2 Accessing & managing financial data

In this chapter, we suggest a way to organize your financial data. Everybody, who has experience with data, is also familiar with storing data in various formats like CSV, XLS, XLSX, or other delimited value storage. Reading and saving data can become very cumbersome in the case of using different data formats, both across different projects and across different programming languages. Moreover, storing data in delimited files often leads to problems with respect to column type consistency. For instance, date-type columns frequently lead to inconsistencies across different data formats and programming languages.

This chapter shows how to import different open source data sets. Specifically, our data comes from the application programming interface (API) of Yahoo!Finance, a downloaded standard CSV file, an XLSX file stored in a public Google Drive repository, and other macroeconomic time series. We store all the data in a single database, which serves as the only source of data in subsequent chapters. We conclude the chapter by providing some tips on managing databases.

First, we load the global packages that we use throughout this chapter. Later on, we load more packages in the sections where we need them.

The package lubridate provides convenient tools to work with dates and times (Grolemund and Wickham 2011). The package scales (Wickham and Seidel 2022) provides useful scale functions for visualizations.

Moreover, we initially define the date range for which we fetch and store the financial data, making future data updates tractable. In case you need another time frame, you can adjust the dates below. Our data starts with 1960 since most asset pricing studies use data from 1962 on.

start_date <- ymd("1960-01-01")
end_date <- ymd("2021-12-31")

2.1 Fama-French data

We start by downloading some famous Fama-French factors (e.g., Fama and French 1993) and portfolio returns commonly used in empirical asset pricing. Fortunately, there is a neat package by Nelson Areal that allows us to access the data easily: the frenchdata package provides functions to download and read data sets from Prof. Kenneth French finance data library (Areal 2021).

We can use the main function of the package to download monthly Fama-French factors. The set 3 Factors includes the return time series of the market, size, and value factors alongside the risk-free rates. Note that we have to do some manual work to correctly parse all the columns and scale them appropriately, as the raw Fama-French data comes in a very unpractical data format. For precise descriptions of the variables, we suggest consulting Prof. Kenneth French’s finance data library directly. If you are on the site, check the raw data files to appreciate the time you can save thanks to frenchdata.

factors_ff_monthly_raw <- download_french_data("Fama/French 3 Factors")
factors_ff_monthly <- factors_ff_monthly_raw$subsets$data[[1]] |>
  transmute(
    month = floor_date(ymd(str_c(date, "01")), "month"),
    rf = as.numeric(RF) / 100,
    mkt_excess = as.numeric(`Mkt-RF`) / 100,
    smb = as.numeric(SMB) / 100,
    hml = as.numeric(HML) / 100
  ) |>
  filter(month >= start_date & month <= end_date)

It is straightforward to download the corresponding daily Fama-French factors with the same function.

factors_ff_daily_raw <- download_french_data("Fama/French 3 Factors [Daily]")
factors_ff_daily <- factors_ff_daily_raw$subsets$data[[1]] |>
  transmute(
    date = ymd(date),
    rf = as.numeric(RF) / 100,
    mkt_excess = as.numeric(`Mkt-RF`) / 100,
    smb = as.numeric(SMB) / 100,
    hml = as.numeric(HML) / 100
  ) |>
  filter(date >= start_date & date <= end_date)

In a subsequent chapter, we also use the 10 monthly industry portfolios, so let us fetch that data, too.

industries_ff_monthly_raw <- download_french_data("10 Industry Portfolios")
industries_ff_monthly <- industries_ff_monthly_raw$subsets$data[[1]] |>
  mutate(month = floor_date(ymd(str_c(date, "01")), "month")) |>
  mutate(across(where(is.numeric), ~ . / 100)) |>
  select(month, everything(), -date) |>
  filter(month >= start_date & month <= end_date)

It is worth taking a look at all available portfolio return time series from Kenneth French’s homepage. You should check out the other sets by calling get_french_data_list().

2.2 q-factors

In recent years, the academic discourse experienced the rise of alternative factor models, e.g., in the form of the Hou, Xue, and Zhang (2014) q-factor model. We refer to the extended background information provided by the original authors for further information. The q factors can be downloaded directly from the authors’ homepage from within read_csv().

