SP500 US Presidency Analysis [USA]

chart_sp500_by_presidency.r

library(readr)
library(dplyr)
library(ggplot2)
library(scales)
library(quantmod)

sf <- 2
width <- 600 * sf
height <- 335 * sf

options(vsc.dev.args = list(width = width, height = height, res = 72 * sf))

sdate <- as.Date("1900-01-01")
edate <- Sys.Date()

# Helper function to get symbol data and process it
get_symbol_data <- function(symbol, close_col) {
  data <- getSymbols(symbol, from = sdate, to = edate, auto.assign = FALSE) %>%
    as.data.frame() %>%
    mutate(date = row.names(.)) %>%
    select(date, close = !!sym(close_col)) %>%
    mutate(date = as.Date(date))
  return(data)
}

watermark <- function(
    x = structure(Inf),
    y = Inf,
    latest = "") {
  ggplot2::annotate("text",
    y = y,
    x = x,
    label = paste("@USMortality", latest),
    vjust = 1,
    hjust = 1,
    col = "#000000",
    cex = 6,
    fontface = "bold",
    alpha = 0.1
  )
}

# Define custom colors for Democrats (blue) and Republicans (red)
party_colors <- c(
  "Democrat" = "#1f78b4", # blue
  "Republican" = "#e31a1c" # red
)

# Load the S&P 500 data
df <- get_symbol_data("^GSPC", "GSPC.Close")

# Create a data frame of election details
election_info <- data.frame(
  election_date = as.Date(c(
    "1928-11-06", "1932-11-08", "1936-11-03", "1940-11-05", "1944-11-07",
    "1948-11-02", "1952-11-04", "1956-11-06", "1960-11-08", "1964-11-03",
    "1968-11-05", "1972-11-07", "1976-11-02", "1980-11-04", "1984-11-06",
    "1988-11-08", "1992-11-03", "1996-11-05", "2000-11-07", "2004-11-02",
    "2008-11-04", "2012-11-06", "2016-11-08", "2020-11-03"
  )),
  president = c(
    "Herbert Hoover", "Franklin D. Roosevelt", "Franklin D. Roosevelt",
    "Franklin D. Roosevelt", "Franklin D. Roosevelt", "Harry S. Truman",
    "Dwight D. Eisenhower", "Dwight D. Eisenhower", "John F. Kennedy",
    "Lyndon B. Johnson", "Richard Nixon", "Richard Nixon", "Jimmy Carter",
    "Ronald Reagan", "Ronald Reagan", "George H.W. Bush", "Bill Clinton",
    "Bill Clinton", "George W. Bush", "George W. Bush", "Barack Obama",
    "Barack Obama", "Donald Trump", "Joe Biden"
  ),
  party = c(
    "Republican", "Democrat", "Democrat", "Democrat", "Democrat", "Democrat",
    "Republican", "Republican", "Democrat", "Democrat", "Republican",
    "Republican", "Democrat", "Republican", "Republican", "Republican",
    "Democrat", "Democrat", "Republican", "Republican", "Democrat", "Democrat",
    "Republican", "Democrat"
  )
)

days <- 365.25 * 4
# Filter the S&P 500 data for one year after each election date
ts <- election_info |>
  rowwise() |>
  do({
    election_date <- .$election_date
    president <- .$president
    party <- .$party
    df_filtered <- df |>
      filter(date >= election_date & date <= election_date + days) |>
      mutate(
        election_date = election_date,
        president = president,
        party = party,
        days_since_election = as.numeric(date - election_date),
        pct_change = ((close - first(close)) / first(close))
      )
    df_filtered
  }) |>
  bind_rows() |>
  filter(days_since_election < days)

# Plot the data
chart <- ggplot(ts, aes(
  x = days_since_election,
  y = pct_change, group = election_date
)) +
  geom_line(aes(color = president)) +
  scale_y_continuous(labels = percent_format(scale = 100)) +
  labs(
    title = "S&P 500 Percentage Change by U.S. President",
    x = "Days Since Election",
    y = "Percentage Change (%)",
    color = "President"
  ) +
  theme_minimal() +
  watermark()

ggplot2::ggsave(
  filename = "chart1.png", plot = chart, width = width, height = height,
  units = "px", dpi = 72 * sf, device = grDevices::png, type = c("cairo")
)

ts2 <- ts |>
  group_by(party, days_since_election) |>
  summarise(
    sd_value = sd(pct_change),
    pct_change = mean(pct_change),
    n = n(),
    .groups = "drop"
  ) |>
  mutate(
    error_margin = if_else(!is.na(sd_value) & n > 1,
      qt(0.975, df = n - 1) * sd_value / sqrt(n), NA_real_
    ),
    lower_ci =
      if_else(!is.na(error_margin), pct_change - error_margin, NA_real_),
    upper_ci =
      if_else(!is.na(error_margin), pct_change + error_margin, NA_real_)
  )

