9  Value and Bivariate Sorts: Original version

library(tidyverse)
library(DBI)
library(scales)
library(lmtest)
library(sandwich)

tidy_finance <- dbConnect(
  duckdb::duckdb(),
  "data/tidy_finance.duckdb",
  read_only = TRUE)

crsp_monthly <- tbl(tidy_finance, "crsp_monthly") |>
  collect()

crsp_monthly <- crsp_monthly |>
  select(
    permno, gvkey, month, ret_excess,
    mktcap, mktcap_lag, exchange
  ) |>
  drop_na()

compustat <- tbl(tidy_finance, "compustat") |>
  collect()

be <- compustat |>
  select(gvkey, datadate, be) |>
  drop_na() |>
  mutate(month = floor_date(ymd(datadate), "month"))

9.1 Book-to-Market Ratio

me <- crsp_monthly |>
  mutate(sorting_date = month %m+% months(1)) |>
  select(permno, sorting_date, me = mktcap)

bm <- be |>
  inner_join(crsp_monthly |>
    select(month, permno, gvkey, mktcap), by = c("gvkey", "month")) |>
  mutate(
    bm = be / mktcap,
    sorting_date = month %m+% months(6),
    comp_date = sorting_date
  ) |>
  select(permno, gvkey, sorting_date, comp_date, bm)

data_for_sorts <- crsp_monthly |>
  left_join(bm, by = c("permno",
    "gvkey",
    "month" = "sorting_date"
  )) |>
  left_join(me, by = c("permno", "month" = "sorting_date")) |>
  select(
    permno, gvkey, month, ret_excess,
    mktcap_lag, me, bm, exchange, comp_date
  )

data_for_sorts <- data_for_sorts |>
  arrange(permno, gvkey, month) |>
  group_by(permno, gvkey) |>
  fill(bm, comp_date) |>
  filter(comp_date > month %m-% months(12)) |>
  select(-comp_date) |>
  drop_na()

assign_portfolio <- function(data, 
                             sorting_variable, 
                             n_portfolios, 
                             exchanges) {
  breakpoints <- data |>
    filter(exchange %in% exchanges) |>
    pull({{ sorting_variable }}) |>
    quantile(
      probs = seq(0, 1, length.out = n_portfolios + 1),
      na.rm = TRUE,
      names = FALSE
    )

  assigned_portfolios <- data |>
    mutate(portfolio = findInterval(
      pick(everything()) |>
        pull({{ sorting_variable }}),
      breakpoints,
      all.inside = TRUE
    )) |>
    pull(portfolio)
  
  return(assigned_portfolios)
}

9.2 Independent Sorts

value_portfolios <- data_for_sorts |>
  group_by(month) |>
  mutate(
    portfolio_bm = assign_portfolio(
      data = pick(everything()),
      sorting_variable = "bm",
      n_portfolios = 5,
      exchanges = c("NYSE")
    ),
    portfolio_me = assign_portfolio(
      data = pick(everything()),
      sorting_variable = "me",
      n_portfolios = 5,
      exchanges = c("NYSE")
    ),
    portfolio_combined = str_c(portfolio_bm, portfolio_me)
  ) |>
  group_by(month, portfolio_combined) |>
  summarize(
    ret = weighted.mean(ret_excess, mktcap_lag),
    portfolio_bm = unique(portfolio_bm),
    .groups = "drop"
  )

value_premium <- value_portfolios |>
  group_by(month, portfolio_bm) |>
  summarize(ret = mean(ret), .groups = "drop_last") |>
  summarize(value_premium = ret[portfolio_bm == max(portfolio_bm)] -
    ret[portfolio_bm == min(portfolio_bm)])

mean(value_premium$value_premium * 100)

[1] 0.3842144

9.3 Dependent Sorts

value_portfolios <- data_for_sorts |>
  group_by(month) |>
  mutate(portfolio_me = assign_portfolio(
    data = pick(everything()),
    sorting_variable = "me",
    n_portfolios = 5,
    exchanges = c("NYSE")
  )) |>
  group_by(month, portfolio_me) |>
  mutate(
    portfolio_bm = assign_portfolio(
      data = pick(everything()),
      sorting_variable = "bm",
      n_portfolios = 5,
      exchanges = c("NYSE")
    ),
    portfolio_combined = str_c(portfolio_bm, portfolio_me)
  ) |>
  group_by(month, portfolio_combined) |>
  summarize(
    ret = weighted.mean(ret_excess, mktcap_lag),
    portfolio_bm = unique(portfolio_bm),
    .groups = "drop"
  )

value_premium <- value_portfolios |>
  group_by(month, portfolio_bm) |>
  summarize(ret = mean(ret), .groups = "drop_last") |>
  summarize(value_premium = ret[portfolio_bm == max(portfolio_bm)] -
    ret[portfolio_bm == min(portfolio_bm)])

mean(value_premium$value_premium * 100)

[1] 0.3288267