An Implementation of Quality Minus Junk
David Kane, Ryan Kwon, Anthoney Tsou
May 29, 2015
What is Quality?
Characteristics that an investor should be willing to pay extra for
How is Quality Measured?
\[ \frac{P}{B} = \frac{\text{profitability} * \text{payouts}}{\text{required return} - \text{growth}} \]
Profitability
Growth
Safety
Payouts
The Quality Minus Junk Strategy
Short low-quality (junk) stocks
Go long high-quality stocks
Results in high risk-adjusted returns
Example Quality Measurements
library(qmj)
data(quality)
keep_quality <- c("ticker", "profitability", "growth", "safety", "payouts", "quality")
head(quality[keep_quality], n = 10)
ticker profitability growth safety payouts quality
1 ANGI -0.1575365 24.55764246 -0.89076389 -1.8699982 21.639344
2 SBCF -0.9552288 21.17749195 0.23623565 -1.0820805 19.376418
3 UHAL -0.3350943 19.25596295 -0.16064717 -1.9855567 16.774665
4 GUID 0.2245725 13.61798234 0.09569998 -1.5591484 12.379106
5 BRO 0.1484205 -0.04063592 11.76587071 0.2254432 12.099098
6 CFFN 5.7540587 -0.04904148 5.73770806 0.2292110 11.671936
7 UTMD -3.3070925 16.48412446 -1.72466975 0.1363612 11.588723
8 CENX -0.1912805 3.43982232 6.17675052 -0.1956603 9.229632
9 CXW -0.3283571 3.22020489 4.10097444 0.1910389 7.183861
10 RSE 3.6209141 0.04701919 1.88766753 0.9855644 6.541165
Let's Take a Look at ANGI
Observe change in NI, GPROF, GPROF relative to TA, NI relative to TA over time
library(dplyr)
data(financials)
angi_financials <- filter(financials, ticker == "ANGI")
keep_fin <- c("year","NI", "GPROF", "TA")
angi_financials[keep_fin]
year NI GPROF TA
1 2011 -49.04 73.63 111.40
2 2012 -52.89 128.72 96.23
3 2013 -32.99 205.57 105.64
4 2014 -12.07 262.25 154.54
Filtering Companies with Excessively High Singular Components
ticker profitability growth safety payouts quality
1 ANGI -0.1575365 24.55764 -0.8907639 -1.869998 21.63934
head(filter_companies(quality, "growth")[keep_quality], n = 10)
ticker profitability growth safety payouts quality
5 BRO 0.1484205 -0.040635919 11.7658707 0.2254432 12.099098
6 CFFN 5.7540587 -0.049041480 5.7377081 0.2292110 11.671936
8 CENX -0.1912805 3.439822322 6.1767505 -0.1956603 9.229632
9 CXW -0.3283571 3.220204886 4.1009744 0.1910389 7.183861
10 RSE 3.6209141 0.047019195 1.8876675 0.9855644 6.541165
11 PDCO -0.7411908 -0.052962782 0.1659304 6.7805708 6.152348
12 MTSC -0.2509800 2.281272629 3.9553538 0.1548377 6.140484
13 CYS 1.8326580 -0.040161679 3.2979754 0.8379886 5.928460
14 HZO 2.7058184 -0.010954666 1.1415603 1.5434407 5.379865
15 PFG 0.6628262 -0.009983522 4.3283077 0.1303343 5.111485
Customizing Data
data(companies)
# sample_companies <- companies[c(71, 80, 100, 202),]
sample_companies <- data.frame(ticker = c("AFFX", "AGIO", "AIRM", "ARG"), name = rep("_", 4))
raw_info <- get_info(sample_companies)
raw_prices <- get_prices(sample_companies)
custom_financials <- tidyinfo(raw_info)
custom_prices <- tidy_prices(raw_prices)
market_data(sample_companies, custom_financials, custom_prices)[keep_quality]
ticker profitability growth safety payouts quality
1 AIRM 0.82518428 1.1491124 0.008294749 0.8367818 2.8193732
2 ARG 0.58935581 -1.1100242 1.353540616 0.2005718 1.0334440
3 AFFX -0.01039531 0.4530143 -0.334102735 0.4090937 0.5176099
4 AGIO -1.40414478 -0.4921025 -1.027732631 -1.4464472 -4.3704271
Help Pages
help(package="qmj")
?companies
?get_info
Thank you!
Installation:
#library(devtools)
#install_github("anttsou/qmj")
Contact Info:
David Kane- dave.kane@gmail.com
Ryan Kwon- rynkwn@gmail.com
Anthoney Tsou- anttsou@gmail.com