This week we saw two interesting bank collapses: Silvergate Capital Corporation, and SVB Financial Group.
This is a reminder that diversification is important.
The most basic problem in both cases is that they got money from a rather undiverse set of depositors, who experienced unusually large fluctuations in their deposits and withdrawals. They also made overly large bets on the safety of government bonds.
I recently noticed similarities between how I decide what stock market evidence to look at, and how the legal system decides what lawyers are allowed to tell juries.
This post will elaborate on Eliezer’s Scientific Evidence, Legal Evidence, Rational Evidence. In particular, I’ll try to generalize about why there’s a large class of information that I actively avoid treating as Bayesian evidence.
AI looks likely to cause major changes to society over the next decade.
Financial markets have mostly not reacted to this forecast yet. I expect it will be at least a few months, maybe even years, before markets have a large reaction to AI. I’d much rather buy too early than too late, so I’m trying to reposition my investments this winter to prepare for AI.
This post will focus on scenarios where AI reaches roughly human levels sometime around 2030 to 2035, and has effects that are at most 10 times as dramatic as the industrial revolution. I’m not confident that such scenarios are realistic. I’m only saying that they’re plausible enough to affect my investment strategies.
Book review: Investing Amid Low Expected Returns: Making the Most When Markets Offer the Least, by Antti Ilmanen.
This book is a follow-up to Ilmanen’s prior book, Expected Returns. Ilmanen has gotten nerdier in the decade between the two books. This book is for professional investors who want more extensive analysis than what Expected Returns provided. This review is also written for professional investors. Skip this review if you don’t aspire to be one.
A conflict is brewing between China and the West.
Beijing is determined to reassert control over Taiwan. The US, and likely most of NATO, seem likely to respond by, among other things, boycotting China.
We should, of course, worry that this will lead to war between China and the US. I don’t have much insight into that risk. I’ll focus in this post on risks about which I have some insight, without meaning to imply that they’re the most important risks.
Such a boycott would be more costly than the current boycott of Russia, and the benefits would likely be smaller.
How can I predict whether the reaction to China’s action against Taiwan will be a rerun of the response to the recent Russian attack on Ukraine?
I’ll start by trying to guess the main forces that led to the boycott of Russia.
I previously sounded vaguely optimistic about the Baze blood test technology. They shut down their blood test service this spring, “for the foreseeable future”. Their web site suggests that they plan to resume it someday. I don’t have much hope that they’ll resume selling it.
Shortly after I posted about Baze, they stopped reporting numbers for magnesium, vitamin D, and vitamin B12. I.e. they only told me results such as “low”, “optimal”, “normal”, etc. This was apparently was due to FDA regulations, although I’m unclear why.
I’d like to believe that Baze is working on getting permission to report results the way that companies such as Life Extension report a wide variety of tests that are conducted via LabCorp.
At roughly the same time, Thorne Research announced study results of a device that sounds very similar to the Baze device (maybe a bit more reliable?).
Thorne is partly a supplement company, but also already has enough of a focus on testing that I don’t expect it to use tests primarily for selling vitamins, the way Baze did.
I’m debating whether to invest in Thorne.
The ESG investing movement (environmental, social, and corporate governance) is becoming potentially important, potentially good, and potentially corrupt.
I’ll walk through some of the sources of influence on it.
I’ve been noticing more discussion recently about recession risks. Here are some disorganized thoughts.
Here’s an article suggesting that the 1949 recession is a good model for what we face.
The similarities that I consider important are:
Interest rates have declined from over 5% in the late 1960s to under 0.5% in 2020 (I’m using long-term treasury bond rates as an estimate of pure risk-free rates).
There’s clearly something unusual about savings rates, and it’s causing a semi-stable decline in interest rates.
In my review of Shut Out, I guessed that demographic effects of the boomer generation explain 25% the housing boom, via high savings rates and the resulting low interest rates. I’m now revising that estimate down to 10%.
I now see a similar but bigger effect from a different set of demographic changes. I changed my mind due to the paper by Mian, Straub, and Sufi titled What Explains the Decline in r*? Rising Income Inequality Versus Demographic Shifts (think or r* as an idealized version of interest rates).
Wealth affects savings rates more than age does.
Mancur Olson’s The Rise and Decline of Nations tells us that in stable times special interest groups tend to slowly create increasingly rigid agreements to cement their income streams. Major wars, and other cataclysms of that size, occasionally sweep away those rigidities, creating conditions under which faster economic growth is possible.
COVID has been much less cataclysmic than what Olson talks about. Yet I see hints that the recent pandemic has had effects that weakly resemble war. I see stronger evidence that the pandemic was a useful trigger for overcoming the effects status quo bias. I’m writing this post to help clarify my thoughts about how significant these effects will be, and how they’ll affect stock markets.
Is the stock market’s impressive performance partly due to expectations that the pandemic caused a lasting increase in profits?
I’ll guess that it explains one quarter of the stock market’s rise. A fair amount of that guess reflects my intuitions about how much can’t be explained by other factors. That approach is at least as error-prone as estimating individual pandemic effects. So please interpret this post as mostly groping around in the dark.