Graphics
Growth of $10k
Let’s look at the famous FANG stocks (Facebook, Amazon, Netflix, Google) for the past 3 years, with SPY (an S&P 500 ETF) included for reference.
fig <- growth_graph(c("SPY", "FB", "AMZN", "NFLX", "GOOG"), from = "2015-03-02",
to = "2018-03-02")
Notice that I specified a vector of ticker symbols, not a matrix of prices or gains. All of the graphing functions in stocks have the ability to download data on the fly.
Clearly the FANG stocks have continued with their remarkable growth over the past 3 years. I actually held Amazon and Google briefly, but sold them once I decided I couldn’t beat the market through stock-picking. I can’t, but in this case I wish I would have kept trying.
Gains vs. benchmark (alpha, beta, R-squared)
Before buying a fund, you should have a rough idea of how it tends to move with the market. The classic way of doing this is plotting the fund’s daily gains vs. a benchmark (typically the S&P) and looking at the estimated intercept (“alpha”) and slope (“beta”). For example, for Vanguard’s long-term bond fund VBLTX, over the past 5 years:
fig <- gains_graph(c("SPY", "VBLTX"), from = "2013-03-02", to = "2018-03-02")
Definitely a negative trend there, although a weak one: \(R^2 = 0.08\) means only 8% of variability in VBLTX gains are explained by the regression on SPY.
Looking at the regression equation, we see positive alpha and negative beta, suggesting VBLTX and SPY would complement each other nicely in a retirement portfolio.
Compare funds on 1 metric
To compare annualized growth for the 10 Select Sector SPDR ETFs over the past year:
data(sector_spdr_etfs)
fig <- onemetric_graph(tickers = sector_spdr_etfs$ticker,
from = "2017-03-02", to = "2018-03-02")
Notice that I loaded the 10 ticker symbols with data(sector_spdr_etfs). That worked because a dataset called sector_spdr_etfs is included in stocks. Quite a few other ticker lists are also available (run data(package = "stocks") to see them).
Looks like only 6 of the 10 sectors had positive growth over the past year; XLK (technology) was the best performer with 30.3% growth.
Compare funds on 2 metrics
To simultaneousy look at annualized growth and maximum drawdown for the sector ETFs:
fig <- twometrics_graph(tickers = sector_spdr_etfs$ticker,
from = "2017-03-02", to = "2018-03-02",
x.metric = "mdd", y.metric = "cagr")
Here XLY (consumer discretionary) looks pretty good, with the best max drawdown and second-best growth.
One metric over time
Let’s move to another example: Warren Buffett’s Berkshire Hathaway (BRK-B). Let’s see whether BRK-B has consistently outperformed SPY over the past 15 years, looking at the 100-day moving Sharpe ratio:
fig <- onemetric_overtime_graph(tickers = c("SPY", "BRK-B"),
from = "2003-03-02", to = "2018-03-02",
window.units = 100, y.metric = "sharpe")
There are certainly some periods where SPy does a lot better, and some where BRK-B does a lot better. There’s no obvious winner here, visually. The actual 100-day Sharpe ratios are returned by onemetric_overtime_graph, so we can compare the medians and see what percent of the time BRK-B’s is higher:
apply(fig, 2, median)
#> SPY BRK-B
#> 0.07349 0.05481
mean(fig[, 2] > fig[, 1])
#> [1] 0.4331BRK-B has a tendency for worse risk-adjusted returns than SPY - not good, Mr. Buffett!
2-fund portfolio optimization
Until recently, my own retirement portfolio was simply 1/3 UPRO, 2/3 VBLTX. UPRO is a 3x daily S&P 500 ETF, and VBLTX is Vanguard’s long-term bond mutual fund. A good way of visualizing the characteristics of a two-fund portfolio is to plot mean vs. standard deviation of daily gains, for allocations ranging from 100% fund 1 to 100% fund 2. To do that, using data over UPRO’s lifetime, going back to 6/25/09:
fig <- twofunds_graph(tickers = c("UPRO", "VBLTX"), reference.tickers = "SPY",
to = "2018-03-02")
The UPRO/VBLTX strategy sort of trumps SPY, because there are regions on the curve with higher returns and lower volatility than SPY.
For another example, we, could look at annualized growth vs. maximum drawdown rather than mean vs. standard deviation, and look at UPRO/VBLTX as well as UPRO/VBMFX, to see how swapping the long-term fund VBLTX with the total bond fund VBMFX would affect our portfolio.
fig <- twofunds_graph(tickers = c("UPRO", "VBLTX", "UPRO", "VBMFX"),
reference.tickers = "SPY",
to = "2018-03-02",
x.metric = "mdd", y.metric = "cagr")
UPRO/VBLTX clearly has much better performance. One more example, this time Sharpe ratio vs. allocation, for the same two funds plus UPRO/TLT:
fig <- twofunds_graph(tickers = c("UPRO", "VBLTX", "UPRO", "VBMFX", "UPRO", "TLT"),
reference.tickers = "SPY",
to = "2018-03-02",
x.metric = "mean", y.metric = "sharpe",
plot.list = list(ylim = c(0, 0.12)))
The rationale for this graph is that we might want to target the same level of expected returns as SPY, but with as high of a Sharpe ratio as possible. If you go straight up on the graph from the SPY data point, you hit the red curve first, then the orange, and finally the blue. So we would choose UPRO/VBLTX, with something like 75-80% allocated to VBLTX.
3-fund portfolio optimization
The change I made recently was adding Vanguard’s high-yield (aka “junk bond”) mutual fund, VWEHX, to the mix. To visualize characteristics of VWEHX/UPRO/VBLTX:
fig <- threefunds_graph(tickers = c("VWEHX", "UPRO", "VBLTX"),
reference.tickers = "SPY",
to = "2018-03-02")
Each curve is for a fixed allocation to the first fund. The black curve is 0% VWEHX, so it is the same as the UPRO/VBLTX curve in the previous graph. The red curve is 20% VWEHX, and the remaining 80% allocated entirely to VBLTX (bottom-most point), entirely to UPRO (top-right data point), and everywhere in between.
At SPY’s SD, the black curve (0% VWEHX) achieves the highest mean. At SPY’s mean, the blue (40% VWEHX) or red (20% VWEHX) curve has the lowest SD. So depending on what you’re going for, it may or may not be worthwhile adding VWEHX. My rationale was sort of qualitative: two nearly independent alpha-generating bond funds seems better than one.