3 ms·

>If you actually try to use the prediction of MA(3) you will find it is often very hard to use, because it's always just too late. I wonder if the machine learn

>If you actually try to use the prediction of MA(3) you will find it is often very hard to use, because it's always just too late. I wonder if the machine learning version had the same problem, or not. Why does the sensitivity matter when we know that the accuracy was so similar? The point on sensitivity does make me wonder how an exponential moving average would've performed though.

candiodari 8y ago
Because there are "chaotic cases" in statistics. Cases where there are so many possible patterns that stating which pattern the data follows doesn't have any less information than the data itself (or only has a little less information than the data itself). Financial market data is quite notorious for being one such case (if not there wouldn't be any poor mathematicians). So MA(3) is not a general indicator. It's not universally useful, and it isn't the only one this article considers. It was chosen from a great many possible indicators (go to tradingview.com, find a graph, expand the indicators tab, and look at the list. And keep in mind that ALL MA's are just one entry). So there's tens of thousands of indicators that MA(3) was chosen from. So the predictive value of MA(3) as an indicator in the general case is really only 1/10000th of what this person claims it to be (actually infinitely less, but let's assume you use, say, MA(200) as the limit of what you're willing to consider, which makes it a finite number). Predictive information is this: let's say I tell you I know what the outcome is of a football match. And at the end of the match, I pull the outcome out of a stack of papers. The size of that stack of papers is inversely related to how informative my prediction was. If I just put down one paper before the match, that's great. If I put down 1000. Not so great. For this guy, the stack of papers was pretty thick. So the article is saying "I can, out of a great many indicators, find one that performs similar to this machine learning algorithm". Unless the article also gives a clear and definitive reasoning for why THAT indicator was chosen for that dataset, it doesn't contain much information at all.
- longerthoughts 8y ago
  You're presenting this from the perspective of the author's selection of MA(3), but I think what you're really getting at is yes, MA(3) happened to be comparable to the ML model in this case, but the beauty of the ML model was that it knew to nearly replicate MA(3) out of a great many common indicators. Is that essentially what you're saying? If so, I'm curious how things like longer period moving averages or an exponential moving average perform relative to both MA(3) and the ML model in this case.

candiodari 8y ago

Because there are "chaotic cases" in statistics. Cases where there are so many possible patterns that stating which pattern the data follows doesn't have any less information than the data itself (or only has a little less information than the data itself). Financial market data is quite notorious for being one such case (if not there wouldn't be any poor mathematicians). So MA(3) is not a general indicator. It's not universally useful, and it isn't the only one this article considers. It was chosen from a great many possible indicators (go to tradingview.com, find a graph, expand the indicators tab, and look at the list. And keep in mind that ALL MA's are just one entry). So there's tens of thousands of indicators that MA(3) was chosen from. So the predictive value of MA(3) as an indicator in the general case is really only 1/10000th of what this person claims it to be (actually infinitely less, but let's assume you use, say, MA(200) as the limit of what you're willing to consider, which makes it a finite number). Predictive information is this: let's say I tell you I know what the outcome is of a football match. And at the end of the match, I pull the outcome out of a stack of papers. The size of that stack of papers is inversely related to how informative my prediction was. If I just put down one paper before the match, that's great. If I put down 1000. Not so great. For this guy, the stack of papers was pretty thick. So the article is saying "I can, out of a great many indicators, find one that performs similar to this machine learning algorithm". Unless the article also gives a clear and definitive reasoning for why THAT indicator was chosen for that dataset, it doesn't contain much information at all.

longerthoughts 8y ago
You're presenting this from the perspective of the author's selection of MA(3), but I think what you're really getting at is yes, MA(3) happened to be comparable to the ML model in this case, but the beauty of the ML model was that it knew to nearly replicate MA(3) out of a great many common indicators. Is that essentially what you're saying? If so, I'm curious how things like longer period moving averages or an exponential moving average perform relative to both MA(3) and the ML model in this case.