I have been half-joking for a year and a half that maybe index funds should be illegal, but here is an almost entirely serious claim from Sanford C. Bernstein & Co. that they are worse than communism:
In a note titled “The Silent Road to Serfdom: Why Passive Investing is Worse Than Marxism,” a team led by Head of Global Quantitative and European Equity Strategy Inigo Fraser-Jenkins, says that politicians and regulators need to be cognizant of the social case for active management in the investment industry.
“A supposedly capitalist economy where the only investment is passive is worse than either a centrally planned economy or an economy with active market led capital management,” they write.
The basic idea is straightforward.1 The function of the capital markets is to allocate capital. Good companies’ stock prices should go up, so they can raise money and expand. Bad companies should go bankrupt, so that their resources can be re-allocated to more productive purposes. Analysts should be constantly thinking about whether companies are over- or underpriced, so that they can buy the underpriced ones and sell the overpriced ones and keep capital flowing to its best possible uses.
But when those thoughtful active analysts are replaced with passive index funds, the market stops serving that function. Whatever the biggest company is today will remain the biggest company tomorrow, and capital will never be allocated from bad uses to good ones. Indexing is cheaper, yes, but that’s because active management has positive externalities, and if no one will pay for it, those benefits will disappear.2
There is a lot of debate over whether this is actually how it works. For one thing, public stock markets are not really a mechanism for raising capital any more.3 But more fundamentally, there is an alternative view that the rise of passive investing will improve capital allocation, because bad active investors will be driven out but good ones will remain.4 The passive investors can’t influence relative prices, since they just buy the market portfolio, meaning that the fewer but better active investors will continue to make the capital allocation decisions. On this view, lower returns to active management are a sign that prices are more efficient and capital allocation is getting better.5
Fraser-Jenkins et al. don’t buy it.6 Their worry is that the growth in passive and quasi-passive products — not just true index funds but “smart beta” funds that invest based on historically predictive factors – has caused markets to become more correlated, as all the passive funds buy all the same stocks for the same reasons. They are not alone here; I like to quote Nevsky Capital’s final investor letter:
In such a world dominated by index and algorithmic funds historically logical correlations between different asset classes can remain in place long after they have ceased to be logical.
“By definition,” write Fraser-Jenkins et al., “passive flows of capital, given that they seek to emulate or replicate what has already occurred must be backward looking.” And a market that is more correlated, they argue, will do a worse job of allocating capital.7
Anyway it is a fascinating and delightful note but now let’s talk about something slightly different.
Imagine you are in charge of the economy. You decide how much of everything people should produce, and what the prices should be. It is hard! It’s hard to find out how much of the different things people want, and how much everything costs to make, and how to motivate people to make things, and so forth.
There are three basic approaches that you can take8:
- You can be bad at it. You can just announce prices and quantities, and get them wrong, and there will be shortages and bread lines and corruption.
- You can be good at it. But I just said it was hard, so being good at it probably requires you to have a really fancy computer that takes lots of data and crunches it to decide on prices and quantities and so forth.9
- You can have a market. You can just think of a market as a giant distributed computer for balancing supply and demand; each person’s preferences are data, and their interaction is the algorithm that creates prices and quantities.10
Choice 1 is more or less actually existing communism.11 Choice 3 is more or less capitalism.12 Choice 2 is more or less a fantasy, but the problem of figuring out how the computer would work is sometimes called the “socialist calculation problem,” and there is a shockingly wonderful novel about it, Francis Spufford’s “Red Plenty.”
The capital allocation problem is a subset of that more general resource allocation problem, and has similar answer choices: You can have clunky central planning, or you can have a market where investors compete to buy securities and thus set prices, or you can have an ideal but as-yet-undiscovered computer do the allocating. Or I guess13 pure indexing — everyone passively throws money at everything that there is, with no judgment at all — is an imaginable fourth answer, and is strictly worse than the others.14
But the Bernstein note isn’t really about pure passive indexing, just buying and holding the market-cap-weighted portfolio of global financial assets. Not many people do that anyway. Instead they buy a S&P 500 index fund, say, which is a market-cap-weighted portfolio of large-cap U.S. stocks — itself a capital-allocation decision. Or they buy a smart beta fund, which is a portfolio of stocks chosen and weighted based on some set of factors that have historically determined performance. Fraser-Jenkins et al. note ”the bizarre situation that there are more indices than there are large cap stocks.” They can’t all just be throwing money at every stock with no judgment. There is something a bit more going on there.
One way to think about them is that they are all crude algorithms for picking the best companies to allocate capital to. True, diversified, market-cap-weighted index funds are the crudest algorithm. They essentially assume that the companies that were good yesterday will probably be good tomorrow. This is not entirely true, of course, but it’s true enough to be useful, or at least to outperform most human money managers most of the time.
But there is no need to stick with such a crude algorithm. You might notice that, historically, some factors have been associated with outperformance. Companies with low price-to-book ratios might have outperformed companies with high price-to-book ratios, most of the time. So you might invest in a smart-beta value fund that focuses on buying stocks with low price-to-book ratios. If you do that, you are making a capital-allocation decision; you are giving money to companies that you think the market undervalues, whose fundamental performance should justify higher prices.
