ExtractFormulaHistory
The ExtractFormulaHistory factlet is used for extracting one or more items for one
security, an index or a list of securities over time. The factlet is using the FactSet Query
Language (FQL), which is a proprietary data retrieval language used to access a timeseries of FactSet data.
1. FactSet Query Language
Some advantages of FQL include:

The ability to specify dates for any database using the same formats.
With FQL, date formats are flexible. You can use a number of consistent date
formats (defined by FQL) for all databases which makes using and combining
data from different databases easier than ever.

The ability to iterate items, formulas, and functions at any frequency.
With FQL, you can iterate items, formulas, and functions at any frequency. For
example, you can request a series of weekly price to earnings ratios.
To request a time-series of data, a start date, end date and frequency needs to be
specified. If a date is not specified, data is returned from the most recent time period. To
request data for a time series, the start date is the first date of the requested time series
of data. The end date is the last date of the time series. The dates can be designated as
absolute dates or relative dates.
1.1. FQL Date Format - Absolute Dates
Absolute dates indicate a specific day, month-end, fiscal quarter-end, calendar quarterend, fiscal year-end, or calendar year-end as depicted in the examples below:





A day: MM/DD/YYYY (e.g. 7/11/1999)
Note: DD/MM/YYYY is not a valid date format
A month-end: MM/YYYY (e.g. 6/1999)
A fiscal quarter-end: YY/FQ or YYYY/FQ (e.g. 1999/1F, 2000/3F,
2001/2F)
A calendar quarter-end: YY/CQ or YYYY/CQ (e.g. 1999/1C, 00/3C,
2001/1C)
A fiscal year-end: YY or YYYY (e.g. 2000, 01, 1999)
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1.2. FQL Date Format - Relative Dates
Relative dates represent a date relative to the most recently updated period. For
example, 0 (zero) represents the most recently updated period; -1 represents the time
period prior to the most recently updated.
The zero date is determined by the default time period or the natural frequency of the
data being requested. Zero (0) when used with monthly data indicates the most recent
month end. Negative one (-1) when used with annual data indicates one fiscal year prior
to the most recently updated fiscal year.
List of Relative Date Arguments:
D
0D is the most recent trading day, -1D is one trading day prior.
AW
0AW is the most recent trading day, -1AW is the one actual week (7 days) prior
to the most recent trading day.
W
0W is the last day of the most recent trading week (usually Friday), -1W is the
last trading day of the prior week.
AM
0AM is the most recent trading day, -1AM is the same day, one actual month
prior.
M
0M is the last trading day of the most recent month, -1M is the last trading day of
the prior month.
AQ
0AQ is the most recent trading day, -1AQ is the same day 3 months prior
Q
0Q is the last trading day of the company’s most recent fiscal quarter, -1Q is the
last day of the prior fiscal quarter.
CQ
0CQ is the last trading day of the most recent calendar quarter (March, June,
September, or December), -1CQ is the last trading day of the prior calendar
quarter.
AY
0AY is the most recent trading day, -1AY is one actual year (365 days) prior
Y
0Y is the last trading day of the company’s most recent fiscal year, -1Y is the last
trading day of the prior fiscal year.
CY
0CY is the last trading day of the most recent calendar year (the last trading day
in December), -1CY is the last trading day of the prior calendar year.
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For databases available on FactSet, there are a set of items that represent the piece of
information on the database, such as sales, EPS and market value. Formulas that begin
with FG_ are FactSet Global formulas and are some of the most commonly used
formulas to retrieve prices, estimates, valuation, growth, and fundamentals. The
formulas return data from multiple database sources, based on a client's FactSet
