stock market crash

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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?

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.

The stock market reaction to the election was quite strange.

From the first debate through Tuesday, S&P 500 futures showed modest signs of believing that Trump was worse for the market than Clinton. This Wolfers and Zitzewitz study shows some of the relevant evidence.

On Tuesday evening, I followed the futures market and the prediction markets moderately closely, and it looked like there was a very clear correlation between those two markets, strongly suggesting the S&P 500 would be 6 to 8 percent lower under Trump than under Clinton. This correlation did not surprise me.

This morning, the S&P 500 prices said the market had been just kidding last night, and that Trump is neutral or slightly good for the market.

Part of this discrepancy is presumably due to the difference between regular trading hours and after hours trading. The clearest evidence for market dislike of Trump came from after hours trading, when the most sophisticated traders are off-duty. I’ve been vaguely aware that after hours markets are less efficiently priced. But this appears to involve at least a few hundred million dollars of potential profit, which somewhat stretches the limit of how inefficient the markets could plausibly be.

I see one report of Carl Icahn claiming

I thought it was absurd that the market, the S&P was down 100 points on Trump getting elected … but I couldn’t put more than about a billion dollars to work

I’m unclear what constrained him, but it sure looked like the market could have absorbed plenty more buying while I was watching (up to 10pm PST), so I’ll guess he was more constrained by something related to him being at a party.

But even if the best U.S. traders were too distracted to make the markets efficient, that leaves me puzzled about asian markets, which were down almost as much as the U.S. market during the middle of the asian day.

So it’s hard to avoid the conclusion that the market either made a big irrational move, or was reacting to news whose importance I can’t recognize.

I don’t have a strong opinion on which of the market reactions was correct. My intuition says that a market decline of anywhere from 1% to 5% would have been sensible, and I’ve made a few trades reflecting that opinion. I expect that market reactions to news tend to get more rational over time, so I’m now giving a fair amount of weight to the possibility that Trump won’t affect stocks much.

Book review: The Midas Paradox: Financial Markets, Government Policy Shocks, and the Great Depression, by Scott B Sumner.

This is mostly a history of the two depressions that hit the U.S. in the 1930s: one international depression lasting from late 1929 to early 1933, due almost entirely to problems with an unstable gold exchange standard; quickly followed by a more U.S.-centered depression that was mainly caused by bad labor market policies.

It also contains some valuable history of macroeconomic thought, doing a fairly good job of explaining the popularity of theories that are designed for special cases (such as monetarism and Keynes’ “general” theory).

I was surprised at how much Sumner makes the other books on this subject that I’ve read seem inadequate.
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NGDP targeting has been gaining popularity recently. But targeting market-based inflation forecasts will be about as good under most conditions [1], and we have good markets that forecast the U.S. inflation rate [2].

Those forecasts have a track record that starts in 2003. The track record seems quite consistent with my impressions about when the Fed should have adopted a more inflationary policy (to promote growth and to get inflation expectations up to 2% [3]) and when it should have adopted a less inflationary policy (to avoid fueling the housing bubble). It’s probably a bit controversial to say that the Fed should have had a less inflationary policy from February through July or August of 2008. But my impression (from reading the stock market) is that NGDP futures would have said roughly the same thing. The inflation forecasts sent a clear signal starting in very early September 2008 that Fed policy was too tight, and that’s about when other forms of hindsight switch from muddled to saying clearly that Fed policy was dangerously tight.

Why do I mention this now? The inflation forecast dropped below 1 percent two weeks ago for the first time since May 2008. So the Fed’s stated policies conflict with what a more reputable source of information says the Fed will accomplish. This looks like what we’d see if the Fed was in the process of causing a mild recession to prevent an imaginary increase in inflation.

What does the Fed think it’s doing?

  • It might be relying on interest rates to estimate what it’s policies will produce. Interest rates this low after 6.5 years of economic expansion resemble historical examples of loose monetary policy more than they resemble the stereotype of tight monetary policy [4].
  • The Fed could be following a version of the Taylor Rule. Given standard guesses about the output gap and equilibrium real interest rate [5], the Taylor Rule says interest rates ought to be rising now. The Taylor Rule has usually been at least as good as actual Fed policy at targeting inflation indirectly through targeting interest rates. But that doesn’t explain why the Fed targets interest rates when that conflicts with targeting market forecasts of inflation.
  • The Fed could be influenced by status quo bias: interest rates and unemployment are familiar types of evidence to use, whereas unbiased inflation forecasts are slightly novel.
  • Could the Fed be reacting to money supply growth? Not in any obvious way: the monetary base stopped growing about two years ago, M1 and MZM growth are slowing slightly, and M2 accelerated recently (but only after much of the Fed’s tightening).

Scott Sumner’s rants against reasoning from interest rates explain why the Fed ought to be embarrassed to use interest rates to figure out whether Fed policy is loose or tight.

Yet some institutional incentives encourage the Fed to target interest rates rather than predicted inflation. It feels like an appropriate use of high-status labor to set interest rates once every few weeks based on new discussion of expert wisdom. Switching to more or less mechanical responses to routine bond price changes would undercut much of the reason for believing that the Fed’s leaders are doing high-status work.

The news media storytellers would have trouble finding entertaining ways of reporting adjustments that consisted of small hourly responses to bond market changes. Whereas decisions made a few times per year are uncommon enough to be genuinely newsworthy. And meetings where hawks struggle against doves fit our instinctive stereotype for important news better than following a rule does. So I see little hope that storytellers will want to abandon their focus on interest rates. Do the Fed governors follow the storytellers closely enough that the storytellers’ attention strongly affects the Fed’s attention? Would we be better off if we could ban the Fed from seeing any source of daily stories?

