Constructing a factor from scratch

Following along without a WRDS subscription

The real asset growth factor is built from CRSP (stock returns) and Compustat (accounting statements), both distributed through Wharton Research Data Services (WRDS) and both requiring an institutional subscription. So that this vignette builds anywhere — on CRAN, on a continuous-integration runner, on a laptop with no credentials and no network — every download below uses domain = "Pseudo Data" instead of domain = "WRDS".

Pseudo data mirror the schema of the real WRDS panels exactly: the same column names, types, and panel structure, and a calibrated mix of exchanges and industries. Every line of cleaning, lagging, and sorting code therefore runs unchanged. What pseudo data deliberately do not carry is economic content. Returns are independent random draws, and the asset growth signal is unrelated to subsequent returns by construction. Every number in this vignette is a simulation artifact — not just the cross-sectional premia and slopes, but the levels too (the uniformly positive “excess” returns reflect a built-in drift in the generator, not a market risk premium). Treat the code as the lesson and the numbers as placeholders; to reproduce the published premium, swap "Pseudo Data" for "WRDS" (after a one-time set_wrds_credentials()) and rerun.

library(tidyfinance)
library(dplyr)
library(tidyr)
library(lubridate)

Two internal arguments govern the simulated panels: seed fixes the random draws so the document is reproducible, and n_assets sets the size of the cross-section. We deliberately leave both at their defaults (seed = 1234 and n_assets = 1000) instead of passing them explicitly. The WRDS downloads do not accept these arguments, so keeping them out of the download_data() calls is what lets you switch the domain at the end of this vignette without touching anything else. The only parameters we set are the sample dates, which mirror the empirical study.

date_start <- as.Date("1972-01-01")
date_end <- as.Date("2024-12-31")

Step 1: Download the data

Constructing the factor needs two panels: monthly stock returns from CRSP and annual accounting data from Compustat. download_data() is the single entry point for both; the domain and dataset arguments pick the source.

Setting add_ccm_links = TRUE appends the CRSP-Compustat linking identifier (gvkey) to the return panel, which we need to merge in accounting data later.

crsp_monthly <- download_data(
  domain = "Pseudo Data",
  dataset = "crsp_monthly",
  start_date = date_start,
  end_date = date_end,
  add_ccm_links = TRUE
)
crsp_monthly
#> # A tibble: 636,000 × 15
#>    permno date       calculation_date      ret shrout   prc primaryexch siccd
#>     <int> <date>     <date>              <dbl>  <dbl> <dbl> <chr>       <int>
#>  1      1 1972-01-01 1972-01-31       -0.0361  12887. 201.  Q            1284
#>  2      1 1972-02-01 1972-02-29       -0.0193  26252. 997.  Q            1284
#>  3      1 1972-03-01 1972-03-31       -0.0700   5872. 294.  Q            1284
#>  4      1 1972-04-01 1972-04-30        0.0314  45175. 294.  Q            1284
#>  5      1 1972-05-01 1972-05-31       -0.00748 42106. 397.  Q            1284
#>  6      1 1972-06-01 1972-06-30       -0.132   21754. 801.  Q            1284
#>  7      1 1972-07-01 1972-07-31        0.00973 27680.  92.1 Q            1284
#>  8      1 1972-08-01 1972-08-31        0.0241  37520.  34.9 Q            1284
#>  9      1 1972-09-01 1972-09-30       -0.0371  27728. 366.  Q            1284
#> 10      1 1972-10-01 1972-10-31       -0.0859  11404.  66.9 Q            1284
#> # ℹ 635,990 more rows
#> # ℹ 7 more variables: listing_age <int>, mktcap <dbl>, mktcap_lag <dbl>,
#> #   exchange <chr>, industry <chr>, ret_excess <dbl>, gvkey <chr>

Each row is one stock-month, already cleaned: market capitalization (mktcap) and its one-month lag (mktcap_lag) for value weighting, a readable exchange label, the SIC industry code (siccd), and excess returns (ret_excess) net of the risk-free rate. On real WRDS data download_data() also restricts the universe to common equity, folds in delisting returns, and maps exchange and industry codes. Here those columns are simulated, but the exchange and industry mix is calibrated to real CRSP frequencies and siccd is drawn to match each firm’s industry, so the exchange and industry filters we apply in Step 5 still drop the intended firms.

