2020 Dow Strategies

(updated )

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2020 has been an interesting year. Over the years, I have become a fan of stock picking in the Peter Lynch view: Good companies with good management that you understand. However, it requires time and awareness. I am also getting to the age where I need to be more conservative with my money and savings.

I have been gravitating towards Dog of Dow in my Roths in my fight not to use ETFs. Check once a year, rebalance, forget till next year. Mostly safe.

In this post, I am going to compare, in a reproducible way. VFINX will be the proxy for the market benchmark.

What I am going to look at is:

  • Dogs of Dow
    • After the stock market closes on the last day of the year, of the 30 stocks that make up the Dow Jones Industrial Average, select the ten stocks which have the highest dividend yield
  • Small Dogs of Dow
    • Five lowest priced Dogs of Dow
  • Mad Dogs of the Dow
    • After the stock market closes on the last day of the year, of the 30 stocks that make up the Dow Jones Industrial Average, select the ten stocks which have the highest shareholders yield
  • Dobermans Of The Dow
    • Rank Dow constituents by Return on Equity (ROE), keeping the top 20.
    • Rank remaining names by Free Cash Flow Yield, keeping the top 10.

All offer a subset of the Dow Industrial. What is different is how you subset then sort.

Yes, I know they all offer returns. I just like to verify and show how I came to my results.

Dow stocks for Jan 1, 2020 were:

In [1]:
DOW = c(
  "AAPL",
  "AXP",
  "BA",
  "CAT",
  "CSCO",
  "CVX",
  "DIS",
  "DOW",
  "GS",
  "HD",
  "IBM",
  "INTC",
  "JNJ",
  "JPM",
  "KO",
  "MCD",
  "MMM",
  "MRK",
  "MSFT",
  "NKE",
  "PFE",
  "PG",
  "RTX",
  "TRV",
  "UNH",
  "V",
  "VZ",
  "WBA",
  "WMT",
  "XOM"
)

Now to set up the population for each strategy and portfolio. Each make it easier to compair a weight file will be created. On ten stock strategies, selected stocks will have a weight of 1/10 or 0.1

Dogs of Dow

Portfolio 1

In [2]:
DOD_stock <- c("CSCO",
               "CVX",
               "DOW",
               "IBM",
               "KO",
               "MMM",
               "PFE",
               "VZ",
               "WBA",
               "XOM")
DOD_WT <- c(0,0,0,0,0.1,0.1,0,0.1,0,0,0.1,0,0,0,0.1,0,0.1,0,0,0,0.1,0,0,0,0,0,0.1,0.1,0,0.1)

Small Dogs of Dow

Portfolio 2

In [3]:
Small_Dog_Stocks <- c("CSCO",
                      "DOW",
                      "KO",
                      "PFE",
                      "WBA")
SM_DOD_WT <- c(0,0,0,0,0.2,0,0,0.2,0,0,0,0,0,0,0.2,0,0,0,0,0,0.2,0,0,0,0,0,0,0.2,0,0)

Mad Dogs of Dow

Portfolio 3

In [4]:
Mad_Dogs_Stocks  <- c("WBA",
                      "CSCO",
                      "PFE",
                      "CAT",
                      "JPM",
                      "IBM",
                      "AAPL",
                      "MRK",
                      "HD",
                      "GS")


MAD_DOD_WT <- c(0.1,0,0,0.1,0.1,0,0,0,0.1,0.1,0.1,0,0,0.1,0,0,0,0.1,0,0,0.1,0,0,0,0,0,0,0.1,0,0)

Dobermans of Dow

Portfolio 4

In [5]:
Dobermans_Of_Dow <-
  c("IBM",
    "WBA",
    "AXP",
    "CSCO",
    "VZ",
    "INTC",
    "MMM",
    "PFE",
    "JNJ",
    "CAT")
    
Dobermans_WT <- c(0,0.1,0.1,0,0.1,0,0,0,0,0,0.1,0.1,0.1,0,0,0,0.1,0,0,0,0.1,0,0,0,0,0,0.1,0.1,0,0)

Initializations and get data

In [6]:
library(tidyverse, warn.conflicts = T, quietly = T)
library(tidyquant, warn.conflicts = T, quietly = T)
from <- "2020-01-01" #start date
to <- "2020-11-01" #To data is date - 1

Warning message:
"package 'tidyverse' was built under R version 3.6.3"
-- Attaching packages --------------------------------------- tidyverse 1.3.0 --

v ggplot2 3.3.2     v purrr   0.3.4
v tibble  3.0.3     v dplyr   1.0.2
v tidyr   1.1.2     v stringr 1.4.0
v readr   1.3.1     v forcats 0.5.0

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Attaching package: 'lubridate'


The following objects are masked from 'package:base':

    date, intersect, setdiff, union


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Warning message:
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  as.zoo.data.frame zoo 

Version 0.4-0 included new data defaults. See ?getSymbols.

