
CSGP CoStar Group Inc 49.3 21878.4 1191.8 0 6.74
DASTY Dassault Systemes SE (ADR) 32.9 37683.3 3839 1 7.05
DXCM DexCom, Inc. 110.3 14132.9 1031.6 0 20.44
EQIX Equinix Inc 70.2 48264.7 5071.7 1.7 5.46
ETSY Etsy Inc 61.7 7115.6 603.7 0 16.42
EW Edwards Lifesciences Corp 36.7 44736.3 3722.8 0 13.06
FICO Fair Isaac Corporation 36.5 9275.5 1032.5 0 33.71
FIVE Five Below Inc 33.6 7152.1 1559.6 0 10.86
FTNT Fortinet Inc 31.6 13409.2 1801.2 0 11.93
GDDY Godaddy Inc 67.2 11976.7 2660.1 0 12.41
GWRE Guidewire Software Inc 70.3 8835.9 652.8 0 5.84
HEI Heico Corp 49.3 15277.3 1777.7 0.1 10.58
HUBS HubSpot Inc 94.6 6877.4 513 0 10.93
IAC IAC/InterActiveCorp 38.3 19642.5 4262.9 0 6.51
IDXX IDEXX Laboratories, Inc. 49.3 23570.6 2213.2 0 138.03
ILMN Illumina, Inc. 44.3 44864.4 3333 0 10.5
INTU Intuit Inc. 31.6 70225.1 6784 0.8 18.67
INXN InterXion Holding NV 106.8 5995.1 620.2 0 7.95
ISRG Intuitive Surgical, Inc. 38.8 61020.9 3724.2 0 8.44
LULU Lululemon Athletica inc. 33.7 25222.7 3288.3 0 16.36
MA Mastercard Inc 30.1 276768 14950 0.5 55.23
MASI Masimo Corporation 41.8 8078.9 858.3 0 7.8
MDSO Medidata Solutions Inc 43.4 5734.1 635.7 0 8.35
MELI Mercadolibre Inc 285.4 27306.2 1439.7 0 12.59
MKTX MarketAxess Holdings Inc. 56.7 12941.1 435.6 0.6 18.93
MPWR Monolithic Power Systems, Inc. 31.5 6761.2 582.4 1 9.44
MTCH Match Group Inc 38.4 22264.7 1729.9 0 105.51
OLED Universal Display Corporation 45.5 8622.8 247.4 0.2 11.36
PAYC Paycom Software Inc 51.3 12812.8 566.3 0 28.6
PCTY Paylocity Holding Corp 47.6 5150.5 467.6 0 17.01
PEGA Pegasystems Inc. 167.2 5686.4 891.6 0.2 10.14
PEN Penumbra Inc 134 5142.8 444.9 0 11.3
RMD ResMed Inc. 30.6 19181.5 2606.6 1.2 9.33
RNG RingCentral Inc 139.4 11062.4 673.6 0 30.93
ROL Rollins, Inc. 43.6 11383.4 1821.6 1.2 15.04
RP RealPage Inc 31.3 6084.5 869.5 0 5.3
TAL TAL Education Group (ADR) 39.4 21326.2 2563 0 8.45
TECH BIO-TECHNE Corp 34.6 7483.8 714 0.6 6.52
TREX Trex Company Inc 30.2 5074.2 684.3 0 13.05
TYL Tyler Technologies, Inc. 43.5 10004.5 935.3 0 6.98
VEEV Veeva Systems Inc 62 21366.2 862.2 0 15.27
VRSK Verisk Analytics, Inc. 32.3 25948.4 2395.1 0.6 11.73
ZAYO Zayo Group Holdings Inc 42.5 7997.8 2578 0 5.95

Book review: Prediction Machines: The Simple Economics of Artificial Intelligence, by Ajay Agrawal, Joshua Gans, and Avi Goldfarb.

Three economists decided to write about AI. They got excited about AI, and that distracted them enough that they only said a modest amount about the standard economics principles that laymen need to better understand. As a result, the book ended up mostly being simple descriptions of topics on which the authors had limited expertise. I noticed fewer amateurish mistakes than I expected from this strategy, and they mostly end up doing a good job of describing AI in ways that are mildly helpful to laymen who only want a very high-level view.

The book’s main goal is to advise business on how to adopt current types of AI (“reading this book is almost surely an excellent predictor of being a manager who will use prediction machines”), with a secondary focus on how jobs will be affected by AI.

The authors correctly conclude that a modest extrapolation of current trends implies at most some short-term increases in unemployment.

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Book review: Principles: Life and Work, by Ray Dalio.

Most popular books get that way by having an engaging style. Yet this book’s style is mundane, almost forgetable.

Some books become bestsellers by being controversial. Others become bestsellers by manipulating reader’s emotions, e.g. by being fun to read, or by getting the reader to overestimate how profound the book is. Principles definitely doesn’t fit those patterns.

Some books become bestsellers because the author became famous for reasons other than his writings (e.g. Stephen Hawking, Donald Trump, and Bill Gates). Principles fits this pattern somewhat well: if an obscure person had published it, nothing about it would have triggered a pattern of readers enthusiastically urging their friends to read it. I suspect the average book in this category is rather pathetic, but I also expect there’s a very large variance in the quality of books in this category.

Principles contains an unusual amount of wisdom. But it’s unclear whether that’s enough to make it a good book, because it’s unclear whether it will convince readers to follow the advice. Much of the advice sounds like ideas that most of us agree with already. The wisdom comes more in selecting the most underutilized ideas, without being particularly novel. The main benefit is likely to be that people who were already on the verge of adopting the book’s advice will get one more nudge from an authority, providing the social reassurance they need.


Some of why I trust the book’s advice is that it overlaps a good deal with other sources from which I’ve gotten value, e.g. CFAR.

Key ideas include:

  • be honest with yourself
  • be open-minded
  • focus on identifying and fixing your most important weaknesses

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Book(?) review: The Great Stagnation: How America Ate All The Low-Hanging Fruit of Modern History, Got Sick, and Will (Eventually) Feel Better, by Tyler Cowen.

Tyler Cowen wrote what looks like a couple of blog posts, and published them in book form.

The problem: US economic growth slowed in the early 1970s, and hasn’t recovered much. Median family income would be 50% higher if the growth of 1945-1970 had continued.

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Book review: Where Is My Flying Car? A Memoir of Future Past, by J. Storrs Hall (aka Josh).

If you only read the first 3 chapters, you might imagine that this is the history of just one industry (or the mysterious lack of an industry).

But this book attributes the absence of that industry to a broad set of problems that are keeping us poor. He looks at the post-1970 slowdown in innovation that Cowen describes in The Great Stagnation[1]. The two books agree on many symptoms, but describe the causes differently: where Cowen says we ate the low hanging fruit, Josh says it’s due to someone “spraying paraquat on the low-hanging fruit”.

The book is full of mostly good insights. It significantly changed my opinion of the Great Stagnation.

The book jumps back and forth between polemics about the Great Strangulation (with a bit too much outrage porn), and nerdy descriptions of engineering and piloting problems. I found those large shifts in tone to be somewhat disorienting – it’s like the author can’t decide whether he’s an autistic youth who is eagerly describing his latest obsession, or an angry old man complaining about how the world is going to hell (I’ve met the author at Foresight conferences, and got similar but milder impressions there).

Josh’s main explanation for the Great Strangulation is the rise of Green fundamentalism[2], but he also describes other cultural / political factors that seem related. But before looking at those, I’ll look in some depth at three industries that exemplify the Great Strangulation.

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