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Serious question: I fully understand the idea of historical algorithms being no true indicator of the future. With that said....... If an algorithm consistent
by Exuma 5y ago
Serious question:
I fully understand the idea of historical algorithms being no true indicator of the future. With that said.......
If an algorithm consistently performs over 20+ years of data (through multiple black swan events, multiple major events), then why is it not safe to assume it likely will continue going forward? Wouldn't 20 years of "evidence" be a huge amount, such that future events likely wouldn't deviate much from that...?
- AS37 5y agoOverfitting, 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.
- elcomet 5y agoAnother issue is that once the algorithm becomes known, and used be many people, it will not work any more.
- xwdv 5y agoIt would eventually fail to perform even if it was kept secret.
- hungryforcodes 5y agoRight on point -- it's called "alpha decay".
- praptak 5y agoNo, because of the survivorship bias. People invent many algorithms all the time. You only hear about the ones which turned out exceptionally well.
- dmillar 5y agotrading costs, liquidity, structural changes in the economy etc. del prado's machine learning in finance book gives some good insight into common backtesting pitfalls/thinking that are present here. the "safe" assumption if you find a systematic strategy that backtests well is that your actual, go-forward sharpe ratio will print 50% of what your backtests suggest.
- Jeff_Brown 5y agoI don't know, but it surely depends in part on whether you've published the algorithm. Once a strategy becomes well-known, its gains are likely to be arbitraged away.
- iamadog4 5y agoJanuary effect.
- nlh 5y agoThis is the layman’s explanation I was given to me by my finance professor in college: In the extreme: If you take completely random price fluctuations and plot them on a graph then review them, they are no longer “random” - they become fixed because they’ve been recorded. It becomes possible to look for patterns, of which there will always be some (that’s the nature of randomness). It doesn’t matter the time scale. So your system that works over 20, 30, 50 years of past data is just fitting to the randomly-generated but not-actually-random once recorded set of data. From now into the future, the randomness will re-emerge and the system will fail. Another way of thinking about it: imagine you click a button to generate a random time series over 20 years, then build a trading algorithm based on that single click. Great - it works! Now click again and regenerate the entire series and see if it still works :)
- Grustaf 5y agoIt's very simple, the best strategy for the last 20 years (with hindsight) would be to buy the shares that have performed best over this period, but it wouldn't work better than chance going forward. Obviously, sitting down and creating that "strategy" would be silly, nobody would think that would work. But if you're using ML or just test a million random strategies, you could end up with something along those lines, a strategy that is optimised for the particular paths stock prices took up to know.
- teachingassist 5y ago> the best strategy for the last 20 years (with hindsight) would be to buy the shares that have performed best over this period, The best strategy doesn't seem to me to be straightforward to calculate, even with perfect hindsight. It might be to continually switch in the short-term most profitable asset, after taking into account the transaction costs and risks of influencing the market.
- Grustaf 5y agoSure, it's just an example. Let's say you're looking for the best buy and hold strategy then, the point is that what you actually want in the future is a strategy that doesn't depend on the particular circumstances in the preceding 20 years.
- ffggvv 5y agoimo in market cycle terms 20 years isn’t that long. especially since market paradigms shift. ie the last 20 years have been low interest rates, low inflation, with strong secular growth in tech and stagnation elsewhere, and globalization this varies a lot from the 60s or 70s where there was massive inflation, high in interest, etc.. we may be entering a new paradigm with the changes in fiscal, monetary policy and globalism running its course