== Need to Learn tidyquant? ====================================================
Business Science offers a 1-hour course - Learning Lab #9: Performance Analysis & Portfolio Optimization with tidyquant!
</> Learn more at: https://university.business-science.io/p/learning-labs-pro </>

Get Data

In [7]:
DOW_monthly_returns <- DOW %>%
  tq_get(get  = "stock.prices",
         from = from,
         to   = to) %>%
  group_by(symbol) %>%
  tq_transmute(select     = adjusted, 
               mutate_fun = periodReturn, 
               period     = "monthly", 
               col_rename = "Ra")

Get Baseline data

In [8]:
baseline_returns_monthly <- "VFINX" %>%
    tq_get(get  = "stock.prices",
           from = from,
           to   = to) %>%
    tq_transmute(select     = adjusted, 
                 mutate_fun = periodReturn, 
                 period     = "monthly", 
                 col_rename = "Rb")

Aggregate Portfolio Returns

In [9]:
DOW_returns_monthly_multi <- DOW_monthly_returns %>%
    tq_repeat_df(n = 4)


weights <- c(DOD_WT,SM_DOD_WT,MAD_DOD_WT,Dobermans_WT)
stocks <- DOW
weights_table <-  tibble(stocks) %>%
    tq_repeat_df(n = 4) %>%
    bind_cols(tibble(weights)) %>%
    group_by(portfolio)



portfolio_returns_monthly_multi <- DOW_returns_monthly_multi %>%
    tq_portfolio(assets_col  = symbol, 
                 returns_col = Ra, 
                 weights     = weights_table, 
                 col_rename  = "Ra")

Ungrouping data frame groups: symbol

Merging and Assessing Performance

In [10]:
RaRb_DOW_multiple_portfolio <- left_join(portfolio_returns_monthly_multi, 
                                     baseline_returns_monthly,
                                     by = "date")

Performance and Stats

In [11]:
RaRb_DOW_multiple_portfolio %>%
    tq_performance(Ra = Ra, Rb = NULL, performance_fun = table.Stats)
A grouped_df: 4 × 17
portfolio ArithmeticMean GeometricMean Kurtosis LCLMean(0.95) Maximum Median Minimum NAs Observations Quartile1 Quartile3 SEMean Skewness Stdev UCLMean(0.95) Variance
<int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 -0.0211 -0.0233 -0.0813 -0.0708 0.1211 -0.0328 -0.1180 0 10 -0.0572 0.0166 0.0220 0.5504 0.0695 0.0286 0.0048
2 -0.0184 -0.0202 -0.9786 -0.0642 0.0917 -0.0285 -0.1034 0 10 -0.0574 0.0320 0.0203 0.3056 0.0641 0.0275 0.0041
3 -0.0052 -0.0073 -1.2158 -0.0533 0.0985 -0.0189 -0.1004 0 10 -0.0407 0.0508 0.0213 0.0348 0.0672 0.0428 0.0045
4 -0.0216 -0.0233 -0.6139 -0.0646 0.0887 -0.0237 -0.1061 0 10 -0.0583 0.0150 0.0190 0.2040 0.0602 0.0214 0.0036

CAPM

In [12]:
RaRb_DOW_multiple_portfolio %>%
    tq_performance(Ra = Ra, Rb = Rb, performance_fun = table.CAPM)
A grouped_df: 4 × 13
portfolio ActivePremium Alpha AnnualizedAlpha Beta Beta- Beta+ Correlation Correlationp-value InformationRatio R-squared TrackingError TreynorRatio
<int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 -0.2673 -0.0249 -0.2609 0.8645 0.6761 1.3064 0.9461 0e+00 -3.1168 0.8951 0.0858 -0.2849
2 -0.2385 -0.0216 -0.2309 0.7518 0.5375 0.9596 0.8929 5e-04 -1.9971 0.7973 0.1194 -0.2893
3 -0.1051 -0.0090 -0.1025 0.8558 0.6286 0.8240 0.9687 0e+00 -1.5198 0.9384 0.0692 -0.0983
4 -0.2670 -0.0248 -0.2604 0.7406 0.6894 1.0121 0.9363 1e-04 -2.6658 0.8767 0.1002 -0.3322

AnnualizedReturns

In [13]:
RaRb_DOW_multiple_portfolio %>%
    tq_performance(Ra = Ra, Rb = NULL, performance_fun = table.AnnualizedReturns)
A grouped_df: 4 × 4
portfolio AnnualizedReturn AnnualizedSharpe(Rf=0%) AnnualizedStdDev
<int> <dbl> <dbl> <dbl>
1 -0.2463 -1.0229 0.2408
2 -0.2175 -0.9802 0.2219
3 -0.0841 -0.3612 0.2328
4 -0.2460 -1.1802 0.2085

Benchmark Returns

In [14]:
baseline_returns_monthly %>%
    tq_performance(Ra = Rb, Rb = NULL, performance_fun = table.AnnualizedReturns)
A tibble: 1 × 3
AnnualizedReturn AnnualizedSharpe(Rf=0%) AnnualizedStdDev
<dbl> <dbl> <dbl>
0.021 0.0797 0.2635

Graphs

In [15]:
portfolio_growth_monthly_multi <- DOW_returns_monthly_multi %>%
    tq_portfolio(assets_col  = symbol, 
                 returns_col = Ra, 
                 weights     = weights_table, 
                 col_rename   = "investment.growth",
                 wealth.index = TRUE) %>%
    mutate(investment.growth = investment.growth * 10000)

portfolio_growth_monthly_multi %>%
    ggplot(aes(x = date, y = investment.growth, color = factor(portfolio))) +
    geom_line(size = 2) +
    labs(title = "Portfolio Growth",
         subtitle = "Dog Strategies Portfolios",
         caption = "Mad Dogs of Dow (Portfolio 3) is doing the best",
         x = "", y = "Portfolio Value",
         color = "Portfolio") +
    #geom_smooth(method = "loess",formula = "y ~ x") +
    theme_tq() +
    scale_color_tq() +
    scale_y_continuous(labels = scales::dollar)
No description has been provided for this image

Disclosure

This material is not intended to be relied upon as a forecast, research 
or investment advice, and is not a recommendation. Past performance is 
not always indicative of future returns. I may or may not own stocks listed

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