Do any other interest groups prefer stable interest rates over stable inflation rates? I expect a wide range of preferences among Wall Street firms, but I’m unaware which preferences are dominant there.

Consumers presumably prefer that their banks, credit cards, etc have predictable interest rates. But I’m skeptical that the Fed feels much pressure to satisfy those preferences.

We need to fight those pressures by laughing at people who claim that the Fed is easing when markets predict below-target inflation (as in the fall of 2008) or that the Fed is tightening when markets predict above-target inflation (e.g. much of 2004).

P.S. – The risk-reward ratio for the stock market today is much worse than normal. I’m not as bearish as I was in October 2008, but I’ve positioned myself much more cautiously than normal.


[1] – They appear to produce nearly identical advice under most conditions that the U.S. has experienced recently.

I expect inflation targeting to be modestly safer than NGDP targeting. I may get around to explaining my reasons for that in a separate post.

[2] – The link above gives daily forecasts of the 5 year CPI inflation rate. See here for some longer time periods.

The markets used to calculate these forecasts have enough liquidity that it would be hard for critics to claim that they could be manipulated by entities less powerful than the Fed. I expect some critics to claim that anyway.

[3] – I’m accepting the standard assumption that 2% inflation is desirable, in order to keep this post simple. Figuring out the optimal inflation rate is too hard for me to tackle any time soon. A predictable inflation rate is clearly desirable, which creates some benefits to following a standard that many experts agree on.

[4] – providing that you don’t pay much attention to Japan since 1990.

[5] – guesses which are error-prone and, if a more direct way of targeting inflation is feasible, unnecessary. The conflict between the markets’ inflation forecast and the Taylor Rule’s implication that near-zero interest rates would cause inflation to rise suggests that we should doubt those guesses. I’m pretty sure that equilibrium interest rates are lower than the standard assumptions. I don’t know what to believe about the output gap.

I was quite surprised by a paper (The Surprising Alpha From Malkiel’s Monkey and Upside-Down Strategies [PDF] by Robert D. Arnott, Jason Hsu, Vitali Kalesnik, and Phil Tindall) about “inverted” or upside-down[*] versions of some good-looking strategies for better-than-market-cap weighting of index funds.

They show that the inverse of low volatility and fundamental weighting strategies do about as well as or outperform the original strategies. Low volatility index funds still have better Sharpe ratios (risk-adjusted returns) than their inverses.

Their explanation is that most deviations from weighting by market capitalization will benefit from the size effect (small caps outperform large caps), and will also have some tendency to benefit from value effects. Weighting by market capitalization causes an index to have lots of Exxon and Apple stock. Fundamental weighting replaces some of that Apple stock with small companies. Weighting by anything that has little connection to company size (such as volatility) reduces the Exxon and Apple holdings by more than an order of magnitude. Both of those shifts exploit the benefits of investing in small-cap stocks.

Fundamental weighting outperforms most strategies. But inverting those weights adds slightly more than 1% per year to those already good returns. The only way that makes sense to me is if an inverse of market-cap weighting would also outperform fundamental weighting, by investing mostly in the smallest stocks.

They also show you can beat market-capitalization weighted indices by choosing stocks at random (i.e. simulating monkeys throwing darts at the list of companies). This highlights the perversity of weighting by market-caps, as the monkeys can’t beat the simple alternative of investing equal dollar amounts in each company.

This increases my respect for the size effect. I’ve reduced my respect for the benefits of low volatility investments, although the reduced risk they produce is still worth something. That hasn’t much changed my advice for investing in existing etf’s, but it does alter what I hope for in etf’s that will become available in the future.

[*] – They examine two different inverses:

  1. Taking the reciprocal of each stock’s original weight
  2. Taking the max(weight) and subtracting each stock’s original weight

In each case the resulting weights are then normalized to add to 1.

Book review: Fragile by Design: The Political Origins of Banking Crises and Scarce Credit, by Charles W. Calomiris, and Stephen H. Haber.

This book start out with some fairly dull theory, then switches to specific histories of banking in several countries with moderately interesting claims about how differences in which interest groups acquired power influenced the stability of banks.

For much of U.S. history, banks were mostly constrained to a single location, due to farmers who feared banks with many branches would shift their lending elsewhere when local crop failures made local farms risky to loan to. Yet comparing to Canada, where seemingly small political differences led to banks with many branches, it seems clear that U.S. banks were more fragile because of those restrictions, and less competition in the U.S. left consumers with less desirable interest rates.

By the 1980s, improved communications eroded farmers’ ability to tie banks to one locale, so political opposition to multi-branch banks vanished, resulting in a big merger spree. The biggest problem with this merger spree was that the regulators who approved the mergers asked for more loans to risky low-income borrowers. As a result, banks (plus Fannie Mae and Freddie Mac) felt compelled to lower their standards for all borrowers (the book doesn’t explain what problems they would have faced if they had used different standards for loans the regulators pressured them to make).

These stories provide a clear and plausible explanation of why the U.S. has a pattern of banking crises that Canada and a few other well-run countries have almost entirely avoided over the past two centuries. But they suggest the U.S. banking crises should have been more unique among mature democracies than was actually the case.

The authors are overly dismissive of problems that don’t fit their narrative. Commenting on the failure of Citibank, Lehman, AIG, etc to sell more equity in early 2008, they say “Why go to the markets to raise new capital when you are confident that the government is going to bail you out?”. It seems likely bankers would have gotten better terms from the market as long as they didn’t wait until the worst part of the crisis. I’m pretty sure they gave little thought to bailouts, and relied instead on overly complacent expectations for housing prices.

The book has a number of asides that seem as important as their main points, such as claims that Britain’s greater ability to borrow money led to its military power, and its increased need for military manpower drove its expansion of the franchise.