Computerized stock pickers could very well beat humans in the future. Witness what AI-driven machines have been able to accomplish in chess, at the very complex game of go, and even at the multitudinous choices present in StarCraft. But right now, despite serious advances in modelling the market, digital stockpickers still have a long way to go.
Richard Dewey outlines why beating the market is a very hard thing to do in this article from Bloomberg:
One reason machine investing remains an elite domain is obvious. By definition, most investors can’t beat the average, and every computer that momentarily finds a winning formula will soon face others trying to outwit it. But it turns out that investing is also simply harder than, say, predicting your next Amazon purchase.
“It’s one of the most difficult problems in applied machine learning,” says Ciamac Moallemi, a professor at Columbia Business School and a principal at Bourbaki LLC. Here are just some of the devilish problems financial engineers are trying to crack:
- The Data Keeps Changing. Or, in quantspeak, it’s nonstationary. An example of stationary data might be the distance between your left eye and your nose. Unless you have plastic surgery, it’s a constant. If a machine is fed hundreds of pictures of you, it will be able to identify you with high probability.
- There’s More Noise Than Signal. Stocks move all the time, and not always for any discernible reason. Most market moves are what economists call noise trading. To go back to the image-recognition analogy, imagine a computer trying to identify people in photos that were taken in the dark. Most of the data in those pictures is noise—useless black pixels.
- The Edge You’re Looking for Is Really Small. An obvious signal—for example, to buy stocks on the first day of every month—is not of much use. If that worked in the past, it was probably just a fluke, and even if it isn’t, it’s going to be quickly discovered and traded away by others.