Next we download annual Compustat statements. The additional_columns argument pulls in any raw fields beyond the defaults; we ask for total assets (at) — the input to the asset growth signal — and income before extraordinary items (ib), which we use later as an earnings filter.

compustat_annual <- download_data(
  domain = "Pseudo Data",
  dataset = "compustat_annual",
  start_date = date_start,
  end_date = date_end,
  additional_columns = c("at", "ib"),
  only_usd = TRUE
) |>
  select(gvkey, date, at, ib)
compustat_annual
#> # A tibble: 53,000 × 4
#>    gvkey  date          at      ib
#>    <chr>  <date>     <dbl>   <dbl>
#>  1 010001 1972-12-01 154.  -11.4  
#>  2 010001 1973-12-01 145.   15.6  
#>  3 010001 1974-12-01 117.    7.28 
#>  4 010001 1975-12-01 138.   50.9  
#>  5 010001 1976-12-01  90.0   3.79 
#>  6 010001 1977-12-01  62.0   0.647
#>  7 010001 1978-12-01  95.2  18.6  
#>  8 010001 1979-12-01 107.    5.49 
#>  9 010001 1980-12-01  92.7   8.28 
#> 10 010001 1981-12-01 138.   37.9  
#> # ℹ 52,990 more rows

Step 2: Construct and lag the signal

Asset growth for firm \(i\) in year \(t\) is the year-over-year percentage change in total assets,

\[ AG_{i,t} = \frac{AT_{i,t} - AT_{i,t-1}}{AT_{i,t-1}}. \]

Computing it means lining up each firm’s total assets with its own value one year earlier. add_lagged_columns() does exactly that: grouped by firm (gvkey), it attaches each firm’s total assets from exactly one year before (lag = years(1)), matching the lagged date directly. The same function also takes a max_lag to instead search a window for the most recent value, which we use in the next step. We then take the percentage change and set non-finite results (from a zero or missing prior value) to NA.

Ratios of balance-sheet items produce extreme outliers, and a handful of firms with explosive growth would otherwise dominate summary statistics and regression slopes. We therefore winsorize the signal once, within each statement date, and carry the winsorized variable through the rest of the vignette: winsorize() pulls the most extreme 1% on each tail back to the 1st and 99th percentiles. If you would rather drop extreme observations than clamp them, the package also provides trim(), which removes everything beyond the same cutoffs. Winsorizing matters for the Fama-MacBeth regression in Step 6, where the magnitude of the signal enters directly, but it does not affect the portfolio sorts in Steps 4 and 5: a sort depends only on how firms rank, and winsorizing compresses the tails without reordering them.

sorting_variable_data <- compustat_annual |>
  add_lagged_columns(
    cols = "at",
    lag = years(1),
    by = "gvkey"
  ) |>
  mutate(
    asset_growth = (at - at_lag) / at_lag,
    asset_growth = if_else(is.finite(asset_growth), asset_growth, NA_real_)
  ) |>
  group_by(date) |>
  mutate(asset_growth = winsorize(asset_growth, cut = 0.01)) |>
  ungroup() |>
  select(gvkey, date, asset_growth, ib)

Before using the signal, it pays to look at it. create_summary_statistics() returns the standard cross-sectional summary for one or more variables.

sorting_variable_data |>
  drop_na(asset_growth) |>
  create_summary_statistics(asset_growth, detail = TRUE) |>
  select(variable, n, mean, sd, q05, q50, q95) |>
  knitr::kable(
    digits = 3,
    caption = "Cross-sectional distribution of asset growth."
  )

Even after winsorizing, the mean sits above the median: asset growth is right-skewed, with a tail of fast-expanding firms pulling the average up. That skew is a property of the constructed variable and so survives in pseudo data — unlike any relationship between the signal and future returns, which does not.

Step 3: Align the signal with returns

The signal is annual; returns are monthly. To form portfolios we need each stock-month paired with the most recently observable value of asset growth — using only what an investor could have known at the formation date. This is where look-ahead bias (slipping in information that was not yet public) creeps in if you are not careful: the accounting for fiscal year \(t\) is not public the instant the year ends.

join_lagged_values() merges the annual signal onto the monthly panel through a configurable lag window. Setting ff_adjustment = TRUE applies the Fama and French (1993) timing convention: accounting data are assumed public roughly six months after the fiscal year-end and held for the following twelve months. Because the pseudo statements all carry a December fiscal year-end, the 7-to-18-month window places each signal in the July-to-June holding period that the convention prescribes. The window is anchored on the statement date, so for real firms with other fiscal year-ends the exact calendar months shift accordingly.

sorting_data <- crsp_monthly |>
  join_lagged_values(
    new_data = sorting_variable_data,
    id_keys = "gvkey",
    min_lag = months(7),
    max_lag = months(18),
    ff_adjustment = TRUE
  ) |>
  select(
    date,
    permno,
    exchange,
    siccd,
    ret_excess,
    mktcap_lag,
    asset_growth,
    ib
  ) |>
  filter(date >= date_start + months(12) + months(18))

The final filter() drops the start of the sample, where the one-year growth calculation and the lag window leave the signal mechanically missing. What remains is a clean stock-month panel carrying excess returns, a value weight, and a properly lagged signal.

