clean factorsSPGMI and stocksCRSP data sets

sandbox/Vestcor Data Cleaning/CleanFAData.R

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+# to do: clean data in stocksCRSP and factorsSPGMI
+### 1. clean sector names
+### 2. delete 7 securities in financials, real estate, and utilities sectors
+### 3. fix cap group assignments and create CapGroup and CapGroupL variables
+### 4. rename "TickerLast" as "TickerL"
+
+load("C:/FA/FactorAnalytics/data/factorsSPGMI.rda")
+load("C:/FA/FactorAnalytics/data/stocksCRSP.rda")
+
+factorsSPGMI_tmp <- factorsSPGMI
+stocksCRSP_tmp <- stocksCRSP
+
+### 1. clean up list of sectors for factorsSPGMI & stocksCRSP
+
+# confirm factorsSPGMI and stocksCRSP have incorrect sectors (misspelled)
+unique(factorsSPGMI_tmp$Sector) # contains 16 sectors with dupes
+unique(stocksCRSP_tmp$Sector) # containts 16 sectors with dupes
+
+# replacement data
+bad_sectors <- unique(factorsSPGMI_tmp$Sector)
+good_sectors <- c("InfoTech","Industrials","Healthcare","ConsumStap","Energy",
+                  "Materials","ConsumDisc","ComServices","Utilities",
+                  "RealEstate","Healthcare","Financials","ConsumDisc",
+                  "InfoTech","ConsumStap","ComServices")
+sector_table <- data.frame(cbind(bad_sectors,good_sectors))
+colnames(sector_table) <- c("BadSectors","GoodSectors")
+
+# replacements
+factorsSPGMI_tmp$Sector <- sector_table$GoodSectors[match(factorsSPGMI_tmp$Sector,sector_table$BadSectors)]
+stocksCRSP_tmp$Sector <- sector_table$GoodSectors[match(stocksCRSP_tmp$Sector,sector_table$BadSectors)]
+
+# confirm factorsSPGMI and stocksCRSP have correct sectors
+unique(factorsSPGMI_tmp$Sector) # contains 11 sectors with no dupes
+unique(stocksCRSP_tmp$Sector) # containts 11 sectors with no dupes
+
+### 2. delete 7 securities to leave temp sample of 293 stocks in 8 sectors (no
+### financials, utilities, or real estate)
+
+# get tickers to remove
+to_remove <- unique(factorsSPGMI_tmp[factorsSPGMI_tmp$Sector %in% c("Financials","RealEstate","Utilities"),]$TickerLast)
+to_remove_crsp <- unique(stocksCRSP_tmp[stocksCRSP_tmp$Sector %in% c("Financials","RealEstate","Utilities"),]$TickerLast)
+(to_remove == to_remove_crsp) # confirm same stocks to be removed from both data sets
+
+# check size of factorsSPGMI and stocksCRSP
+dim(factorsSPGMI_tmp)[1] # 276 * 300 = 82800 rows
+dim(stocksCRSP_tmp)[1] # 276 * 300 = 82800 rows
+
+# delete members of to_remove from factorsSPGMI and stocksCRSP
+stocksCRSP_tmp <- stocksCRSP_tmp[!(stocksCRSP_tmp$TickerLast %in% to_remove),]
+factorsSPGMI_tmp <- factorsSPGMI_tmp[!(factorsSPGMI_tmp$TickerLast %in% to_remove),]
+
+# check size of factorsSPGMI and stocksCRSP & same membership
+dim(factorsSPGMI_tmp)[1] # 276 * 293 = 80868 rows
+dim(stocksCRSP_tmp)[1] # 276 * 300 = 80868 rows
+unique(sort(stocksCRSP_tmp$TickerLast)) == unique(sort(factorsSPGMI_tmp$TickerLast))
+
+### 3. fix cap group assignments and create CapGroup and CapGroupL variables
+# cap group membership deemed incorrect, to be replaced with new assignments.
