I was rich. Right?
I mean, that’s what my Bloomberg said. I’d just entered in an index built from companies with “cat” in their names — yes, the furry felines — hit a button and watched it back-test to an 849,751 percent return. Forget the internet, I thought. Cats are about to take over smart beta.
This is the story of the time I designed my own factor fund as a way of learning about one of Wall Street’s hottest trends — and its pitfalls. There are already ETFs that focus on themes, such as “biblically responsible” companies or ones popular with millennials. Quants have hundreds of style tilts, and their exploding popularity has created a gold rush for creators. I wanted in.
I notified Andrew Ang, head of factor investing strategies at BlackRock Inc. Everything in my program was by the book, I assured him. It was rules-based, equal-weighted and premised on a simple story — that people love cats.
“I love cats, too, and obviously cats are superior, so this is a great investment strategy,” Ang said, as I began to plot my career as a quant. Then he said, “I’m joking, of course.”
Alas, though decades of research back up the idea that you can sort stocks by traits like volatility and momentum and beat the market, Ang saw a far less glorious future for my Abyssinian anomaly. Actually, it failed virtually every conceptual test he could think of, a lesson for anyone convinced she’s found the key to riches in statistical engineering.
“The No. 1 thing is that it lacks an economic foundation,” Ang said.
Pitfall 1: Economic Intuition
So how, exactly, did I go about investing in cats? Factor funds rely on formulas, preset criteria that tell you which stocks to include and which to chuck out. It’s the idea behind things like value ETFs, which gather groups of shares that share the common characteristic of cheapness. The idea is that put together, they’ll beat the wider market.
My model buys any U.S. company with “cat” in it, like CATerpillar, or when “communiCATion” is in the name. It rebalances quarterly to keep trading costs low. That’s important for when Vanguard or BlackRock license it and charge a competitively low fee.
Full disclosure, I’m a dog person, and believe a company runs better when its spirit animal takes a labradoodle form. But building a dog factor portfolio leaves you with penny stocks like Junkiedog.com Inc., offered at $5 in 2013 and now trading at less than 2 cents.
It just so happens that when I ran the study with cats, it returned nearly 850,000 percent on a six-year backtest. That led me to ex-post facto assign an economic rationale to the benefit of cat-containing names. And although keyboard cat is an internet star, I’m told by Goldman Sachs Asset Management this isn’t a real economic story that would lead to robust returns over time.
“It’s very curious, and I appreciate the effort,” said Nicholas Chan, portfolio manager in the firm’s Quantitative Investment Strategies group. “But you came up with an investment idea that doesn’t have economic intuition. When we come up with an investment hypotheses, we’re economists first and statisticians second.”
BlackRock and Goldman build strategies around factors like value and low volatility because there’s a clear explanation for why they might work: investors under-price boring stocks, for example. By coming up with a thesis only after the results were known, I’ve data snooped my way into an unreliable factor. Unfortunately for me, there’s little evidence that investors are pulled towards catty stocks.
Pitfall 2: P-Hacking
Because of my stubborn desire to produce claw-some returns, I took my thesis and ran with it. Fine, so my first few trials didn’t spit out exactly what I wanted. No biggie, I’ve got the statistical resources of Bloomberg LP at my fingertips — so I tinkered with the data until it did.
At first, I only invested in companies beginning with C – A – T to capture the essence of my investment thesis. But that backtest spit out this:
Not great. But expand the data-set a little, CAT anywhere, and the returns look stellar, making my hypothesis look better. In the scientific community, this is called p-hacking, and it got me into trouble with Ang.
“We’re after broad and consistent sources of returns,” he said. “Since you’ve tweaked it so much, that gives me less confidence that there’s underlying economics in the source.”