Solutions to exercises

For annual values, see 18.1:

data_ml %>%

group_by(date) %>%

mutate(growth = Pb > median(Pb)) %>%

# Creates the sort

ungroup() %>%

# Ungroup

mutate(year = lubridate::year(date)) %>%

# Creates a year variable

group_by(year, growth) %>%

# Analyze by year & sort

summarize(ret = mean(R1M_Usd)) %>%

# Compute average return

ggplot(aes(x = year, y = ret, fill = growth)) theme(legend.position = c(0.7, 0.8))

+ geom_col(position = “dodge”) + # Plot!

Figure 18.1 The value factor: annual returns. https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9781003034858/4da82c50-f1fa-4834-bfc4-d8efd0ea8260/content/fig18_1_C.jpg"/>