Downloading WRDS Data

list_libraries(conn)

Load the list of Postgres schemata the user has permission to access

list_tables(conn, library::String)

List all of the views/tables/foreign tables within a schema

describe_table(conn, library::String, table::String)

Get a table's description (row count, columns, column types)

get_table(
                conn,
                library::String,
                table::String;
                obs::Union{Nothing, Int} = nothing,
                offset::Int = 0,
                cols = nothing
            )

Create a DataFrame from a table

raw_sql(
    conn,
    query::String
)

Executes raw sql code, and converts code to a DataFrame

function comp_data(
    conn[,
    gvkeys::AbstractArray{String},]
    dateStart::Union{Date,Int}=1950,
    dateEnd::Union{Date,Int}=Dates.today();
    annual::Bool=true,
    filters::Dict{String,<:Any}=Dict(
        "datafmt" => "STD",
        "indfmt" => "INDL",
        "consol" => "C",
        "popsrc" => "D"
    ),
    cols::Array{String}=["gvkey", "datadate", "fyear", "sale", "revt", "xopr"]
)

Downloads data from Compustat for firms (if list of gvkeys is provided, filters to those firms) available firms over a period. Data can be annual (set annual=true) or quarterly (set annual=false). For quarterly data, you also likely need to change the columns that are downloaded (ie, sales is "saleq" in quarterly data).

Filters is a dictionary of String => String (or array of String) pairings that will be applied to the SQL query.

function crsp_stocknames(
    conn;
    cols::Array{String}=["permno", "cusip", "ncusip", "comnam", "namedt", "nameenddt", "ticker"]
)

function crsp_stocknames(
    conn,
    cusip::Array{String};
    cols::Array{String}=["permno", "cusip", "ncusip", "comnam", "namedt", "nameenddt", "ticker"],
    cusip_col="cusip", # either "cusip" "ncusip" or "ticker"
)

function crsp_stocknames(
    conn,
    permno::Array{<:Number};
    cols::Array{String}=["permno", "cusip", "ncusip", "comnam", "namedt", "nameenddt", "ticker"],
)

Download crsp.stockname data (with non-missing ncusip). If an array of strings is passed, by default assumes it is a list of cusips, can be a list of ncusip or tickers (change cusip_col). Can also take an array of numbers which is assumed to be a list of permnos.

function crsp_market(
    conn,
    dateStart::Union{Date,Int}=1950,
    dateEnd::Union{Date,Int}=Dates.today();
    col::Union{String, Array{String}}="vwretd"
)

Downloads the data from the daily or monthly stock index file (dsi and msi) for a range of dates with one value for each day (with various return columns). Available columns are:

• "vwretd": Value weighted return with dividends
• "vwretx": Value weighted return without dividends
• "ewretd": Equal weighted return with dividends
• "ewretx": Equal weighted return without dividends
• "sprtrn": S&P500 return

function crsp_data(
    conn,
    [permnos::Vector{<:Real},]
    s::Date=Date(1925),
    e::Date=today();
    cols = ["ret", "vol", "shrout"],
    filters::Dict{String, <:Any}=Dict{String, Any}(),
    adjust_crsp_data::Bool=true
)

function crsp_data(
    conn,
    permnos::Vector{<:Real},
    dates::Vector{Date},
    [dates_end::Vector{Date}];
    cols=["ret", "vol", "shrout"],
    adjust_crsp_data::Bool=true,
    query_size_limit::Int=3000
)

Downloads data from the crsp stockfiles, which are individual stocks. To download the data from the monthly stockfile, change the default table to the monthly stockfile:

WRDSMerger.default_tables["crsp_stock_data"] = "crsp.msf"
WRDSMerger.default_tables["crsp_index"] = "crsp.msi"
WRDSMerger.default_tables["crsp_delist"] = "crsp.msedelist"

Arguments

• permnos::Vecotr{<:Real}: A vector of firm IDs, if provided, will only download data for those firms
• s::Date=Date(1925) and e::Date=today(): Downloads all data between two dates provided
• dates::Vector{Date}: Downloads data for a set of permnos on the date provided
  • dates_end::Vector{Date}: If provided, then treats the dates as the start of a period and will download data for the permnos between the two dates
• adjust_crsp_data::Bool=true: This will call crsp_adjust with all options set to true, it will only do the operations that it has the data for.

function crsp_delist(
    conn;
    cols::Array{String}=[
        "permno",
        "dlstdt",
        "dlret"
    ],
    date_start::Date=Date(1926),
    date_end::Date=today()
)

Fetches the CRSP delist dataset, typically for the returns on the day of delisting.

function crsp_adjust(
    conn,
    df::DataFrame;
    kwargs...
)

This makes 4 common adjustments to CRSP data:

1. When the price in CRSP is negative, that means there was not a specific close price. Typically, researchers take the absolute value to get the actual price.
2. Prices are not adjusted for splits (returns are). CRSP includes the number (cfascpr) that will adjust prices to be comparable through time
3. Similar to prices, shares outstanding are not adjusted. The number used to adjust is different due to various events.
4. Prices are not adjusted for delisting, so this downloads the necessary dataset and adjusts returns accordingly.

Arguments

Options on what to adjust

• adjustprcnegatives::Bool=true: Corresponds to (1) above
• adjustprcsplits::Bool=true: Corresponds to (2) above
• adjustshrsplits::Bool=true: Corresponds to (3) above
• adjust_delist::Bool=true: Corresponds to (4) above

Pre-existing column names

• date::String="date": date column
• idcol::String="permno": primary identifier (permno) column
• prc_col::String="prc": price column
• ret_col::String="ret": return column
• prcsplitscol::String="cfacpr": price split adjustment factor
• shrout_col::String="shrout": shares outstanding
• shroutsplitscol::String="cfacshr": share split adjustment factor

Options to relabel columns

• adjustednegprc_col::String="prc": relabel prices to this after taking absolute value
• adjustedprccol::String="prc_adj": relabel prices to this after adjusting for splits
• adjustedshroutcol::String="shrout_adj":relabel shares outstanding to this after adjusting for splits

ff_data(conn, date_start=Date(1926,7,1), date_end=today(); cols=["date","mktrf","smb","hml","rf","umd"])

Download Fama-French factor data from the ff.factors_daily table (configurable via default_tables["ff_factors"]). Returns a DataFrame with the requested columns over the given date range.

Available columns include mktrf, smb, hml, rf, and umd (momentum).