Tag Archives: cullen-roche

Have The Robots Broken The Stock Market?

As more and more economic data comes out, it is becoming clear that China really isn’t having much of an impact on the US economy. Today’s initial jobless claims is about as real-time as it gets, and they’re near all-time lows. So now, Monday’s flash crash is becoming an even hotter topic. Here’s my experience, and why more human involvement might not be such a bad thing. The markets were sloppy last week, and we went out on a bad note. Sentiment was very negative. And when Chinese stocks continued to crash on Sunday, it looked like we might be on the verge of something nasty. Uncertainty was everywhere. And then the robots took control. I watched the futures market almost all night on Sunday, and we were seeing 100-point moves in the Dow Futures contract within a few minutes. This was not human controlled. And it was not rational. I reached out to a friend of mine who has some experience in High Frequency Trading, and here’s what he said to me: ” I am beginning to wonder if certain algorithms don’t get confused during these liquidity events. This week’s trading looked like momo [momentum] algos chasing price which turned into a positive feedback loop on itself until the system just crashed. ” That statement resonated with me. After all, I’ve traded through the rise of the robots, and I used to specialize specifically in illiquid markets, but I don’t recall anything quite like this other than the Flash Crash of 2010. When I woke up on Monday morning and watched the market open, I’d never seen so many broken positions. There were dozens of ETFs trading at 25-50% discounts to their NAV. I was buying the Schwab U.S. Mid-Cap ETF (NYSEARCA: SCHM ) at a 25% discount. (click to enlarge) That is, I was playing market maker with my small asset management business in a broken market, because I looked at the prices and I knew for a fact, without seeing the Intraday Indicative Value of the ETF, that we were trading at irrational discounts. The robots failed to do that. Humans like myself, who have traded in these kinds of markets before, are like first responders. We run into the fire when everyone else is running away from it. Had there been more human market-making involvement that morning, I doubt this price discrepancy would have even occurred. I am certainly not against the rise of the robots and the technological progress we’re making, but some of the developments of the last few years do make me wonder if we’re relying a bit too much on the robots at times… Disclosure: My firm is net long SCHM (and has been for a long time) in many accounts. P.S. – I should add that this does not add credence to some of the commentary that ETFs are illiquid or inefficient products. In fact, ETFs traded precisely how we should have expected them to. And when some underlying positions didn’t open, many irrational sellers sold into the panic. This was not a product error. This was a human error. I wasn’t the only one who recognized this error in real-time.

Smart Beta, Dumb Money And EMH

Someone asked me about Smart Beta in the forum the other day and I got to thinking about this. Indexers are all basically chasing some form of beta. But some indexers chase beta in stupid ways and some indexers chase beta in smart ways. An increasingly common example of this is the many forms of factor investing that have become popular in recent years (in case you haven’t noticed, I don’t like factor investing – see here and here ). I am generalizing, but I tend to believe that factor investing is just a new clever way to get people to pay higher fees for owning index funds. Now, this particular reader asked about the profit factor so I went exploring. It turns out that there are more than a few ETFs that track this profit factor. The largest one is the WisdomTree MidCap Earnings ETF ( EZM), which has 770 million in assets and “seeks to track the investment results of earnings-generating mid-cap companies in the U.S. equity market.” So, I go and compare this fund to the Russell Mid-Cap Index. It actually appears to be beating the index since inception, but it has a 99% correlation. Something doesn’t smell right about that. So, I look under the hood and find that it actually deviates from the Mid-Cap Index quite a bit. While the Mid-Cap Index has an average market cap of 10.5B this fund has a market cap of just 4.2B. Ah, so there’s the outperformance. It’s not profits, it’s just higher risk smaller cap stocks. And if you layer on the Russell 2,000 Small Cap Index, whose market cap is 1.5B, you get a near perfect replica of EZM. This is precisely what I expected given that I’ve run some version of this experiment almost every day for the last few years when assessing people’s portfolios. The kicker is, this fund isn’t “smart” at all. The only thing that’s smart about it is that it deviates from the Russell Mid-Cap Index giving it the appearance of better performance. And so what we have here is a sort of sad case of dumb money chasing market inefficiency and proving that the only thing inefficient here is their factor chasing charade. And in doing so they’re paying 0.38% per year for a fund that costs as low as 0.07% elsewhere. That’s almost $2.5 million in annual fees being flushed down the drain there. And that’s just one fund out of a growing list of hundreds and maybe thousands. I’d laugh if it didn’t make me sad. Share this article with a colleague

The One Factor To Explain Them All

Yesterday’s post on hedge funds got me thinking again about how vague “risk factors” are. CAPM uses a one factor model showing that risk explained why certain assets performed better than others.¹ Basically, take more risk and you’ll generate a better return. That didn’t exactly explain things fully though. In fact, higher risk often correlates with worse returns.² Over the course of the last 25 years the idea of “factor investing” has really boomed. And investment companies loved this because they could market specific stylized facts that explained why the markets do certain things and why you should pay them high fees so they can take advantage of those things for you. Heck, even hardcore passive investors, who are notorious fee avoiders, will trip over themselves buying higher fee funds trying to guess the best factors to own at certain times. As a result, we got the small cap factor and the value factor and the momentum factor and all sorts of other factors. I think we’re up to 1,000+ factors now. There are so many I could just start making them up. And I will. Right now. For instance, companies whose founders are dog owners might outperform companies whose founders are cat owners. This is a perfectly logical assumption because dogs are better than cats so company founders who own dogs must be smarter than company founders who own cats. So, we now have the dog vs cat factor. If I tweak some data and find it’s statistically meaningful over long periods of time then I might even start a high fee fund to sell you. No cat owners allowed, obviously. Okay, okay. I am being stupid. I know. But you see my point, right? A lot of these “factors” could be nothing more than slick data mining by someone who found a pattern that doesn’t really exist. We want to understand and be able to predict things so badly that we often find stylized facts where they don’t even exist. But maybe we just can’t know. That doesn’t mean it’s not worth trying to find out or to try guessing about future outcomes. But we should start from one general factor: The We-Know-A-Lot-Less-Than-We-Think-We-Know Factor This doesn’t require some law of the financial markets that says anything has to outperform anything else over the long-term. Yes, we know that stocks will generally beat bonds because they’re a contract that gives the equity owner greater claim to profits than the bonds, but that doesn’t mean stocks have to outperform bonds or even that they’re more risky (whatever “risk” means in the context of this discussion to begin with, but that’s a whole other matter). The point is, you don’t necessarily earn a “risk premia” in stocks. You earn a contractual premia assuming the firm earns enough profit to pay it out (good luck predicting which firms will generate the highest profits in the future) and a bunch of apes with keyboards try to guess what the future value of those profits will be. Importantly, no one needs the efficient market hypothesis to understand why markets are really hard to beat. You just need the basic arithmetic of global asset allocation to understand that . This whole monetary system is something humans created from thin air. And we have, at best, an imprecise understanding of what financial assets are really worth at certain times and so the best we can do is try to understand the world for what it is, slap together some sound assumptions about the future, reduce our frictions, manage the known risks as best as possible and hope it doesn’t all fall apart at some point. Is it really much more complex than that? ¹ – Here’s the Three Factor Model. Here’s the Five Factor Model. Here’s a 100+ Factor model paper. Here’s a real model. 2 – See this paper titled ” High idiosyncratic volatility and low returns: International and further U.S. evidence “.