My Porfolio 2022 going forward?

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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

Now that I got that out of the way

Synopsis

I am a quant/mechanical investor at heart. I no longer have the time to do quant work or stock picking. Sad to say, I am going the ETF route. Something safe and diverified. I like the concept of not looking at my portfolio and have it just work.

I have had a few choices:

  • Vanguard Target Retirement 2030 Fund (VTHRX)
  • Some other Targe Date fund
  • Some combos of ETFs
  • Continue to overthing or stess out buying individual stocks

What is good about the latter is that you can decide your own allocation. Because of my situation, I do not need to be in bonds at all. the rule of 120 or 100 does not apply. My current retirement fund functions as such.

In spite of my age. a 100/0 or 90/10 stock/bond ration is acceptable

For my main profolio, I am going for a 90/10 stock/bond and 60/40 US/International. I would prefer a home bias, but from what I read, going forward, the United States might not be all that.

Side note: Most US firms are really international/multinational. The benefit of US stocks is the SEC and regulators.

Now for a quick look at the plan.

The ETFs that I have chose are:

  • VTI Vanguard Total Stock Market Index Fund;ETF
  • VXUS: Vanguard Total International Stock Index Fund;ETF
  • AGG: iShares Core US Aggregate Bond ETF
  • BNDX: Vanguard Total International Bond Index Fund;ETF

VTI/VXUS at 60/40 could be reduced to just VT. However, if I choose to change alocation, it is easier.

I am not always a fan of looking at generated charts by others (I like to see how they produced their results). So here are a few that can be reproduced.

In [1]:
#Load Libraries
suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(tidyquant))


#Stocks an Weights
ETF <- c('VTI','VXUS','AGG','BNDX')
wt <- c(.54,.36,.07,.03)

#set periods
start_date <- today() - years(10)
end_date <- today()
start_date
end_date

Get the prices

In [2]:
suppressWarnings(stock_returns_monthly  <- ETF %>%
    tq_get(get  = "stock.prices",
           from = start_date,
           to   = end_date) %>%
    group_by(symbol) %>%
    tq_transmute(select     = adjusted, 
                 mutate_fun = periodReturn, 
                 period     = "monthly", 
                 col_rename = "Ra"))
#stock_returns_monthly

Get Baseline data

S&P500 is the benchmark that most try to beat

In [3]:
suppressWarnings(baseline_returns_monthly <- "VOO" %>%
  tq_get(get  = "stock.prices",
         from = start_date,
         to   = end_date) %>%
  tq_transmute(select     = adjusted, 
               mutate_fun = periodReturn, 
               period     = "monthly", 
               col_rename = "Rb"))

Change data to monthly returns

In [4]:
suppressWarnings(portfolio_returns_monthly <- stock_returns_monthly %>%
  tq_portfolio(assets_col  = symbol, 
               returns_col = Ra, 
               weights     = wt,
               rebalance_on = "years",
               col_rename  = "Ra"))

RaRb_single_portfolio <- left_join(portfolio_returns_monthly, 
                                   baseline_returns_monthly,
                                   by = "date")

Perfomance Data

Returns

In [5]:
RaRb_single_portfolio %>%
  tq_performance(Ra = Ra, Rb = NULL, performance_fun = table.AnnualizedReturns)
A tibble: 1 × 3
AnnualizedReturn AnnualizedSharpe(Rf=0%) AnnualizedStdDev
<dbl> <dbl> <dbl>
0.1185 0.9998 0.1185
In [6]:
RaRb_single_portfolio %>%
  tq_performance(Ra = Ra, Rb = Rb, performance_fun = table.CAPM)
A tibble: 1 × 12
ActivePremium Alpha AnnualizedAlpha Beta Beta- Beta+ Correlation Correlationp-value InformationRatio R-squared TrackingError TreynorRatio
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-0.0448 -0.0019 -0.0223 0.8846 0.9122 0.8332 0.969 0 -1.3611 0.9389 0.0329 0.134

This is a good question here. If there is a negative 4% to the S&P500 and is almost completely corrilated? Why not just stick with either SPY or VOO? From what I understand and read, Global is expanding in percentage of the market. It is what VANGARD is thinking.

In [7]:
portfolio_growth_monthly <- stock_returns_monthly %>%
    tq_portfolio(assets_col   = symbol, 
                 returns_col  = Ra, 
                 weights      = wt, 
                 col_rename   = "investment.growth",
                 wealth.index = TRUE) %>%
    mutate(investment.growth = investment.growth * 10000)

portfolio_growth_monthly %>%
    ggplot(aes(x = date, y = investment.growth)) +
    geom_line(size = 2, color = palette_light()[[1]]) +
    labs(title = "Portfolio Growth",
         subtitle = "54% VTI, 36% VXUS, 7% AGG, 3% BNDX",
         caption = "What $1000 becomes",
         x = "", y = "Portfolio Value") +
    geom_smooth(method = "loess") +
    theme_tq() +
    scale_color_tq() +
    scale_y_continuous(labels = scales::dollar)

ETF <- c('VTI','VXUS','AGG','BNDX')
wt <- c(.54,.36,.07,.03)

Warning message in PerformanceAnalytics::Return.portfolio(., weights = weights, :
"NA's detected: filling NA's with zeros"
Warning message:
"`type_convert()` only converts columns of type 'character'.
- `df` has no columns of type 'character'"
`geom_smooth()` using formula 'y ~ x'
No description has been provided for this image

How your money grows

In [8]:
portfolio_growth_monthly
A tibble: 121 × 2
date investment.growth
<date> <dbl>
2011-12-30 10115.19
2012-01-31 10663.84
2012-02-29 11084.03
2012-03-30 11242.82
2012-04-30 11125.93
2012-05-31 10322.65
2012-06-29 10766.46
2012-07-31 10840.44
2012-08-31 11101.59
2012-09-28 11389.60
2012-10-31 11306.04
2012-11-30 11439.11
2012-12-31 11718.38
2013-01-31 12163.76
2013-02-28 12204.69
2013-03-28 12516.14
2013-04-30 12790.51
2013-05-31 12802.93
2013-06-28 12522.74
2013-07-31 13139.56
2013-08-30 12813.32
2013-09-30 13439.86
2013-10-31 13951.27
2013-11-29 14175.02
2013-12-31 14470.48
2014-01-31 13949.92
2014-02-28 14616.35
2014-03-31 14684.61
2014-04-30 14760.02
2014-05-30 15050.72
... ...
2019-07-31 21999.10
2019-08-30 21594.43
2019-09-30 22001.25
2019-10-31 22512.98
2019-11-29 23150.16
2019-12-31 23855.85
2020-01-31 23659.91
2020-02-28 21972.23
2020-03-31 18953.83
2020-04-30 21035.61
2020-05-29 22087.58
2020-06-30 22676.30
2020-07-31 23829.87
2020-08-31 25261.79
2020-09-30 24521.19
2020-10-30 24044.76
2020-11-30 26784.32
2020-12-31 28051.81
2021-01-29 27993.20
2021-02-26 28751.10
2021-03-31 29615.31
2021-04-30 30883.90
2021-05-28 31214.09
2021-06-30 31750.76
2021-07-30 32077.91
2021-08-31 32847.87
2021-09-30 31504.75
2021-10-29 33240.36
2021-11-30 32569.42
2021-12-07 33376.09

Need to stop now. If I get a chance, I will update this page later.

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