3 ms·
It's worth noting the less-than-well-known saying that "correlation does not imply correlation" (which is a very nice turn of phrase too). [1] In the introducti
by conistonwater 10y ago
It's worth noting the less-than-well-known saying that "correlation does not imply correlation" (which is a very nice turn of phrase too). [1] In the introduction they say the effect is significant at the 5% level, which for an exploratory data analysis doesn't mean much.
[1] http://andrewgelman.com/2014/08/04/correlation-even-imply-correlation/ http://andrewgelman.com/2014/08/04/correlation-even-imply-co...
- rahrahrah 10y agoReddit discussion https://www.reddit.com/r/investing/comments/5fd8q3/to_predict_the_stock_market_with_some_indicator_x/ https://www.reddit.com/r/investing/comments/5fd8q3/to_predic...
- conistonwater 10y agoSince I said "correlation does not imply correlation", not "correlation does not imply causation", I'm not sure what you're replying to.
- rahrahrah 10y agoIf you're not sure just ignore it. It's there for everybody else.
- stonemetal 10y agoIt explains what "correlation doesn't imply correlation" means to those of us who have never heard it before.
- alfalfasprout 10y agoMore likely there are cyclical effects that compounded together have a phase similar to the moon cycle. These kinds of effects are actually quite common if you have the data and computing power to mine for them. So there is likely an indirect causal element at play here. It's just not the moon. The problem is that this is sometimes wrong in a big way. Once you account for commissions, the need for risk controls (few investors will tolerate 20%+ drawdowns), and the need to realize returns intermittently I doubt this will outperform the S&P by more than a little bit. Even if there is some statistical edge here, it would require massive sums of money which would probably hurt returns due to market distortion effects. To truly take advantage of something like this you have to also use plenty of other signals and understand their relationships to develop a strategy that scales even if your signal loses some predictive power.