Even that is pretty crude, though. You could get more nuanced, and invest in a fancy quantitative hedge fund that digs through mountains of data to find signals that have historically predicted stock prices, and then applies those signals to future prices.
As these algorithms get more complicated, they also get more expensive to implement. S&P 500 indexing is basically free. Smart beta is more expensive, but everyone expects the price of smart beta to eventually fall to, basically, free.15 Fancy algorithmic hedge funds are expensive, but they are perhaps being disrupted by hobbyist sites that let random data scientists build algorithms to predict future stock prices, and then allocate money to the best-performing ones.
In Fraser-Jenkins’s taxonomy, all of these algorithms more or less count as “passive,” because they all look at historical correlations to predict future returns, as opposed to an “active” style that attempts to predict future returns based on a fundamental analysis of real economic factors.16 But that distinction is not particularly clean. A smart-beta fund focused on the value factor is in a sense doing very crude fundamental analysis: It looks at a company’s financial statements, compares them to its stock price, and buys stocks that seem to be mispriced based on their fundamentals. My Bloomberg View colleague Noah Smith wrote today about two financial economists who published a more sophisticated fundamental-investing algorithm — one that uses 28 items from financial statements — that seems to outperform the market. You could implement that algorithm “passively,” as it were, but it seems to be making investing decisions based on where it sees fundamental mispricing, not just on past stock prices.
And of course any human active investor is mostly relying on historical correlations and pattern-matching to make predictions of future fair value. You invest in Twitter because you think it will be the next Facebook, or you don’t invest in Twitter because you think it will be the next MySpace; you go long oil companies because real-economy conditions remind you of the last time oil rallied, or you go short because those conditions remind you of the last time oil tanked. Human investors reason by this sort of informal empiricism; robot investors just formalize it.
One broadly plausible thing to expect is that, in the long run, the robots will be better at this than the humans.17 Another broadly plausible thing to expect is that, in the long run, the robots will keep getting better at it.
What does it mean to say that the robots will keep getting better at it? Surely it means that robots will become more accurate at allocating capital to businesses that will perform best in the future. They will make those decisions partly by looking at the prices of financial assets (correlations among stock prices), and partly by looking at fundamental financial data (correlations between companies’ stock prices and their financial statements), and partly by looking at operational data (correlations between retail-industry stock prices and satellite pictures of retailers’ parking lots), and partly by looking at macroeconomic data (correlations between stock prices and interest rates or oil prices), and partly by looking at whatever else is handy and might somehow predict stock prices (correlations between stock prices and sunspots, or Twitter sentiment). And as more data is available and analytical techniques improve, they will get better and better at all of this. Along the way, there will be missteps and spurious correlations and herding into bad ideas, but in the very long run you’d expect the robots to constantly improve their capital-allocation decisions.
I mean, I would, though I don’t assert it as a law of economics or computer science. It’s more of an aesthetic sense about the possibilities for technology.
One other thing to consider is that eventually the best robot will predictably and repeatedly outperform the second-best robot, so why would you invest with the second-best robot? (This is, to some extent, what it means when people say that returns to algorithmic investing are declining.) Modern investment management supports a diversity of investing styles and products in part because people have truly different preferences about risk and where they want to invest, but also in part because it is hard to tell which style will perform better in the future. But as the Best Capital Allocating Robot gets better and better at allocating capital, it will be harder to justify investing in the Concentrated Mid-Cap Ultra Value Fund or whatever. Just invest in the Best Capital Allocating Robot!18 He’s the best.
So the logical/whimsical end point is, if you want to invest in U.S. business, you give your money to the Best Capital Allocating Robot (U.S. Division), and that robot — whose prowess has been proven over time in fierce competition — applies the best algorithms to the best data set to make the best possible capital-allocation decisions, and you get the best returns, and the economy gets the best capital allocation.
Obviously this is all nonsense. I mean! Obviously! The robots will always be imperfect, and random chance will always intrude, and decisions based on past data will never perfectly predict the future, and investing preferences will always differ, and you’ll never be able to scientifically identify the best algorithm, and competition and diversity will always be important, and all of this is silly.
But isn’t it fun? I have joked, a couple of times, about modern financial capitalism solving the socialist calculation problem. One of those times was about Uber, which is apparently working on an algorithm to allocate cars based on data in the world, without the intermediation of a price system. There will just … magically …be more Ubers when demand is high, and fewer when demand is low, and that won’t be because the invisible hand of the market pulls more self-interested drivers onto the streets as more passengers bid up the price of their service, but just because Uber has a computer program that is really smart at telling cars where to go. The Best Capital Allocating Robot will be like that, only for financial capital instead of cars.
That is: The market is the best algorithm ever developed for allocating capital. So far! But it also creates incentives for someone to build a better algorithm.
Again: I know this is silly. But as a wild extrapolation of the far future of financial capitalism, I submit to you that it is less silly than the ”Silent Road to Serfdom” thesis. That thesis is that, in the long run, financial markets will tend toward mindlessness, a sort of central planning — by an index fund – that is worse than 1950s communism because it’s not even trying to make the right decisions.