Yes. I use mathematical and intuitive approaches. The intuitions may be relied upon until such time that additional testing/research can confirm/reject those hypotheses. At that point, the intuitive parameters are replaced with the mathematical results of the testing/research.
Let me see if I understand what you are saying: 75% probability of prediction is not the same as positive profit expectancy? In other words, just like selling OTM options, one has usually > 80% win probability but the 20% that you lose can wipe you out?
Thank you. I read the two articles you posted prior. Very interesting, I used to try modeling using similar parameters listed by the papers but did not (and did not know how to) apply machine learning. Basically I used multi parameters regression to try forecast. It didn't work very well for me. Question: Do you have any suggestion how I can learn Support Vector Machine learning, or are there software package that can do SVM? Regards,
Exactly. It does not have to be options, there are a lot of strategies on linear instruments that are asymmetric by nature. Let's say you're an HFT trader and are making markets in a stock X. You don't really try to do anything sophisticated, simply buy for the bid and right away try sell at the offer. The median result of each trade will be the bid/ask spread (let's say 1 cent), but once in a blue moon when you are sitting on the bid, news come out and the stock moves $1 down.
Are you making a one day ahead forecast, up or down, of a two day moving average? If so, your null is 75% accuracy, your model is adding nothing. Just guess up if today's close is above MA2 and guess down if below. You'll be right 75% of the time. If the math of why is beyond you, here is a simulation that illustrates the point: > require(quantmod) > a <-data.frame(rnorm(10001)) > b <- cbind(a,c(rollmean(a$rnorm,2),0)) > colnames(b) <- c('Returns','MA2') > (sum(sign(b$Returns * b$MA)+1)-1)/20000 [1] 0.7533