We also need to adjust this data. First, we discard information we will not use in the remainder of the book. Then, we rename the columns with the “R_”-prescript using regular expressions and write all column names in lowercase. You should always try sticking to a consistent style for naming objects, which we try to illustrate here - the emphasis is on try. You can check out style guides available online, e.g., Hadley Wickham’s tidyverse style guide.

factors_q_monthly_link <-
  "http://global-q.org/uploads/1/2/2/6/122679606/q5_factors_monthly_2021.csv"

factors_q_monthly <- read_csv(factors_q_monthly_link) |>
  mutate(month = ymd(str_c(year, month, "01", sep = "-"))) |>
  select(-R_F, -R_MKT, -year) |>
  rename_with(~ str_remove(., "R_")) |>
  rename_with(~ str_to_lower(.)) |>
  mutate(across(-month, ~ . / 100)) |>
  filter(month >= start_date & month <= end_date)

2.3 Macroeconomic predictors

Our next data source is a set of macroeconomic variables often used as predictors for the equity premium. Welch and Goyal (2008) comprehensively reexamine the performance of variables suggested by the academic literature to be good predictors of the equity premium. The authors host the data updated to 2021 on Amit Goyal’s website. Since the data is an XLSX-file stored on a public Google drive location, we need additional packages to access the data directly from our R session. Therefore, we load readxl to read the XLSX-file (Wickham and Bryan 2022) and googledrive for the Google drive connection (D’Agostino McGowan and Bryan 2021).

Usually, you need to authenticate if you interact with Google drive directly in R. Since the data is stored via a public link, we can proceed without any authentication.

The drive_download() function from the googledrive package allows us to download the data and store it locally.

macro_predictors_link <-
  "https://docs.google.com/spreadsheets/d/1OArfD2Wv9IvGoLkJ8JyoXS0YMQLDZfY2"

drive_download(
  macro_predictors_link,
  path = "data/macro_predictors.xlsx"
)

Next, we read in the new data and transform the columns into the variables that we later use:

  1. The dividend price ratio (dp), the difference between the log of dividends and the log of prices, where dividends are 12-month moving sums of dividends paid on the S&P 500 index, and prices are monthly averages of daily closing prices (Campbell and Shiller 1988; Campbell and Yogo 2006).
  2. Dividend yield (dy), the difference between the log of dividends and the log of lagged prices (Ball 1978).
  3. Earnings price ratio (ep), the difference between the log of earnings and the log of prices, where earnings are 12-month moving sums of earnings on the S&P 500 index (Campbell and Shiller 1988).
  4. Dividend payout ratio (de), the difference between the log of dividends and the log of earnings (Lamont 1998).
  5. Stock variance (svar), the sum of squared daily returns on the S&P 500 index (Guo 2006).
  6. Book-to-market ratio (bm), the ratio of book value to market value for the Dow Jones Industrial Average (Kothari and Shanken 1997)
  7. Net equity expansion (ntis), the ratio of 12-month moving sums of net issues by NYSE listed stocks divided by the total end-of-year market capitalization of NYSE stocks (Campbell, Hilscher, and Szilagyi 2008).
  8. Treasury bills (tbl), the 3-Month Treasury Bill: Secondary Market Rate from the economic research database at the Federal Reserve Bank at St. Louis (Campbell 1987).
  9. Long-term yield (lty), the long-term government bond yield from Ibbotson’s Stocks, Bonds, Bills, and Inflation Yearbook (Welch and Goyal 2008).
  10. Long-term rate of returns (ltr), the long-term government bond returns from Ibbotson’s Stocks, Bonds, Bills, and Inflation Yearbook (Welch and Goyal 2008).
  11. Term spread (tms), the difference between the long-term yield on government bonds and the Treasury bill (Campbell 1987).
  12. Default yield spread (dfy), the difference between BAA and AAA-rated corporate bond yields (Fama and French 1989).
  13. Inflation (infl), the Consumer Price Index (All Urban Consumers) from the Bureau of Labor Statistics (Campbell and Vuolteenaho 2004).

