Repository cleanup

.gitignore

@@ -1,36 +1,5 @@
-# History files
-.Rhistory
-.Rapp.history
-
-# Session Data files
+# R files
 .RData
 
-# Example code in package build process
-*-Ex.R
-
-# Output files from R CMD build
-/*.tar.gz
-
-# Output files from R CMD check
-/*.Rcheck/
-
-# RStudio files
-.Rproj.user/
-
-# produced vignettes
-vignettes/*.html
-vignettes/*.pdf
-
-# knitr and R markdown default cache directories
-/*_cache/
-/cache/
-
-# Temporary files created by R markdown
-*.utf8.md
-*.knit.md
-
-# Shiny token, see https://shiny.rstudio.com/articles/shinyapps.html
-rsconnect/
-
 WRDS_pwd.txt
 crsp.msf.csv

README.md

@@ -1,3 +1,26 @@
 # Short-term Momentum trading strategy
 
 Short-term momentum trading strategy implemented for the lecture "Systematic risk premia strategies traded at hedge funds" at University of Zurich, FS 2018.
+
+---
+
+## Overview
+
+* Implementation in R
+* In accordance to paper:
+
+    + Monthly pricing data from the Center for Research in Security Prices (CRSP)
+
+    + Sample starts in July, 1963 and ends in December, 2016
+    + All common shares traded on NYSE, AMEX, and Nasdaq 
+Measure short-term momentum using the return over the previous month: 𝑀𝑂𝑀_(𝑖,𝑡) = 𝑟_(𝑖,𝑡−1)
+
+    + Measure short-term turnover using previous month volume and number of shares outstanding data: 𝑇𝑂_(𝑖,𝑡) = 𝑉𝑂𝐿_(𝑖,𝑡−1) / 𝑆𝐻𝑅𝑂𝑈𝑇_(𝑖,𝑡−1)
+
+    + Portfolios are value-weighted by market capitalization and rebalanced at the end of each month
+
+## Results
+
+![Main findings (excess returns)](./results/main_findings_table.png)
+
+![Cumulative performance of strategies](./results/strategies_performance.png)

backtest/backtest.R

@@ -129,7 +129,6 @@ for (month in 1:n.months) {
   turnover <- turnover[idx.intersect]
   marketCap <- marketCap[idx.intersect]
 
-  ####################
   # double sorts
 
   # value-weighted returns in each decile
@@ -138,7 +137,7 @@ for (month in 1:n.months) {
     return(weightedMean(rets, caps))
   }, n.rows, n.columns)
 
-  # value-weighted returns in each decile
+  # value-weighted excess returns in each decile
   ts.deciles.rets.ex[ , , month] <- doublesort.conditional.colbreaks(rets.ex, turnover, momentum, breakpoints.mom, function(rets) {
     caps <- mwnd.marketCap[month, which(stock.names %in% names(rets))]
     return(weightedMean(rets, caps))
@@ -227,9 +226,9 @@ plot.data <- melt(plot.data.wide, id.vars=c('date'))
 
 ggplot(data=plot.data, aes(x=date, group=variable)) +
   geom_line(aes(y=value, colour=variable)) +
-  labs(y="cumulative excess returns", x="") +
+  labs(y="Dollar value of investment", x="") +
   scale_x_date(date_breaks='48 months', labels=date_format("%m.%Y")) +
-  scale_y_continuous(labels=function(x) paste0(x*100, '%'), breaks=seq(-100, 100, by=2)) +
+  scale_y_continuous(labels=function(x) x*100, breaks=seq(-100, 100, by=2)) +
   scale_colour_manual('', values=c('low.turnover'='red', 'high.turnover'='black', 'combo'='green', 'market'='blue'),
                       labels=c('Short-term reversal', 'Short-term momentum', '50:50 combo', 'FF market')) +
   theme_hc() +
@@ -245,9 +244,9 @@ plot.data <- melt(plot.data.wide, id.vars=c('date'))
 
 ggplot(data=plot.data, aes(x=date, group=variable)) +
   geom_line(aes(y=value, colour=variable)) +
-  labs(y="cumulative excess returns", x="") +
+  labs(y="Dollar value of investment", x="") +
   scale_x_date(date_breaks='48 months', labels=date_format("%m.%Y")) +
-  scale_y_continuous(labels=function(x) paste0(x*100, '%'), breaks=seq(-100, 100, by=2)) +
+  scale_y_continuous(labels=function(x) x*100, breaks=seq(-100, 100, by=2)) +
   scale_colour_manual('', values=c('low.turnover'='red', 'high.turnover'='black', 'combo'='green', 'market'='blue'),
                       labels=c('Short-term reversal', 'Short-term momentum', '50:50 combo', 'FF market')) +
   theme_hc() +