In bautheac/finRes: Data-science and research in finance and financial economics in R

knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
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download.file("https://www.dropbox.com/s/htnd7o9nnkk8ng8/references.bib?dl=1", paste(folder, "references.bib", sep = "/"))

finRes is home to a number of packages that, although self-contained with consumption value on their own, host datasets that play important roles in the finRes suite, mostly in relation to data collection, storage and wrangling but also to analytics and asset pricing in particular. At the time of writing, the set of dataset packages in finRes includes: BBGsymbols, fewISOs, GICS, FFresearch and factors.

BBGsymbols

The BBGsymbols package plays a critical role in finRes where it provides both the pullit and the storethat packages with support for interacting with Bloomberg through the Rblpapi interface [@Armstrong_Rblpapi].

library(BBGsymbols)

data(list = c("fields", "months", "rolls", "tickers_cftc", "tickers_futures"), package = "BBGsymbols")

fields

The fields dataset is the workhorse in BBGsymbols; it gathers Bloomberg datafields that have been carefully selected over time through experience. It provides popular historical and contemporaneous data fields that are likely to provide the necessary information and beyond for any rigorous research or more applied work in finance and financial economics. Financial instruments covered at the time of writing include 'equity', referring to any equity like security, 'fund' encompassing any money managing entity and 'futures' covering the futures markets. The author welcomes pull requests that could help expanding the current coverage.

# data.table::as.data.table(fields)
tibble::as_tibble(fields)
# head(fields)

months

The months dataset details the symbols used to refer to calendar months in Bloomberg and financial markets in general. It is particularly useful when working with financial derivatives such as futures contracts.

months

rolls

The rolls dataset details the symbols used to refer to the various roll types and adjustments available in Bloomberg when working with futures term structure contracts. These symbols can be used to construct bespoke tickers that allow the user to query Bloomberg for futures term structure data with the desired roll characteristics.

rolls

tickers_CFTC

The tickers_cftc dataset gathers Bloomberg position data tickers for a number of futures series. These tickers allow direct retrieval from Bloomberg via pullit of corresponding position data as reported by the US Commodity Futures Trading Commission (CFTC) in a collection of weekly market reports including the 'legacy', 'disaggregated', 'supplemental' and 'traders in financial futures' (TFF) reports. See ?tickers_CFTC for details.

tickers_cftc

tickers_futures

The tickers_futures dataset gathers futures active contract Bloomberg tickers as well as a collection of qualitative information for several popular futures series including commodity, currency, financial and index futures with underlyings from various asset classes.

# data.table::as.data.table(tickers_futures)
tibble::as_tibble(tickers_futures)
# head(tickers_futures)

fewISOs

fewISOs provides a collection of financial economics related ISO code datasets conveniently packaged for consumption in R. Beyond their self-contained consumption value these datasets belong to finRes where they help with data wrangling and exploration. At the time of writing fewISOs hosts the countries, currencies and exchanges datasets.

library(fewISOs)

data(list = c("countries", "currencies", "exchanges"), package = "fewISOs")

countries

The countries dataset corresponds to the ISO 3166-1 sub-standard, part of the ISO 3166 standard published by the International Organization for Standardization (ISO) that defines codes for the names of countries, dependent territories, special areas of geographical interest, and their principal subdivisions (e.g., provinces or states). The sub-standard comes in three sets of country codes, all provided in the dataset:

• ISO 3166-1 alpha-2: two-letter country codes (most widely used).
• ISO 3166-1 alpha-3: three-letter country codes. Allows for a better visual association between the codes and the country names than the alpha-2 codes.
• ISO 3166-1 numeric: three-digit country codes. These are identical to those developed and maintained by the United Nations Statistics Division, with the advantage of script (writing system) independence, and hence useful for people or systems using non-Latin scripts.

countries

currencies

The currencies dataset corresponds to the ISO 4217 standard that defines codes for worldwide currencies and comes as a three-letter alphabetic as well as an alternative three-digit numeric code, both provided in the dataset. The ISO 4217 three-letter alphabetic code standard is based on the ISO 3166-1 code standard for countries with the first two letters corresponding the ISO 3166-1 alpha-2 code for the country issuing the corresponding currency and the third corresponding to the first letter of the currency name when possible. The three-digit numeric code is the same as the ISO 3166-1 numeric code for the issuing country when possible.

