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- ################################################################################
- ### Fetch data on the selected stock and analyze for under/overvaluation. This
- ### is a simple method which involves determining the long-term trend of the
- ### stock, and seeing if it is currently above/below that trend. It should
- ### work decently on stocks that are "well behaved" (long track record of
- ### continuous gains, and a high r^2)
- ################################################################################
- library(tidyverse)
- ### Stock ticker for the stock of interest
- stock <- "BRK-A" ; stock_txt <- "Berkshire Hathaway class A"
- ### What are the start/end dates you are interested in
- date_start <- as.Date("1995-01-01")
- date_end <- as.Date(Sys.Date())
- ################################################################################
- ### No changes should be necessary below here.
- ################################################################################
- ### Function to add recession bars to graphs
- geom_recession_bars <- function(date_start, date_end, fill = "darkseagreen4") {
- date_start <- as.Date(date_start, origin = "1970-01-01")
- date_end <- as.Date(date_end, origin = "1970-01-01")
- recessions_tibble <-
- tribble(
- ~peak, ~trough,
- "1953-07-01", "1954-05-01",
- "1957-08-01", "1958-04-01",
- "1960-04-01", "1961-02-01",
- "1969-12-01", "1970-11-01",
- "1973-11-01", "1975-03-01",
- "1980-01-01", "1980-07-01",
- "1981-07-01", "1982-11-01",
- "1990-07-01", "1991-03-01",
- "2001-03-01", "2001-11-01",
- "2007-12-01", "2009-06-01",
- "2020-02-01", "2020-04-01"
- ) %>%
- mutate(peak = as.Date(peak), trough = as.Date(trough))
- recessions_trim <- recessions_tibble %>%
- filter(peak >= min(date_start) & trough <= max(date_end))
- if (nrow(recessions_trim) > 0) {
- recession_bars <- geom_rect(data = recessions_trim, inherit.aes = F,
- fill = fill, alpha = 0.25,
- aes(xmin = as.Date(peak, origin = "1970-01-01"),
- xmax = as.Date(trough, origin = "1970-01-01"),
- ymin = -Inf, ymax = +Inf))
- } else {
- recession_bars <- geom_blank()
- }
- }
- ### Fetch stock prices from Yahoo Finance
- prices <-
- quantmod::getSymbols(stock, env = NULL, src = "yahoo", from = date_start, to = date_end) %>%
- zoo::fortify.zoo() %>%
- select(Index, 2) %>%
- tsibble::as_tsibble() %>%
- rename(date = Index, value = 2) %>%
- filter(value != 0) %>%
- arrange(date) %>%
- mutate(
- log_value = log(value),
- decimal_date = decimal_date(date)
- )
- ### Do a linear regression and pluck out the coefficients
- fit_prices <- lm(log_value ~ decimal_date, data = prices %>% filter(value != 0))
- coeff1 <- fit_prices %>% broom::tidy() %>% filter(term == "(Intercept)") %>% pull("estimate")
- coeff2 <- fit_prices %>% broom::tidy() %>% filter(term == "decimal_date") %>% pull("estimate")
- adj_r2 <- fit_prices %>% broom::glance() %>% pull("adj.r.squared")
- growth <- paste((coeff2 * 100) %>% round(digits = 1), "%/yr", sep = "")
- doubling_time <- paste((log(2) / log(1 + coeff2)) %>% round(digits = 1), "yrs")
- ### Use the regression coefficients to compute the trend
- prices <-
- prices %>%
- mutate(fit_log_value = coeff1 + coeff2 * decimal_date) %>%
- mutate(fit_value = exp(fit_log_value)) %>%
- mutate(value_per = 1 - (fit_value / value))
- valuation <- prices %>% arrange(date) %>% tail(n = 1) %>% pull(value_per)
- # Determine number of years of trend growth represented by over/undervaluation
- years <- (abs(valuation) / coeff2) %>% round(digits = 1)
- # Format string to display valuation in useful form
- valuation <- (100 * valuation) %>% round(digits = 1)
- fair_price <- format(round((prices %>% tail(n = 1) %>% pull(value))*(1 - valuation / 100), digits = 2), big.mark = ",")
- if (valuation > 0) {
- valuation_txt <- paste("overvalued by ", abs(valuation), "% (", years, " years growth), fair price = $", fair_price, sep = "")
- } else {
- valuation_txt <- paste("undervalued by ", abs(valuation), "% (", years, " years growth), fair price = $", fair_price, sep = "")
- }
- ##########################################################################
- ### Graph 1: Price of stock
- ##########################################################################
- p_norm <-
- ggplot(data = prices) +
- theme_bw() +
- theme(legend.title = element_blank()) +
- geom_line(aes(x = date, y = value)) +
- geom_line(aes(x = date, y = fit_value)) +
- geom_recession_bars(min(prices$date), max(prices$date)) +
- scale_x_date(breaks = scales::pretty_breaks(10)) +
- scale_y_continuous(breaks = scales::pretty_breaks(10), labels = scales::label_comma()) +
- labs(title = paste("Current stock price: $", format(round(prices %>% tail(n = 1) %>% pull(value), digits = 2), big.mark = ","), ", growth = ", growth, ", doubling time = ", doubling_time, sep = ""),
- caption = "", x = "", y = "US $")
- print(p_norm)
- ##########################################################################
- ### Graph 2: log(Price of stock)
- ##########################################################################
- caption <- paste("Data retrieved from Yahoo Finance on", format(Sys.time(), "%B %d, %Y at %I:%M %p %Z"))
- p_log <-
- ggplot(data = prices) +
- theme_bw() +
- theme(legend.title = element_blank()) +
- geom_line(aes(x = date, y = log_value)) +
- geom_line(aes(x = date, y = fit_log_value)) +
- geom_recession_bars(min(prices$date), max(prices$date)) +
- scale_x_date(breaks = scales::pretty_breaks(10)) +
- scale_y_continuous(breaks = scales::pretty_breaks(10)) +
- labs(title = paste("log(Stock Price) adj R^2 = ", adj_r2 %>% round(digits = 3), sep = ""),
- caption = "", x = "", y = "log(US $)")
- print(p_log)
- ##########################################################################
- ### Graph 3: Standard deviation above/below trend
- ##########################################################################
- # Compute mean / standard deviation of % above/below trend
- mean_stock <- prices %>% filter(value != 0) %>% pull(value_per) %>% mean()
- sd_stock <- prices %>% filter(value != 0) %>% pull(value_per) %>% sd()
- p_trend <-
- ggplot(data = prices) +
- theme_bw() +
- theme(legend.title = element_blank()) +
- geom_line(aes(x = date, y = 1 - (fit_value / value))) +
- geom_hline(yintercept = 0, linetype = "dotted") +
- geom_recession_bars(min(prices$date), max(prices$date)) +
- scale_x_date(breaks = scales::pretty_breaks(10)) +
- scale_y_continuous(breaks = scales::pretty_breaks(10),
- labels = scales::percent_format(accuracy = 1),
- sec.axis = sec_axis(~ (. - mean_stock) / sd_stock,
- name = "Z-Score",
- breaks = scales::pretty_breaks(6))
- ) +
- labs(title = paste("Detrended stock price, ", valuation_txt, sep = ""),
- caption = caption, x = "", y = "%")
- print(p_trend)
- ### Print final graph
- title <- cowplot::ggdraw() + cowplot::draw_label(paste(stock_txt, " (", stock, ")", sep = ""), fontface = "bold")
- print(cowplot::plot_grid(title, p_norm, p_log, p_trend, nrow = 4, ncol = 1, align = "v", rel_heights = c(0.1, 1, 1, 1)))
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