+# Suggest a point-in-time replacement based on CRSP 70/85/98 percentile splits
+# into LargeCap (up to 75% mkt coverage), MidCap (75% to 85%), SmallCap (85% to 
+# 98%), and MicroCap (above 98%), replace current CapGroup with reassigned data 
+# at each point in time (e.g. membership changes); create new variable CapGroupL
+
+# import market percentiles (data from from CompuStat via Vestcor)
+MktCapPercentiles <- read.csv("MktCapPercentiles.csv")
+factorsSPGMI_tmp$MktCap <- exp(factorsSPGMI_tmp$LogMktCap)
+
+factorsSPGMI_tmp$CapGroupLC <- MktCapPercentiles$LC[match(as.Date(factorsSPGMI_tmp$Date),as.Date(MktCapPercentiles$Date))]
+factorsSPGMI_tmp$CapGroupMC <- MktCapPercentiles$MC[match(as.Date(factorsSPGMI_tmp$Date),as.Date(MktCapPercentiles$Date))]
+factorsSPGMI_tmp$CapGroupSC <- MktCapPercentiles$SC[match(as.Date(factorsSPGMI_tmp$Date),as.Date(MktCapPercentiles$Date))]
+
+factorsSPGMI_tmp$CapGroup <- ifelse(factorsSPGMI_tmp$MktCap < factorsSPGMI_tmp$CapGroupSC,"MicroCap",
+                                    ifelse(factorsSPGMI_tmp$MktCap < factorsSPGMI_tmp$CapGroupMC,"SmallCap",
+                                           ifelse(factorsSPGMI_tmp$MktCap < factorsSPGMI_tmp$CapGroupLC,"MidCap",
+                                                  "LargeCap")))
+
+# create CapGroupL variable
+end_dat <- factorsSPGMI_tmp[factorsSPGMI_tmp$Date == "2015-12-31",]
+factorsSPGMI_tmp$CapGroupL <- end_dat$CapGroup[match(factorsSPGMI_tmp$TickerLast,end_dat$TickerLast)]
+
+
+# match CapGroup and CapGroupL in stocksCRSP
+factorsSPGMI_tmp$MatchVar <- paste(factorsSPGMI_tmp$Date,factorsSPGMI_tmp$Ticker,sep="-")
+stocksCRSP_tmp$MatchVar <- paste(stocksCRSP_tmp$Date,stocksCRSP_tmp$Ticker,sep="-")
+stocksCRSP_tmp$CapGroup <- factorsSPGMI_tmp$CapGroup[match(stocksCRSP_tmp$MatchVar,factorsSPGMI_tmp$MatchVar)]
+stocksCRSP_tmp$CapGroupL <- factorsSPGMI_tmp$CapGroupL[match(stocksCRSP_tmp$MatchVar,factorsSPGMI_tmp$MatchVar)]
+
+# reorder columns
+factorsSPGMI_tmp <- factorsSPGMI_tmp[,c("Date","Ticker","TickerLast","Company",
+                                        "CapGroup","CapGroupL","GICS",
+                                        "Sector","AnnVol12M","Beta60M","BP","EP",
+                                        "LogMktCap","PM12M1M","AccrualRatioCF",
+                                        "AstAdjChg1YOCF","CFROIC","Chg1YAstTo",
+                                        "EBITDAEV","FCFP","PM1M","SEV")]
+
+stocksCRSP_tmp <- stocksCRSP_tmp[,c("Date","Ticker","TickerLast","Company",
+                                    "CapGroup","CapGroupL","GICS","Sector","Return",
+                                    "RetExDiv","Price","PrcSplitAdj","Ret4WkBill",
+                                    "Ret13WkBill","Ret1YrBill","mktIndexCRSP")]
+
+### 4. rename TickerLast as TickerL
+names(factorsSPGMI_tmp)[names(factorsSPGMI_tmp) == "TickerLast"] <- "TickerL"
+names(stocksCRSP_tmp)[names(stocksCRSP_tmp) == "TickerLast"] <- "TickerL"
+
+
+### 5. save data
+#setwd("C:/FA/FactorAnalytics/data")
+factorsSPGMI <- factorsSPGMI_tmp
+stocksCRSP <- stocksCRSP_tmp
+save(factorsSPGMI,file="factorsSPGMI.rda")
+save(stocksCRSP,file="stocksCRSP.rda")