the-daily-trading-coach-101-lessons-for-becoming-your-own-trading-psychologist-brett-steenbarger-_2009_-a23

322 THE DAILY TRADING COACHprice. Thus our analyses can only use 998 of the data points of the 1,000that we downloaded. If you want an even 1,000 data points, you’d have todownload the last 1,002 values.With a bit of practice, all of this will become second nature. It willtake only a minute or two to open your data files, copy and paste the rawdata, write your formulas, and copy the cells to complete your sheet. In thisexample, we are exploring how the prior day’s return is related to the nextday’s return. We’re setting the spreadsheet up to ask the question, “Doesit make sense to buy after an up day/sell after a down day; does it makesense to sell after an up day/buy after a down day; or does it make no apparentdifference?” I call the independent variable the candidate predictor,because we don’t really know if it is related to our variable of interest. It’salso only a candidate because we’re not conducting the statistical significancetests that would tell us more conclusively that this is a significantpredictor. Rather, we’re using the analysis much as we used the charting inthe prior lesson: as a way to generate hypotheses.Remember, in the current examples, we’re using historical relationshipto describe patterns in markets, not to statistically analyzethem. We’re generating, not testing, hypotheses.If I had been interested in examining the relationship between the priorweek’s price change with the next week’s return, the spreadsheet wouldlook very similar, except the raw data would consist of weekly index data,rather than daily. In general, it’s neatest for analysis if you are investigatingthe impact of the prior period’s data on the next period. This ensuresthat all observations are independent; there are no overlapping data.To see what I mean, consider investigating the relationship of theprior week’s (five-day) price change on the price change over the next fivetrading days utilizing daily market data. Your independent variable in columnF would now look like “=((E7-E2)/E2)*100)”—price change over thepast five days. The dependent variable in column G would be written as“=((E12-E7)/E7)*100”: the next five-day’s price change. Note, however, thatas you copy those cells down the spreadsheet per the above procedure, thateach observation at cells F8, F9, F10, and so on and G8, G9, G10, and soon, is not completely independent. The prior five-day return overlaps thevalues for F8, F9, and F10, and the prospective five-day return overlaps forcells G8, G9, and G10. This will always be the case when you’re using asmaller time period for your raw data than the period that you’re investigatingfor your independent and dependent variables.Inferential statistical tests depend on each observation in the data setbeing independent, so it is not appropriate to include overlapping data