currencies

exchanges

The exchanges dataset corresponds to the ISO 10383 standard that defines four alphanumeric character Market Identifier Codes (MIC). These are unique identification codes used to identify securities trading exchanges, trading platforms and regulated or non-regulated markets as sources of prices and related information in order to facilitate automated processing.

exchanges

GICS

GICS packages the Global Industry Classification Standard (GICS) dataset for consumption in R. Beyond its self-contained consumption value GICS belongs to finRes where, along with BBGsymbols and fewISOs, it helps with data wrangling and exploration.

library(GICS)

data(list = c("standards"), package = "GICS")

standards

The GICS is a standardized classification system for equities developed jointly by Morgan Stanley Capital International (MSCI) and Standard & Poor's. The GICS methodology is used by the MSCI indexes, which include domestic and international stocks, as well as by a large portion of the professional investment management community. The GICS hierarchy begins with 11 sectors and is followed by 24 industry groups, 68 industries, and 157 sub-industries. Each stock that is classified will have a coding at all four of these levels with all these provided in the standards dataset.

standards

FFresearch

FFresearch conveniently packages Fama/French asset pricing research data for consumption in R. The data is pulled directly from Kenneth French's online data library.

library(FFresearch)

data(list = c("factors", "portfolios_univariate", "portfolios_bivariate", "portfolios_trivariate",
              "portfolios_industries", "variables", "breakpoints"), package = "FFresearch")

portfolios

univariate

The portfolios_univariate dataset provides various feature time series for Fama/French portfolios formed on single variable sorts. Sorting variables include size, book-to-market, operating profitability and investment.

portfolios_univariate

bivariate

The portfolios_bivariate dataset provides various feature time series for Fama/French portfolios formed on two variable sorts. Sorting variables include size, book-to-market, operating profitability and investment.

portfolios_bivariate

trivariate

The portfolios_trivariate dataset provides various feature time series for Fama/French portfolios formed on three variable sorts. Sorting variables include size, book-to-market, operating profitability and investment.

portfolios_trivariate

industries

The portfolios_industries dataset provides various feature time series for Fama/French industry portfolios [@fama_industry_1997].

portfolios_industries

factors

The factors dataset provides the return (factors) and level (risk free rate) time series for the classic Fama/French asset pricing factors as used in their three [@fama_cross_section_1992; @fama_common_1993; @fama_size_1995] and most recently five-factor [@fama_five_factor_2015; @fama_dissecting_2016; @fama_international_2017] asset pricing models very popular to the asset pricing enthusiasts.

factors

variables

The variables dataset is a helper dataset that provides details, including construction methods, for the variables used to construct the portfolios and asset pricing factors above.

variables

breakpoints

Finally, the breakpoints dataset is a helper dataset that provides the times series for the variables breakpoints used to construct the variables that in turn allow the construction of the portfolios and asset pricing factors above-mentioned.

breakpoints

factors

The factors package gathers various asset pricing research factor time series for convenient consumption in R with the data directly pulled from the authors' website. The current version includes the factor data from Kenneth's French, also available in the FFresearch package described above, as well as factor data from Robert F. Stambaugh.

library(factors)

data(list = c("fama_french", "stambaugh"), package = "factors")

Fama & French

The fama_french dataset provides the return (factors) and level (risk free rate) time series for the classic Fama/French asset pricing factors as used in their three [@fama_cross_section_1992; @fama_common_1993; @fama_size_1995] and most recently five-factor [@fama_five_factor_2015; @fama_dissecting_2016; @fama_international_2017] asset pricing models very popular to the asset pricing enthusiasts:

fama_french

Stambaugh et al

The stambaugh dataset provides the return (factors) and level (risk free rate) time series for various research asset pricing factors put together by Robert F. Stambaugh and collaborators including Lubos Pastor and Yu Yuan. The factors include traded & non-traded liquidity [@pastor_liquidity_2003], as well as market, size and two 'mispricing' factors: management & performance [@stambaugh_mispricing_2016]:

stambaugh

references

bautheac/finRes documentation built on June 9, 2021, 12:22 a.m.