It’s been nearly six months since Marks’ last memo. In the absence of any major shift in global markets, he has taken the opportunity to discuss a more ‘big picture’ issue – the removal of human beings from the investment process. This comes in three main forms; passive or index investing, quantitative or algorithmic trading, and AI or Machine Learning based systems. Marks looks at some of the problems they solve, and the distortions they create, before finally looking at their impact on investing. The full note is well over 8000 words (but well worth the read), so I’ve tried to capture the essence of his argument for you in under 2000 words.
Is passive investing wise?
“The wisdom of passive investing stems from the belief that the efforts of active investors cause assets to be fairly priced – that’s why there are no bargains to find.
And where do the weightings of the stocks in indices come from? From the prices assigned to stocks by active investors. In short, in the world view that gave rise to index and passive investing, active investors do the heavy lifting of security analysis and pricing, and passive investors freeload by holding portfolios determined entirely by the active investors’ decisions. There’s no such thing as a capitalization weighting to emulate in the absence of active investors’ efforts.
The irony is that it’s active investors – so derided by the passive investing crowd – who set the prices that index investors pay for stocks and bonds, and thus who establish the market capitalizations that determine the index weightings of securities that index funds emulate. If active investors are so devoid of insight, does it really make sense for passive investors to follow their dictates?”
“How much of the investing that takes place has to be passive for price discovery to be insufficient to keep prices aligned with fair values? No one knows the answer to that. Right now, about 40% of all equity mutual fund capital is invested passively, and the figure may be moving in that direction among institutions. That’s probably not enough; most money is still managed actively, meaning a lot of price discovery is still taking place. Certainly 100% passive investing would suffice: can you picture a world in which nobody’s studying companies or assessing their stocks’ fair value? I’d gladly be the only investor working in that world. But where between 40% and 100% will prices begin to diverge enough from intrinsic values for active investing to be worthwhile? That’s the question. I don’t know, but we may find out . . . to the benefit of active investing.”
How smart is 'smart-beta'?
“What all the above means is that for a stock to be added to index or smart-beta funds is an artificial form of increased popularity, and it is relative popularity that determines the relative prices of stocks in the short run.
The large positions occupied by the top recent performers – with their swollen market caps – mean that as ETFs attract capital, they have to buy large amounts of these stocks, further fuelling their rise. Thus, in the current up-cycle, over-weighted, liquid, large-cap stocks have benefitted from forced buying on the part of passive vehicles, which don’t have the option to refrain from buying a stock just because its overpriced.
Like the tech stocks in 2000, this seeming perpetual-motion machine is unlikely to work forever. If funds ever flow out of equities and thus ETFs, what has been disproportionately bought will have to be disproportionately sold. It’s not clear where index funds and ETFs will find buyers for their over-weighted, highly appreciated holdings if they have to sell in a crunch. In this way, appreciation that was driven by passive buying is likely to eventually turn out to be rotational, not perpetual.”
The illusion of liquidity
“If bad news or a downturn in investor psychology causes the market to drop, invariably there’ll be a price at which an ETF holder can sell, but it may not be a “good execution.” The price received may represent a discount from the value of the underlying assets, or it may be less than it would have been if the market were functioning on an even keel.
If you withdraw from a mutual fund, you’ll get the price at which the underlying stocks or bonds closed that day, the net asset value or NAV. But the price you get when you sell an ETF – like any security on an exchange – will only be what a buyer is willing to pay for it, and I suspect that in chaos, that price could be less than the NAV of the underlying securities. Mechanisms are in place that their designers say should prevent the ETF price from materially diverging from the underlying NAV. But we won’t know if “should” is the same as “will” until the mechanisms are tested in a serious market break.”
“The weakness lies in the assumption that a vehicle can provide more liquidity than is provided by its underlying assets. There’s nothing wrong with the fact that ETFs may prove illiquid. The problem will arise if the people who invested in them did so with the expectation of liquidity that isn’t there when they need it.”
Soros’ theory of market reflexivity
“The actions of market participants change the market. Nothing in a market always continues, independent and unchanged. A market is nothing more than the people in it and the decisions they make, and the behavior of those people shapes the market. “
“It seems obvious that a formula’s application and popularization eventually will bring an end to its effectiveness. Let’s say (in an incredibly simplified example) your study of the market shows that small-company stocks have beaten the market over a given period, so you overweight them.
Since “beating the market,” “out-appreciating” and “out-performing” often are just the flip side of “becoming relatively expensive,” I doubt any group of stocks can outperform for long without becoming fully- or over-priced, and thus primed for underperformance.