Step 4: Sort stocks into portfolios

A portfolio sort ranks stocks each period by the signal and splits them into ordered groups; the spread between the extreme groups is the factor return. implement_portfolio_sort() runs the whole sort in one call. It takes the panel, the sorting variable, the sorting method, and a portfolio_sort_options object that bundles how breakpoints are formed.

For the baseline we sort into quintiles, computing breakpoints from NYSE-listed stocks only — a standard convention that keeps the many small NASDAQ firms from dominating the cutoffs — and rebalance every July to match the Fama-French timing we used when lagging the signal.

options_baseline <- portfolio_sort_options(
  breakpoint_options_main = breakpoint_options(
    n_portfolios = 5,
    breakpoints_exchanges = "NYSE"
  )
)

portfolio_returns <- implement_portfolio_sort(
  data = sorting_data,
  sorting_variables = "asset_growth",
  sorting_method = "univariate",
  rebalancing_month = 7,
  portfolio_sort_options = options_baseline
)
portfolio_returns
#> # A tibble: 3,030 × 5
#>    portfolio date       ret_excess_vw ret_excess_ew ret_excess_vw_capped
#>        <int> <date>             <dbl>         <dbl>                <dbl>
#>  1         1 1974-07-01       0.00440      0.00268              0.00329 
#>  2         1 1974-08-01       0.0144       0.0155               0.0183  
#>  3         1 1974-09-01       0.0127       0.0174               0.0122  
#>  4         1 1974-10-01       0.00393     -0.00804             -0.000116
#>  5         1 1974-11-01       0.00896      0.00957              0.0118  
#>  6         1 1974-12-01       0.00973      0.00277              0.00876 
#>  7         1 1975-01-01       0.00745      0.0136               0.00862 
#>  8         1 1975-02-01       0.00381     -0.000778             0.00609 
#>  9         1 1975-03-01       0.00760      0.0111               0.00828 
#> 10         1 1975-04-01      -0.00240     -0.00264             -0.00322 
#> # ℹ 3,020 more rows

This baseline call named only two things; everything else came from a documented default. That is the point: a default call reproduces standard practice, and every construction choice that papers usually leave implicit — an 80th-percentile cap for the capped value-weighted return, no minimum portfolio size, the standard CRSP/Compustat column names — is here an argument you can see and change. The output reports three weighting schemes per portfolio-month: value-weighted (ret_excess_vw), equal-weighted (ret_excess_ew), and capped value-weighted (ret_excess_vw_capped).

The factor itself is the long-short spread. compute_long_short_returns() takes the difference between the extreme portfolios; direction = "bottom_minus_top" goes long low-asset-growth firms and short high-asset-growth firms, the sign of the documented premium.

factor_returns <- portfolio_returns |>
  compute_long_short_returns(direction = "bottom_minus_top")

portfolio_summary <- bind_rows(
  portfolio_returns |>
    mutate(portfolio = as.character(portfolio)),
  factor_returns |>
    mutate(portfolio = "Long-short")
) |>
  group_by(portfolio) |>
  summarize(
    mean_vw = mean(ret_excess_vw, na.rm = TRUE) * 12,
    mean_ew = mean(ret_excess_ew, na.rm = TRUE) * 12,
    .groups = "drop"
  )

portfolio_summary |>
  knitr::kable(
    digits = 4,
    col.names = c("Portfolio", "Value-weighted", "Equal-weighted"),
    caption = paste(
      "Annualized mean excess returns by asset growth quintile, plus the",
      "long-short spread. On real data the value-weighted mean declines",
      "across quintiles and the long-short row is a sizable premium; here",
      "the quintile ordering and the long-short value (and its sign) are",
      "sampling noise."
    )
  )

On real WRDS data the value-weighted means decline monotonically from the low- to the high-asset-growth quintile, and the long-short spread reproduces the negative premium of Cooper, Gulen, and Schill (2008). On the pseudo panel there is no systematic decline: the differences across quintiles, any apparent ordering, and the long-short spread (whatever its sign on a given run) are all sampling noise, as they must be.