# Plot the data with 95% CI
chart <- ggplot(ts2, aes(
  x = days_since_election,
  y = pct_change, color = party
)) +
  geom_line() +
  geom_ribbon(aes(ymin = lower_ci, ymax = upper_ci, fill = party),
    alpha = 0.2, color = NA, show.legend = FALSE
  ) +
  scale_y_continuous(
    labels = percent_format(scale = 100),
    limits = c(-0.25, 0.75),
    oob = scales::squish
  ) +
  scale_color_manual(values = party_colors) +
  scale_fill_manual(values = party_colors) +
  labs(
    title = "S&P 500 Percentage Change by Party",
    subtitle = "Since 1927",
    x = "Days Since Election",
    y = "Percentage Change (%)",
    color = "Party",
    linetype = "President"
  ) +
  theme_minimal() +
  watermark()

ggplot2::ggsave(
  filename = "chart2.png", plot = chart, width = width, height = height,
  units = "px", dpi = 72 * sf, device = grDevices::png, type = c("cairo")
)


ts3 <- ts |>
  group_by(days_since_election) |>
  summarise(
    sd_value = sd(pct_change),
    pct_change = mean(pct_change),
    n = n(),
    .groups = "drop"
  ) |>
  mutate(
    error_margin = if_else(!is.na(sd_value) & n > 1,
      qt(0.975, df = n - 1) * sd_value / sqrt(n), NA_real_
    ),
    lower_ci = if_else(!is.na(error_margin), pct_change - error_margin, NA_real_),
    upper_ci = if_else(!is.na(error_margin), pct_change + error_margin, NA_real_)
  )

chart <- ggplot(ts3, aes(x = days_since_election, y = pct_change)) +
  geom_line(aes(color = "Mean"), linewidth = 1, linetype = "solid") +
  geom_ribbon(aes(ymin = lower_ci, ymax = upper_ci),
    alpha = 0.2, color = NA, show.legend = FALSE
  ) +
  geom_line(
    data = ts |> filter(president == "Donald Trump"),
    aes(x = days_since_election, y = pct_change, color = "Trump"), linewidth = 1
  ) +
  geom_line(
    data = ts |> filter(president == "Joe Biden"),
    aes(x = days_since_election, y = pct_change, color = "Biden"), linewidth = 1
  ) +
  scale_y_continuous(
    labels = scales::percent_format(scale = 100),
    limits = c(-0.1, 0.75),
    oob = scales::squish
  ) +
  scale_color_manual(
    values = c(
      "Mean" = "black",
      "Trump" = unname(party_colors[2]),
      "Biden" = unname(party_colors[1])
    ),
    breaks = c("Mean", "Trump", "Biden"), # Custom order in the legend
    name = "Legend"
  ) +
  labs(
    title =
      "S&P 500 Mean Change since U.S. Presidential Election - Trump vs. Biden",
    subtitle = "Since 1927",
    x = "Days Since Election",
    y = "Percentage Change (%)"
  ) +
  theme_minimal() +
  watermark()

ggplot2::ggsave(
  filename = "chart3.png", plot = chart, width = width, height = height,
  units = "px", dpi = 72 * sf, device = grDevices::png, type = c("cairo")
)

# Calculate Gains Normalized by Years in Office
presidential_gains <- ts |>
  group_by(president, party) |>
  summarize(
    start_date = min(date),
    end_date = max(date),
    start_close = close[which.min(date)],
    end_close = close[which.max(date)]
  ) |>
  mutate(
    total_days = as.numeric(end_date - start_date),
    years_in_office = total_days / 365.25, # Account for leap years
    total_gain = ((end_close - start_close) / start_close) * 100,
    annualized_gain = total_gain / years_in_office
  )

# Plotting the Annualized Gains
chart <- ggplot(presidential_gains, aes(
  x = reorder(president, annualized_gain),
  y = annualized_gain, fill = party
)) +
  geom_bar(stat = "identity") +
  geom_text(
    aes(label = sprintf("%.1f%%", annualized_gain)),
    hjust = -0.1, # Adjust this value as needed
    color = "black",
    size = 3
  ) +
  coord_flip() +
  labs(
    title = "Annualized Stock Market Gain During Presidency",
    x = "President",
    y = "Annualized Percentage Gain (%)"
  ) +
  scale_fill_manual(values = party_colors) +
  theme_minimal() +
  watermark()

ggplot2::ggsave(
  filename = "chart4.png", plot = chart, width = width, height = height,
  units = "px", dpi = 72 * sf, device = grDevices::png, type = c("cairo")
)

https://www.mortality.watch/charts/list.html#sp500-us-presidency-analysis-usa