Download and Process Global Q Factor Data

Downloads and processes Global Q factor data based on the specified dataset, date range, and source URL. The processing includes date conversion, renaming variables to a standardized format, scaling factor values, and filtering by the specified date range.

Usage

download_data_factors_q(
  dataset = NULL,
  start_date = NULL,
  end_date = NULL,
  type = deprecated(),
  url = "https://global-q.org/uploads/1/2/2/6/122679606/"
)

Arguments

dataset

The name of the dataset to download (e.g., "q5_factors_daily_2023.csv", "q5_factors_monthly_2023.csv").

start_date

Optional. A character string or Date object in "YYYY-MM-DD" format specifying the start date for the data. If not provided, the full dataset is returned.

end_date

Optional. A character string or Date object in "YYYY-MM-DD" format specifying the end date for the data. If not provided, the full dataset is returned.

type

Use dataset instead.

url

The base URL from which to download the dataset files.

Value

A tibble with processed factor data, including the date, risk-free rate, market excess return, and other factors, filtered by the specified date range.

References

Hou, K., Xue, C., & Zhang, L. (2015). Digesting anomalies: An investment approach. Review of Financial Studies, 28(3), 650-705. doi:10.1093/rfs/hhu068

Hou, K., Mo, H., Xue, C., & Zhang, L. (2019). Which factors? Review of Finance, 23(1), 1-35. doi:10.1093/rof/rfy032

See also

Other download functions: download_data(), download_data_constituents(), download_data_factors_ff(), download_data_fred(), download_data_fred_md(), download_data_huggingface(), download_data_jkp(), download_data_macro_predictors(), download_data_osap(), download_data_pastor_stambaugh(), download_data_risk_free(), download_data_stambaugh_yuan(), download_data_stock_prices(), download_factor_library_grid(), download_factor_library_ids()

Examples

# \donttest{
download_data_factors_q("q5_factors_daily_2024", "2020-01-01", "2020-12-31")
#> # A tibble: 253 × 7
#>    date       risk_free mkt_excess       me        ia       roe        eg
#>    <date>         <dbl>      <dbl>    <dbl>     <dbl>     <dbl>     <dbl>
#>  1 2020-01-02  0.000055   0.00863  -0.0112  -0.00173   0.000711  0.00340 
#>  2 2020-01-03  0.000055  -0.00673   0.00235 -0.00190  -0.00157   0.000749
#>  3 2020-01-06  0.000055   0.00360  -0.00358 -0.00407  -0.00481   0.000426
#>  4 2020-01-07  0.000055  -0.00192  -0.00138 -0.00322  -0.00512  -0.00224 
#>  5 2020-01-08  0.000055   0.00467  -0.00107 -0.00118   0.00453   0.00635 
#>  6 2020-01-09  0.000055   0.00649  -0.00684 -0.000656  0.00295   0.00510 
#>  7 2020-01-10  0.000055  -0.00335  -0.00237 -0.00206   0.000406  0.00323 
#>  8 2020-01-13  0.000055   0.00730  -0.00200  0.00208   0.00307   0.000787
#>  9 2020-01-14  0.000055  -0.000568  0.00456 -0.00138  -0.00965  -0.00680 
#> 10 2020-01-15  0.000055   0.00164   0.00304 -0.00268  -0.00108   0.00104 
#> # ℹ 243 more rows
download_data_factors_q("q5_factors_annual_2024")
#> No `start_date` or `end_date` provided. Returning the full data set.
#> # A tibble: 58 × 8
#>     date risk_free mkt_excess  year      me       ia      roe      eg
#>    <int>     <dbl>      <dbl> <dbl>   <dbl>    <dbl>    <dbl>   <dbl>
#>  1  1967    0.0415     0.246   19.7  0.406  -0.120    0.202   -0.0366
#>  2  1968    0.0529     0.0882  19.7  0.245   0.151   -0.0205   0.118 
#>  3  1969    0.0659    -0.175   19.7 -0.118   0.00561  0.152    0.128 
#>  4  1970    0.0638    -0.0643  19.7 -0.0837  0.235   -0.00461  0.193 
#>  5  1971    0.0432     0.118   19.7  0.0577  0.0118   0.120    0.0566
#>  6  1972    0.0389     0.129   19.7 -0.0995  0.0454   0.0513   0.149 
#>  7  1973    0.0706    -0.263   19.7 -0.176   0.0801   0.0110   0.173 
#>  8  1974    0.0808    -0.358   19.7  0.0460  0.192    0.117    0.201 
#>  9  1975    0.0582     0.324   19.8  0.172   0.0751  -0.0594   0.112 
#> 10  1976    0.0516     0.218   19.8  0.225   0.0463  -0.0304   0.112 
#> # ℹ 48 more rows
# }