As an example of how you can leverage tracking errors, suppose the person buying the stock is expecting it to rise 25% over the next month. If you are long 100 shares of this stock and have gained 10% so far in the month, then your current gains are 25% lower than expected or 8%. You could sell at 10% above current price for a profit of 16%. Without making judgments about whether that was wise or not, there is no need to be able to account for these types of errors. If you do hold out judgment on possible past future events (even without factoring them into your analysis), then any errors will become magnified over time. It’s important not to make assumptions based on insufficient information; you should always assume worst case scenario here since no one knows what would happen if something were subcritical.
Many people spend hours trying to determine what they should buy based on historic performance (assuming all else is equal). The spreadsheet created by someone like me shows only the best case scenario actual results because I already know that exactly what happened last year will probably happen again this year giving me an easy calculation for my forecasted returns assuming all other factors stay constant. The spreadsheet does not show historical results nor does it attempt to match up hypothetical price observations with historical record keeping effectively underestimating downside potential due to missing huge declines as well as overestimating upside potential due to missing smaller ones.
It’s human nature to want more