For variable definitions and the required data transformations, you can consult the material on Amit Goyal’s website.

macro_predictors <- read_xlsx(
  "data/macro_predictors.xlsx",
  sheet = "Monthly"
) |>
  mutate(month = ym(yyyymm)) |>
  mutate(across(where(is.character), as.numeric)) |>
  mutate(
    IndexDiv = Index + D12,
    logret = log(IndexDiv) - log(lag(IndexDiv)),
    Rfree = log(Rfree + 1),
    rp_div = lead(logret - Rfree, 1), # Future excess market return
    dp = log(D12) - log(Index), # Dividend Price ratio
    dy = log(D12) - log(lag(Index)), # Dividend yield
    ep = log(E12) - log(Index), # Earnings price ratio
    de = log(D12) - log(E12), # Dividend payout ratio
    tms = lty - tbl, # Term spread
    dfy = BAA - AAA # Default yield spread
  ) |>
  select(month, rp_div, dp, dy, ep, de, svar,
    bm = `b/m`, ntis, tbl, lty, ltr,
    tms, dfy, infl
  ) |>
  filter(month >= start_date & month <= end_date) |>
  drop_na()

Finally, after reading in the macro predictors to our memory, we remove the raw data file from our temporary storage.

file.remove("data/macro_predictors.xlsx")
[1] TRUE

2.4 Other macroeconomic data

The Federal Reserve bank of St. Louis provides the Federal Reserve Economic Data (FRED), an extensive database for macroeconomic data. In total, there are 817,000 US and international time series from 108 different sources. As an illustration, we use the already familiar tidyquant package to fetch consumer price index (CPI) data that can be found under the CPIAUCNS key.

library(tidyquant)

cpi_monthly <- tq_get("CPIAUCNS",
  get = "economic.data",
  from = start_date,
  to = end_date
) |>
  transmute(
    month = floor_date(date, "month"),
    cpi = price / price[month == max(month)]
  )

To download other time series, we just have to look it up on the FRED website and extract the corresponding key from the address. For instance, the producer price index for gold ores can be found under the PCU2122212122210 key. The tidyquant package provides access to around 10,000 time series of the FRED database. If your desired time series is not included, we recommend working with the fredr package (Boysel and Vaughan 2021). Note that you need to get an API key to use its functionality. We refer to the package documentation for details.

2.5 Setting up a database

Now that we have downloaded some (freely available) data from the web into the memory of our R session let us set up a database to store that information for future use. We will use the data stored in this database throughout the following chapters, but you could alternatively implement a different strategy and replace the respective code.

There are many ways to set up and organize a database, depending on the use case. For our purpose, the most efficient way is to use an SQLite database, which is the C-language library that implements a small, fast, self-contained, high-reliability, full-featured, SQL database engine. Note that SQL (Structured Query Language) is a standard language for accessing and manipulating databases and heavily inspired the dplyr functions. We refer to this tutorial for more information on SQL.

There are two packages that make working with SQLite in R very simple: RSQLite (Müller et al. 2022) embeds the SQLite database engine in R, and dbplyr (Wickham, Girlich, and Ruiz 2022) is the database back-end for dplyr. These packages allow to set up a database to remotely store tables and use these remote database tables as if they are in-memory data frames by automatically converting dplyr into SQL. Check out the RSQLite and dbplyr vignettes for more information.

An SQLite database is easily created - the code below is really all there is. You do not need any external software. Note that we use the extended_types=TRUE option to enable date types when storing and fetching data. Otherwise, date columns are stored and retrieved as integers. We will use the resulting file tidy_finance.sqlite in the subfolder data for all subsequent chapters to retrieve our data.

tidy_finance <- dbConnect(
  SQLite(),
  "data/tidy_finance.sqlite",
  extended_types = TRUE
)

Next, we create a remote table with the monthly Fama-French factor data. We do so with the function dbWriteTable(), which copies the data to our SQLite-database.

  dbWriteTable(tidy_finance,
    "factors_ff_monthly",
    value = factors_ff_monthly,
    overwrite = TRUE
  )

We can use the remote table as an in-memory data frame by building a connection via tbl().

factors_ff_monthly_db <- tbl(tidy_finance, "factors_ff_monthly")

