[R] Generating abnormal returns in R

[R. Michael Weylandt]

Well, it's not hard to write the code for it, but if you know the
secret way to accurately model "abnormal returns," you'll be a far
richer man than I quite soon.

Less snidely, one needs to say quite a bit more about a distribution
to specify it than "not Gaussian."

Michael

On Sat, Mar 10, 2012 at 12:46 PM, drsenne <dr_senne at pandora.be> wrote:
> Hello
>
> This is my first post on this forum and I hope someone can help me out.
> I have a datafile (weeklyR) with returns of +- 100 companies.
> I acquired this computing the following code:
>
> library("tseries");
> tickers  = c("GSPC" , "BP" , "TOT" ,    "ENI.MI" , "VOW.BE" ,   "CS.PA" ,
> "DAI.DE" ,      "ALV.DE" ,      "EOAN.DE" ,     "CA.PA" ,       "G.MI" , "DE" , "EXR.MI" ,
> "MUV2.BE" , "UG.PA" , "PRU.L", "VOD.L" , "DPB.BE" , "REP.MC" , "RWE.BE" ,
> "AGN.AS" , "FTE.PA" , "EAD" , "LGEN.L" , "CNP.PA" , "ULVR.L" , "TKA.BE" ,
> "RIO.L" , "NOK" , "SGO.PA" , "RNO.PA" , "VIE.PA" , "BAYN.DE" , "SAN.PA"  ,
> "DG.PA" , "SSE.L" , "GSK.L" , "EN.PA" , "LYB" , "MLSNP.PA" , "IBE.MC" ,
> "EURS.PA" , "AH.AS" , "VIV.PA" , "TIT.MI" , "VOLV-B.ST" , "ABI.BR" ,
> "LHA.DE" , "OML.L" , "CNA.L" , "CON.DE" , "PHG" , "AZN.L" , "SBRY.L" ,
> "BA.L" , "BT-A.L" , "AF.PA" , "430021.VI" , "SL.L" , "ERIC-A.ST" , "CDI.PA"
> , "AAL.L" , "ALO.PA" , "DELB.BR" , "HOT.BE" , "GAS.MC" , "SU.PA" , "OR.PA" ,
> "FNC.MI" , "MRW.L" , "MAP.MC" , "ML.PA" , "IMT.L" , "EBK.DE" , "PP.PA" ,
> "ACN" , "BTI" , "CRG.IR" , "CPG.L" , "BN.PA" , "NG.L" , "T7L.BE" , "HEIA.AS"
> , "ACS.MC" , "LG.PA" , "STAN.L" , "ALU.PA" , "FRE.MU" , "SW.PA" , "WOS.L" ,
> "AKZA.AS" , "HEN.MU")
> for( series in tickers ){
> print(series)
> close <-
> get.hist.quote(instrument=series,retclass="zoo",quote="AdjClose",compression="d",
> start="2000-1-1",  end="2011-12-31",quiet=TRUE)
> if(series==tickers[1]){ pricedata = close }else{ pricedata = merge(
> pricedata , close ) }
> }
> colnames(pricedata) = tickers
> # Avoid a missing because of trade halt for that stock
> pricedata = na.approx(pricedata)
> weeklyR = diff(log(pricedata))
> time(weeklyR) = as.Date(time(weeklyR))
> print(weeklyR)
> save(weeklyR , file = "weeklyR.Rdata")
> write.zoo(weeklyR,file="weeklyR.csv",quote=T,sep=",", na = "NA", dec = "." ,
> row.names = F,col.names = T)
>
> Now I need to make a market model in R so i can generate abnormal returns
> from these stocks. As market index I would like to use the GSPC. I also need
> to consider abnormal returns calculated over a sixty-trading-day window.
> Can this be done in R? Is it difficult to write this code?
>
> Any help would be much appreciated!
>
> thanks
>
> drsenne
>
>
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