Interpret predictions weekly and monthly stock price returns [closed]

I have built a model in R that predicts weekly and monthly returns of stock prices using regression trees, roughly based on https://www.r-bloggers.com/using-cart-for-stock-market-forecasting/. In my training and test sets weekly and monthly return rates (the target variables) are calculated for Fridays as follows:

• Weekly: (Friday close - Monday open) / Monday open
• Monthly: (Last Friday of the month close - first Monday of the month open) / first Monday of the month open

Weekly returns are available for each Friday of the week and monthly returns are available for each last Friday of the month.

Using the predictive model, I can then predict weekly and monthly returns using the data available today. E.g. if today's data is xyz then the model predicts a weekly return of 0.1 and a monthly return of -0.02.

df_test$pred_weekly_return <- predict(tree_weekly,df_test) df_test$pred_monthly_return <- predict(tree_monthly,df_test)


I am struggling with how to interpret these predicted weekly and monthly returns. If I use today's Wednesday 14 December data, is the predicted weekly return then for next week Wednesday 21 December or for this week Friday 16 December? And is the predicted monthly return then for the end of this month 31 December or, say, 31 days from now 13 January?

Any advice would be greatly appreciated.

• Could you publish the code you are using? Thank you – vonjd Mar 10 '18 at 9:09
• I'm voting to close this question as off-topic because it was crossposted to CrossValidated and has an accepted answer there. – Bob Jansen Mar 10 '18 at 10:15

$y_t$= $B_{0_t}$ + $B_{1_{t-1}}$ $x_{1_{t-1}}$+ .... $B_{n_{t-1}}$ $x_{n_{t-1}}$