6 ms·
Overfitting, even on a 20 year dataset.
by AS37 5y ago
Overfitting, even on a 20 year dataset.
- Exuma 5y agoInteresting My ML knowledge is somewhat rusty... does overfitting occur more often on models with many input parameters (ie.. neural networks). His algorithm seems very simple, without really using ML at all, it's more of just a procedural 1-2-3 step thing, with no actual learning. Can you explain how overfitting works into his algorithm?
- dmillar 5y agoThere's no overfitting in the traditional/model sense here. This is a pretty rudimentary momentum strategy (long best performers). Implementing this on any kind of scale would be expensive to trade since it rebalance's daily. For momentum, Jegadeesh-Titman paper is much of the foundation for these types of strategies, if you're interested. But as others have pointed out, the "smart money" saturated this trade decades ago.
- Exuma 5y agoInteresting... what do you mean by this bit? > Implementing this on any kind of scale would be expensive to trade since it rebalance's daily.
- xwdv 5y agoIf you really want the answer to this question on your own, try out his algorithm and you will see what happens. Either he's right, and you make the suggested returns in a year, or he's wrong and you slowly realize why. It will be a learning experience. Don't take anyone's word for it.
- Exuma 5y agoTo do that would be to presume I doubt the validity of your claim (I DO believe what you said). I specifically asked how overfitting applies to a simple procedural technique, rather than a multi-dimensional method like a neural network. Trying myself, and losing money, doesn't explain how overfitting applies to procedural steps (as I said, my ML is rusty)
- seoaeu 5y agoThe concern with these sorts of strategies is that you might discover the problem quickly. Say for instance if the stock you shorted doubles in value and you lose all your money. Even if there's only a small probability of that happening in a given year, going bankrupt by definition negates all the gains you got previously. Worse still, when you short stocks it is actually possible to lose more than your entire investment so a strategy can work amazingly 99/100 times and still have negative expected value.
- AS37 5y agoThere are still parameters here. On {day/week/month} n, determine which {n} product(s) of {product brand}'s {product type} of {underlying asset type} gave the {highest/lowest/some of each} return. At n + {a number} {days/weeks/months} go {short/long/some of each} at {market price/limit price} at {market open/market close/time in day} the previously identified securities. Close your position on day n + {a number} afterward at {market price/limit price} at {market open/market close/time of day}.
- Exuma 5y agoAh ok, interesting. Thanks
- 0-_-0 5y agoI think you're right and overfitting certainly wouldn't explain bad performance of this algorithm, you can't overfit with such a simple strategy. The market can change in response to discovering this strategy though.
- quantumofalpha 5y agoMultiple hypothesis testing. This particular attempt seemed to have succeeded. But how many were tried that didn't? If you torture the data long enough it will confess.