Steve Mayo![]() Legend ![]() ![]() ![]() ![]() Posts: 414 Joined: 10/11/2012 Location: Austin, TX ![]() | Inferential statistics require having a hypothesis, call it a theory, that you are trying to accept or reject. What's your hypothesis? You conclusion seems to be that PW is not useful because there is variability when you shift the starting/ending date. But, you give nothing against which to compare that variability. Here's a distribution graph looking at rolling quarterly returns for an OV portfolio (I forget now which one, and it really doesn't matter because most of them behave similarly) versus an EQUALLY-LEVERAGED market portfolio (i.e., both were at 200% margin and the same starting equity). It uses about 16 months of data ending in October. You can clearly see that both OV and SPY have high variability and OV is slightly higher risk on the downside, but the mean return for OV is twice that of the market; there is a clear rightward shift toward more and higher positive returns. You don't need a p-value here to see that your odds of making a profit (and more so, for not losing money) are going to be better with the OV portfolio This was a stock OV portfolio, so it's not a test of the value of PW. To do that experiment properly, you would need to compare non-PW portfolios and the market return to your results. Unfortunately, that's really hard to do. The experiment I did on the PW for OmniVesting.com did suggest that PW is better than no PW but again it takes a whole lot of more data when analyzing anything in the stock market due to that high inherent variability so a definitive answer just isn't possible. My point is just that variability is an inherent feature of the market. Saying that PW has variability too doesn't mean anything in that context. Is the variability higher or lower than a stock OV portfolio, or higher/lower than the overall market? And what's the nature of that variability? Generally, people care most about not losing money; they are willing to accept variability on the gain side if it reduces the frequency of trades on the loss side. Your data seems to support this as well. If you look at your rolling return summaries, very few of the intervals ended with a loss, and only the RSI failed to produce a positive result with 95% confidence - I'd already advised to avoid MDD, Calmar and CAR for technical reasons. Even not knowing what the overall market did in those timeframes, it still looks pretty impressive to me. [Edited by Steve Mayo on 11/14/2014 6:14 PM] ![]() ![]() |