In this example, we’ll use a simple linear model to identify the
trend in annual returns to determine if the stock returns are decreasing
or increasing over time.
Analyze a Single Stock
First, let’s collect stock data with tq_get()
AAPL <- tq_get("AAPL", from = "2007-01-01", to = "2016-12-31")
AAPL
## # A tibble: 2,518 × 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AAPL 2007-01-03 3.08 3.09 2.92 2.99 1238319600 2.56
## 2 AAPL 2007-01-04 3.00 3.07 2.99 3.06 847260400 2.61
## 3 AAPL 2007-01-05 3.06 3.08 3.01 3.04 834741600 2.59
## 4 AAPL 2007-01-08 3.07 3.09 3.05 3.05 797106800 2.61
## 5 AAPL 2007-01-09 3.09 3.32 3.04 3.31 3349298400 2.82
## 6 AAPL 2007-01-10 3.38 3.49 3.34 3.46 2952880000 2.96
## 7 AAPL 2007-01-11 3.43 3.46 3.40 3.42 1440252800 2.92
## 8 AAPL 2007-01-12 3.38 3.40 3.33 3.38 1312690400 2.89
## 9 AAPL 2007-01-16 3.42 3.47 3.41 3.47 1244076400 2.96
## 10 AAPL 2007-01-17 3.48 3.49 3.39 3.39 1646260000 2.90
## # … with 2,508 more rows
Next, come up with a function to help us collect annual log returns.
The function below mutates the stock prices to period returns using
tq_transmute(). We add the type = "log" and
period = "monthly" arguments to ensure we retrieve a tibble
of monthly log returns. Last, we take the mean of the monthly returns to
get MMLR.
get_annual_returns <- function(stock.returns) {
stock.returns %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
type = "log",
period = "yearly")
}
Let’s test get_annual_returns out. We now have the
annual log returns over the past ten years.
AAPL_annual_log_returns <- get_annual_returns(AAPL)
AAPL_annual_log_returns
## # A tibble: 10 × 2
## date yearly.returns
## <date> <dbl>
## 1 2007-12-31 0.860
## 2 2008-12-31 -0.842
## 3 2009-12-31 0.904
## 4 2010-12-31 0.426
## 5 2011-12-30 0.228
## 6 2012-12-31 0.282
## 7 2013-12-31 0.0776
## 8 2014-12-31 0.341
## 9 2015-12-31 -0.0306
## 10 2016-12-30 0.118
Let’s visualize to identify trends. We can see from the linear trend
line that AAPL’s stock returns are declining.
AAPL_annual_log_returns %>%
ggplot(aes(x = year(date), y = yearly.returns)) +
geom_hline(yintercept = 0, color = palette_light()[[1]]) +
geom_point(size = 2, color = palette_light()[[3]]) +
geom_line(size = 1, color = palette_light()[[3]]) +
geom_smooth(method = "lm", se = FALSE) +
labs(title = "AAPL: Visualizing Trends in Annual Returns",
x = "", y = "Annual Returns", color = "") +
theme_tq()
Now, we can get the linear model using the lm()
function. However, there is one problem: the output is not “tidy”.
mod <- lm(yearly.returns ~ year(date), data = AAPL_annual_log_returns)
mod
##
## Call:
## lm(formula = yearly.returns ~ year(date), data = AAPL_annual_log_returns)
##
## Coefficients:
## (Intercept) year(date)
## 58.86279 -0.02915
We can utilize the broom package to get “tidy” data from
the model. There’s three primary functions:
augment: adds columns to the original data such as
predictions, residuals and cluster assignmentsglance: provides a one-row summary of model-level
statisticstidy: summarizes a model’s statistical findings such as
coefficients of a regression
We’ll use tidy to retrieve the model coefficients.
## # A tibble: 2 × 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 58.9 113. 0.520 0.617
## 2 year(date) -0.0291 0.0562 -0.518 0.618
Adding to our workflow, we have the following:
get_model <- function(stock_data) {
annual_returns <- get_annual_returns(stock_data)
mod <- lm(yearly.returns ~ year(date), data = annual_returns)
tidy(mod)
}
Testing it out on a single stock. We can see that the “term” that
contains the direction of the trend (the slope) is “year(date)”. The
interpetation is that as year increases one unit, the annual returns
decrease by 3%.
## # A tibble: 2 × 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 58.9 113. 0.520 0.617
## 2 year(date) -0.0291 0.0562 -0.518 0.618
Now that we have identified the trend direction, it looks like we are
ready to scale.
Scale to Many Stocks
Once the analysis for one stock is done scale to many stocks is
simple. For brevity, we’ll randomly sample ten stocks from the
S&P500 with a call to dplyr::sample_n().
set.seed(10)
stocks_tbl <- tq_index("SP500") %>%
sample_n(5)
stocks_tbl
## # A tibble: 5 × 8
## symbol company ident…¹ sedol weight sector share…² local…³
## <chr> <chr> <chr> <chr> <dbl> <chr> <dbl> <chr>
## 1 WYNN Wynn Resorts Limited 983134… 2963… 1.89e-4 Consu… 1128728 USD
## 2 SRE Sempra Energy 816851… 2138… 1.63e-3 Utili… 3367953 USD
## 3 MAA Mid-America Apartment Com… 59522J… 2589… 5.80e-4 Real … 1226777 USD
## 4 NTAP NetApp Inc. 64110D… 2630… 4.70e-4 Infor… 2387463 USD
## 5 FFIV F5 Inc. 315616… 2427… 2.80e-4 Infor… 639860 USD
## # … with abbreviated variable names ¹identifier, ²shares_held, ³local_currency
We can now apply our analysis function to the stocks using
dplyr::mutate and purrr::map. The
mutate() function adds a column to our tibble, and the
map() function maps our custom get_model
function to our tibble of stocks using the symbol column.
The tidyr::unnest function unrolls the nested data frame so
all of the model statistics are accessable in the top data frame level.
The filter, arrange and select
steps just manipulate the data frame to isolate and arrange the data for
our viewing.
stocks_model_stats <- stocks_tbl %>%
select(symbol, company) %>%
tq_get(from = "2007-01-01", to = "2016-12-31") %>%
# Nest
group_by(symbol, company) %>%
nest() %>%
# Apply the get_model() function to the new "nested" data column
mutate(model = map(data, get_model)) %>%
# Unnest and collect slope
unnest(model) %>%
filter(term == "year(date)") %>%
arrange(desc(estimate)) %>%
select(-term)
stocks_model_stats
## # A tibble: 5 × 7
## # Groups: symbol, company [5]
## symbol company data estimate std.e…¹ stati…² p.value
## <chr> <chr> <list> <dbl> <dbl> <dbl> <dbl>
## 1 MAA Mid-America Apartment Commun… <tibble> 0.0241 0.0200 1.20 0.263
## 2 NTAP NetApp Inc. <tibble> 0.0200 0.0558 0.358 0.729
## 3 SRE Sempra Energy <tibble> 0.0129 0.0241 0.533 0.608
## 4 FFIV F5 Inc. <tibble> 0.00127 0.0533 0.0238 0.982
## 5 WYNN Wynn Resorts Limited <tibble> -0.00738 0.0627 -0.118 0.909
## # … with abbreviated variable names ¹std.error, ²statistic
We’re done! We now have the coefficient of the linear regression that
tracks the direction of the trend line. We can easily extend this type
of analysis to larger lists or stock indexes. For example, the entire
S&P500 could be analyzed removing the sample_n()
following the call to tq_index("SP500").