And it seems equally clear that eventually others will detect the same “small-cap effect” and pile into it. In that case, small-cap investing will become widespread and – by definition – no longer a source of superiority.”
“The trend toward passive investing hasn’t occurred because the returns there have been great. It’s because the results from active management have been poor, or at least not good enough to justify the fees charged.
Now clients have wised up, and unless something changes with regard to the above, the trend toward passive investing is going to continue. What could arrest it?
- More active managers could become capable of delivering alpha (but that’s not likely).
- The markets could become easier to beat (that’ll probably happen from time to time).
- Fees could come down so that they’re competitive with passive investment fees (but in that case it’s not clear how the active management infrastructure would be supported).
Unless there are flaws in the above reasoning, the trend toward passive investing is likely to continue. At the very least, it reduces or eliminates management fees, trading costs, overtrading and human error: not a bad combination.
Of course, there are active investors who outperform. Not most, and not half. But there’s a minority who do earn their fees, and they should continue to be in demand.”
On quantitative investing
“Quantitative investing makes good use of the ability of computers to handle vast amounts of data and their freedom from human error. In short, I think computers can do more than the vast majority of investors, and do it better.
Now for limitations. I think of quantitative investing as also a free-riding strategy: it profits from disequilibria caused by others. The supply of “nickels and dimes” is limited to the extent of those disequilibria, and thus only a limited amount of capital can be run this way to great advantage.”
“Computers can do an unmatched job dealing with the things that can be counted: things that are quantitative and objective. But many other things – qualitative, subjective things – count for a great deal, and I doubt computers can do what the very best investors do.”
Can machines do it better?
“Machine learning is still in its infancy. It may be that AI and machine learning will someday permit computers to act as full participants in the markets, analyzing and reacting in real time to vast amounts of data with a level of judgment and insight equal to or better than many investors. But I doubt it will be anytime soon, and Soros’s Theory of Reflexivity reminds us that all those computers are likely to affect the market environment in ways that make it harder for them to achieve success.”
“The greatest investors aren’t necessarily better than others at arithmetic, accounting or finance; their main advantage is that they see merit in qualitative attributes and/or in the long run that average investors miss. And if computers miss them too, I doubt the best few percent of investors will be retired anytime soon.
Will machine learning enable computers to study the entirety of financial history, figure out what made for the most successful investments, and sense what will work in the future? I have no way of knowing, but even if so, I think that’s not enough. Computers, artificial intelligence and big data will help investors know more and make better quantitative decisions. But until computers have creativity, taste, discernment and judgment, I think there’ll be a role for investors with alpha…
If the day comes when intelligent machines run all the money, won’t they all (a) see everything the same, (b) reach the same conclusions, (c) design the same portfolio, and thus (d) perform the same? What, then, will be the route to superior performance? Humans with superior insight. At least that’s my hope.”
The impact on investing
“Most people can’t and don’t beat the market, especially in markets that are more-efficient. On average, all portfolios’ returns are average before taking costs into account.
Active management introduces considerations such as management fees; commissions and market impact associated with trading; and the human error that often leads investors to buy and sell more at the wrong time than at the right time. These all have negative implications for net results.
The only aspect of active management with potential to offset the above negatives is alpha, or personal skill. However, relatively few people have much of it.
For this reason, large numbers of active managers fail to beat the market and justify their fees. This isn’t just my conclusion: if it weren’t so, capital wouldn’t be flowing from active funds to passive funds as it has been.
Regardless, for decades active managers have charged fees as if they earned them. Thus the profitability of many parts of the active investment management industry has been without reference to whether it added value for clients.”
Read the full memo here.
Patrick was one of Livewire’s first employees, joining in 2015 after nearly a decade working in insurance, superannuation, and retail banking. He is passionate about investing, with a particular interest in Australian small-caps.
A very interesting article which sets out what most of us know already, that is not a criticism but it is the problem. To summarise. If AI is that good all will arrive at the same conclusion. Outperforming active managers are rare and therefore too expensive. When the market corrects/crashes as it will one day those passive funds will be a blood bath as every index is required to sell. Patrick short of not getting out of bed, tell me what I should do!!
Hi Derek, thanks for your comment. I think the key is to assess each investment on its own merit. You can't control what happens to the price levels of stocks in a big index, but if the underlying business is strong, it should survive any major selloffs. Please note that this is not financial advice, and I've not considered any of your personal circumstances.