All dplyr calls are evaluated lazily, i.e., the data is not in our R session’s memory, and the database does most of the work. You can see that by noticing that the output below does not show the number of rows. In fact, the following code chunk only fetches the top 10 rows from the database for printing.

factors_ff_monthly_db |>
  select(month, rf)
# Source:   SQL [?? x 2]
# Database: sqlite 3.39.3 [data/tidy_finance.sqlite]
  month          rf
  <date>      <dbl>
1 1960-01-01 0.0033
2 1960-02-01 0.0029
3 1960-03-01 0.0035
4 1960-04-01 0.0019
5 1960-05-01 0.0027
# … with more rows

If we want to have the whole table in memory, we need to collect() it. You will see that we regularly load the data into the memory in the next chapters.

factors_ff_monthly_db |>
  select(month, rf) |>
  collect()
# A tibble: 744 × 2
  month          rf
  <date>      <dbl>
1 1960-01-01 0.0033
2 1960-02-01 0.0029
3 1960-03-01 0.0035
4 1960-04-01 0.0019
5 1960-05-01 0.0027
# … with 739 more rows

The last couple of code chunks is really all there is to organizing a simple database! You can also share the SQLite database across devices and programming languages.

Before we move on to the next data source, let us also store the other five tables in our new SQLite database.

  dbWriteTable(tidy_finance,
    "factors_ff_daily",
    value = factors_ff_daily,
    overwrite = TRUE
  )

  dbWriteTable(tidy_finance,
    "industries_ff_monthly",
    value = industries_ff_monthly,
    overwrite = TRUE
  )

  dbWriteTable(tidy_finance,
    "factors_q_monthly",
    value = factors_q_monthly,
    overwrite = TRUE
  )

  dbWriteTable(tidy_finance,
    "macro_predictors",
    value = macro_predictors,
    overwrite = TRUE
  )

  dbWriteTable(tidy_finance,
    "cpi_monthly",
    value = cpi_monthly,
    overwrite = TRUE
  )

From now on, all you need to do to access data that is stored in the database is to follow three steps: (i) Establish the connection to the SQLite database, (ii) call the table you want to extract, and (iii) collect the data. For your convenience, the following steps show all you need in a compact fashion.

library(tidyverse)
library(RSQLite)

tidy_finance <- dbConnect(
  SQLite(),
  "data/tidy_finance.sqlite",
  extended_types = TRUE
)

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

2.6 Managing SQLite databases

Finally, at the end of our data chapter, we revisit the SQLite database itself. When you drop database objects such as tables or delete data from tables, the database file size remains unchanged because SQLite just marks the deleted objects as free and reserves their space for future uses. As a result, the database file always grows in size.

To optimize the database file, you can run the VACUUM command in the database, which rebuilds the database and frees up unused space. You can execute the command in the database using the dbSendQuery() function.

dbSendQuery(tidy_finance, "VACUUM")
<SQLiteResult>
  SQL  VACUUM
  ROWS Fetched: 0 [complete]
       Changed: 0

The VACUUM command actually performs a couple of additional cleaning steps, which you can read up in this tutorial.

Apart from cleaning up, you might be interested in listing all the tables that are currently in your database. You can do this via the dbListTables() function.

dbListTables(tidy_finance)
Warning: Closing open result set, pending rows
 [1] "beta"                  "compustat"            
 [3] "cpi_monthly"           "crsp_daily"           
 [5] "crsp_monthly"          "factors_ff_daily"     
 [7] "factors_ff_monthly"    "factors_q_monthly"    
 [9] "industries_ff_monthly" "macro_predictors"     
[11] "mergent"               "trace_enhanced"       

This function comes in handy if you are unsure about the correct naming of the tables in your database.

2.7 Exercises

  1. Download the monthly Fama-French factors manually from Ken French’s data library and read them in via read_csv(). Validate that you get the same data as via the frenchdata package.
  2. Download the Fama-French 5 factors using the frenchdata package. Use get_french_data_list() to find the corresponding table name. After the successful download and conversion to the column format that we used above, compare the resulting rf, mkt_excess, smb, and hml columns to factors_ff_monthly. Explain any differences you might find.