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Step 1: Estimating AR(1) Model for daily, weekly, monthly, quarterly and annual returns using the following lines of codes. Here is an example of daily returns. It will give you summary results of your estimate. You need to rerun these codes changing returns data to get different estimates. ret=coredata(returns.daily) \#You need to change returns.daily to returns of other frequency Iret =lag(ret,n=1) combineddata = data.frame(na.omit(cbind(ret,Iret))) colnames(combineddata) =c( ("Ret","LRet") ar1.model =lm( Ret LRet,data = combineddata ) summary(ar1.model) Step 2: Summarize your results in the following table. 111111 Step 3: Interpret your results where there is mean aversion and reversion in stock returns. Step 1: Estimating AR(1) Model for daily, weekly, monthly, quarterly and annual returns using the following lines of codes. Here is an example of daily returns. It will give you summary results of your estimate. You need to rerun these codes changing returns data to get different estimates. ret=coredata(returns.daily) \#You need to change returns.daily to returns of other frequency Iret =lag(ret,n=1) combineddata = data.frame(na.omit(cbind(ret,Iret))) colnames(combineddata) =c( ("Ret","LRet") ar1.model =lm( Ret LRet,data = combineddata ) summary(ar1.model) Step 2: Summarize your results in the following table. 111111 Step 3: Interpret your results where there is mean aversion and reversion in stock returns