Hi. I am toying with the idea of a relatively simple adaptive trading rule based on linear regression. The idea is to use linear regression to model the earnings in price 50 days ahead based on some variable - for example the earnings in the past 50 days. For every possible trade I would estimate the linear model using trading data of the given stock from the past 200 days. So for every possible trade there would be a resample and remodelling. In that way the model could handle that the linear relationship dropped in and out and was different from stock to stock. If the linear model says there will be a positive return it will signal buy otherwise sell. In essence: Let x be the earnings of the past 50 days: log(p(today) / p(today - 50 days)) Let y be the earnings 50 days ahead: log(p(today + 50 days) / p(today)) Use the past 200 days of trading in the given stock as sample data to estimate alfa and beta in the linear model: y = alfa + beta * x Buy the stock if y is positive; otherwise do not. Has anyone worked with a similar strategy? What do you think of it?