Step 5: Customize the sort

The baseline took nearly every default. A realistic specification overrides several at once, and portfolio_sort_options keeps them all in one place. Here we exclude financial and utility firms and firms with non-positive earnings, ignore the smallest 20% of NYSE firms when setting breakpoints, and add a second sort on size: a dependent bivariate sort that controls for size by first bucketing on it, then sorting on asset growth within each bucket.

options_customized <- portfolio_sort_options(
  filter_options = filter_options(
    exclude_financials = TRUE,
    exclude_utilities = TRUE,
    exclude_negative_earnings = TRUE
  ),
  breakpoint_options_main = breakpoint_options(
    n_portfolios = 5,
    breakpoints_exchanges = "NYSE",
    breakpoints_min_size_threshold = 0.2
  ),
  breakpoint_options_secondary = breakpoint_options(
    n_portfolios = 2,
    breakpoints_exchanges = "NYSE"
  )
)

factor_returns_customized <- implement_portfolio_sort(
  data = sorting_data,
  sorting_variables = c("asset_growth", "mktcap_lag"),
  sorting_method = "bivariate-dependent",
  rebalancing_month = 7,
  portfolio_sort_options = options_customized,
  min_portfolio_size = 10,
  quiet = TRUE
) |>
  compute_long_short_returns(direction = "bottom_minus_top")

Moving from a univariate to a size-controlled bivariate sort was a one-word change to sorting_method; the surrounding pipeline is identical. That is the point of exposing construction choices as arguments. The lower-level building blocks (compute_breakpoints(), assign_portfolio(), compute_portfolio_returns()) remain available when you need finer control over one stage.

Step 6: Run an asset-pricing test

A long-short return is one verdict on whether a signal predicts the cross-section of returns. A Fama-MacBeth regression (Fama and MacBeth 1973) gives a complementary one: it runs a cross-sectional regression of returns on the signal period by period, then averages the slopes into a risk premium with Newey-West standard errors (Newey and West 1987). estimate_fama_macbeth() does this from a panel and a model formula.

The signal is already winsorized from Step 2, so we only add log market capitalization as a size control.

fm_data <- sorting_data |>
  drop_na(asset_growth, mktcap_lag, ret_excess) |>
  mutate(log_mktcap = log(mktcap_lag))

fm_results <- estimate_fama_macbeth(
  data = fm_data,
  model = "ret_excess ~ asset_growth + log_mktcap"
)

fm_results |>
  knitr::kable(
    digits = 4,
    caption = paste(
      "Fama-MacBeth regression of monthly excess returns on lagged asset",
      "growth and log market cap. On real data the asset growth slope is",
      "negative and significant; on pseudo data it is indistinguishable",
      "from zero. The large, 'significant' intercept is a mechanical",
      "artifact of the simulated returns' built-in positive drift, not an",
      "alpha."
    )
  )

On real data the slope on asset_growth is negative and significant, matching the portfolio-sort evidence: high-asset-growth firms earn lower subsequent returns. On the pseudo panel the slope is statistical noise. The intercept row carries a large \(t\)-statistic here, but that reflects only the generator’s built-in positive return drift, not a real average return. Switching the standard-error treatment (vcov = "iid") or adding a regressor is a one-argument change.

Where to go next

You have now run the complete tidyfinance workflow: download, construct and lag the signal, align it with returns, sort, customize, and test. Every stage exposed its decisions as arguments with sensible defaults, so the baseline reproduces standard practice and any variation is a small edit.

To reproduce the published asset growth premium, replace "Pseudo Data" with "WRDS" in the two download_data() calls and run set_wrds_credentials() once beforehand; nothing else in the pipeline changes. For the underlying methods and the empirical results on real data, see the book by Scheuch, Voigt, and Weiss (2023), and consult ?download_data, ?implement_portfolio_sort, and ?estimate_fama_macbeth for the full set of options at each stage.

References

Cooper, Michael J., Huseyin Gulen, and Michael J. Schill. 2008. “Asset Growth and the Cross-Section of Stock Returns.” The Journal of Finance 63 (4): 1609–1651.

Fama, Eugene F., and Kenneth R. French. 1993. “Common Risk Factors in the Returns on Stocks and Bonds.” Journal of Financial Economics 33 (1): 3–56.

Fama, Eugene F., and James D. MacBeth. 1973. “Risk, Return, and Equilibrium: Empirical Tests.” Journal of Political Economy 81 (3): 607–636.

Newey, Whitney K., and Kenneth D. West. 1987. “A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix.” Econometrica 55 (3): 703–708.

Scheuch, Christoph, Stefan Voigt, and Patrick Weiss. 2023. Tidy Finance with R. Boca Raton, FL: Chapman & Hall/CRC. https://www.tidy-finance.org.