Rob Arnott (and his colleagues) of Research Affiliates had written a rather lucid paper on stock market bubbles in April 2018. You can find it here. If you are either an investor or a student or, perhaps, importantly, a teacher, it is a MUST-READ.
They brilliantly shred the arguments that somehow painfully try to prove that stock markets were efficient. They are nothing but pathetic attempts at post-hoc rationalisations or rationalising a conclusion that the authors had already arrived at. Such papers should ideally be classified under ‘propaganda’ rather than under ‘research’, notwithstanding all the geeky mathematics deployed.
On market efficiency:
The efficient market hypothesis has been stretched to fit observed market behavior, by allowing cross-sectional and intertemporal variations in risk premia. Prices adjust until the marginal investor becomes willing to assume both market risk and assorted factor-related risks. The market’s willingness to bear these risks varies over time. In this view, high valuation levels don’t represent mispricing; the risk premia just happen to be sufficiently low so as to justify the prices.
These models benefit from being constructed on a post hoc basis to be consistent with market events. Fair enough, because a useful model needs to be consistent with observable data. But the models often shift problems in the observable data, such as puzzlingly high volatility in price-to-dividend ratios, into unobservable nooks and crannies. In other words, perhaps bubble-like prices can be perfectly rational as long as we accept curiously high volatility in the curvature of investors’ utility functions. Sadly, these hypotheses and models lack a key attribute of scientific method: they are unfalsifiable. No practical difference exists between an inefficient market and an efficient market in which risk premia vary in this fashion.
All students of finance must memorise these two paragraphs.
On market timing:
At the beginning of 2000, the 10 largest market-cap tech stocks in the United States, collectively representing a 25% share of the S&P 500 Index—Microsoft, Cisco, Intel, IBM, AOL, Oracle, Dell, Sun, Qualcomm, and HP—did not live up to the excessively optimistic expectations. Over the next 18 years, not a single one beat the market: five produced positive returns, averaging 3.2% a year compounded, far lower than the market return, and two failed outright. Of the five that produced negative returns, the average outcome was a loss of 7.2% a year, or 12.6% a year less than the S&P 500.
Lesson: Timing matters. If you buy high, you reap lower returns unless you were lucky to flip the stock quickly and find a greater fool to buy it off you.
Over the first quarter of 2018, Tesla has been an excellent example of a micro-bubble. Tesla’s current price is arguably fair if most cars are powered by electricity in 10 years, if most of these cars are made by Tesla, if Tesla can make those cars with sufficient margin and quality control and can service the cars properly, and if Tesla can raise additional capital sufficient to cover a $3 billion annual cash drain and another billion to service its debt. To us, that seems an unduly optimistic array of assumptions, especially given the magnitude of Tesla’s debt burden. Such an argument ignores the deep pockets of competitors and the common phenomenon of disruptors being themselves disrupted by newcomers.
It boggles the imagination to hear people speaking of “investing” in bitcoin, an electronic entity that offers no hope of future operating profits or dividends, is little used as a surrogate for money in transactions (trading volume is well over 100 times as large as spending volume), offers an uncertain longer-term use case, and has no objective basis to determine fundamental value.
Will bitcoin and a handful of other cryptocurrencies settle in and become a stable store of value, akin to gold or sovereign currencies? Perhaps.
Those of us who are libertarians, wary of government control of the money supply, are rooting for that outcome.
That said, how many investors are holding cryptocurrencies for any purpose other than the expectation that someone else will pay a higher price at some point in the future?….
Even if we assume that bitcoin has merit as a libertarian alternative to government-sourced fiat currency, it’s hard to justify today’s 1,500 different cryptocurrencies. Many of these were launched with the singular goal of making the originator of the cryptocurrency wildly wealthy in an ICO (initial coin offering).
On the ‘costs’ and distortions of the crypto bubble:
The bitcoin bubble also serves as a wonderful example of how bubbles create harmful distortions in the real economy. The website Digiconomist estimates the run-rate annual electricity utilization of the bitcoin network at 56 billion kilowatt-hours. That’s more than enough to power all the households in Los Angeles for a year, and nearly enough to meet all of Israel’s power demands. Bitcoin already consumes about 0.25% of total global electricity consumption! All just to “produce” new coins on a nonphysical ledger and move these coins around on electronic exchanges….
From the footnotes: One aspect of bitcoin-related energy consumption that won’t disappear so easily is the residual carbon footprint left by bitcoin mining, which is currently dispensing as much CO2 a year as 1,000,000 transatlantic flights.
On the bubble in current technology stocks:
At the end of January 2018, the seven largest-cap stocks in the world were all tech fliers: Alphabet, Apple, Microsoft, Facebook, Amazon, Tencent, and Alibaba. Never before has any sector so dominated the global roster of largest market-cap companies. At the peak of the tech boom, four of the top seven companies by market cap were in the tech sector, and at the peak of the oil bubble, five of the top seven were in the energy sector. Only the Japanese stock market’s bubble at yearend 1989 has matched today’s tech sector dominance of the global market-capitalization league tables. Not only do we have the FANGs, we have FANG+ futures, affording investors a chance to buy the world’s trendiest tech stocks with almost no collateral, and the list is amended quarterly to make sure only the trendiest are on the list.
Yet, how shorting can be perilous:
During the three months August–October 2008, the Zimbabwean dollar plunged from 10 to 1000 per the US dollar, a 100-fold currency collapse. At first, the Zimbabwean stock market was unfazed, rising 500-fold in just eight weeks, while the currency fell 10-fold. Thus, in US dollar terms, the stock market rose an astounding 50-fold over those eight weeks. In the next two weeks, however, the stock market toppled 85% and the currency tumbled another 3-fold. Adjusted for the plummeting Zimbabwean currency, the nation’s stock market plunged 95% in two weeks.
There is really no cataylst that trigger a bubble collapse. I keep saying that to my students and clients:
The all-too-common question—“What’s the catalyst that will cause the market to turn, the bubble to burst?”—is simply a distraction because a catalyst is, by definition, a surprise to most of the market.
What, for example, was the proximate catalyst that ended the tech bubble in March 2000? We have yet to hear a persuasive answer. Yet, the quest for a catalyst is fun and potentially profitable; after all, a few people will identify the catalyst, if there is one, in advance.
You can see why I was very excited about reading this paper and very excited to share it here too. It is a treasure trove of common-sense, practical and valuable wisdom.
Many, many thanks to Rob Arnott and his co-authors.