subscription. While the source can be different depending on the selected company, all
formulas for a given company are pulled from the same database source.
In database specific codes, the database is specified in the FQL code, such as in
P_PRICE where the P_ element of the code denotes the pricing database and
FF_SALES denotes the FactSet Fundamentals database with the FF_ element of the
code. This distinction is highlighted in more detail throughout the examples below
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Inputs:
Input
Description
Identifier: ids= or ison= or ofdb=
ids
one or more securities;ie ids=ibm,msft,fds
OR ISON codes for CURRENT constituents Only - Consult OA page 14491
For More Information.
ison
^%UNIVERSE(ISON_SP500) is entered as
ison=sp500;^%UNIVERSE(ISON_MSCI_WORLD(0,1)) written as
ison=msci_world
isonParams
ison codes that use parameters;ie
;^%UNIVERSE(ISON_MSCI_WORLD(0,1) is written as
ison=msci_world&isonParams=0,1
OR OFDB file
Ofdb
ofdb file(no need for .ofdb extention)
ofdbDate
Specific date used for the constituents from the ofdb
OR Universe; should be small universe (< 500 )
Universe
Universe
Date Range: start,end,freq(default) or dates=
start
valid FQL date; start=0(default)
end
valid FQL date; end=0(default)
freq
valid FQL frequency; ie M
OR
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dates
yymmdd:yymmdd:freq or relative dates ie: 0:-4:m
item(s)
one or more FQL items in ETI orientation only;Only one item
allowed for all other orientation. There are 2 types of syntax
available
Standard FQL Syntax: ie FF_SALES(QTR,-1,-4,M)
OR
Name/Value Pair Syntax ie items=FF_SALES&period=qtr&start=1&end=-4&freq=m
period
used for FACTSET FUNDAMENTALS codes indicating estimate
period(ANN,QTR,SEMI-ANN;default:ANN;ie PERIOD=QTR
orientation
ETI(default),ITE,TIE,TE,ET,IT,TI;ison= does not work with IT or TI
cal
Calendar setting replicating PSETCAL
function;ie:LOCAL,FIVEDAY,FIVEDAYEOM,SEVENDAY, for
exchange code cal=AAM
Decimals
Positionally set according to the items in the selection, ie
decimals=,,3,4,3
dataType
= allows user to define a datatype for a column that has NA for
whole column; ie dataType=double for goog price in the 1990's
feelback
Setting to control data is not falling forward and display NAs
instead of carrying forward values, for those databases that do so
(using 'feelback','n').
Note: FQL codes used in the item parameter may have their arguments in different
positions of their respective codes, depending on if it’s the most commonly used code,
which will start with an FG_ or a database specific code.
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Set an item parameter in the following ways:
A) Standard FQL syntax
item=FF_SALES(QTR,-1,-8,M..USD), P_PRICE(-1,-8,M,USD…) .
This is a new style of syntax that may be used to access FQL history with this
ExtractFormulaHistory factlet and reflects the way a FactSet user would access the
data via ORE or Excel.
All FQL codes used in the item= parameters have their arguments in different positions
of their respective codes. For example, the currency argument for P_PRICE has it in
the 4th position(P_PRICE(-1,-8,M,USD…) ) , while FF_SALES(FF_SALES(QTR,-1,8,M..USD), ) has it in the 6th position.
This is an example of how one would set the items in FQL syntax:
factlet=ExtractFormulaHistory&ids=IBM,FDS&ff_sales(QTR,0,-5,M),p_price(0,-5,M)
B) Name/Value Pair Syntax:
item=FF_SALES,P_PRICE&period=QTR&start=0&end=-5&freq=m&curr=USD.
This is the style of syntax that has been used to access FQL history with this
ExtractFormulaHistory factlet and is still available for usage for clients who wish to
keep their access intact.
To use the codes with the Name/Value Pair Syntax, each code and its
parameters(mask) is stored in a separate file and saved into the production database.
When these codes are set in a file, they need to also keep the order of the arguments
intact. For example p_price("s,e,f,curr,)" or ff_sales(period,s,e,f). More details
about using this syntax is explained on page 3.
This is an example of how one would set the items with Name/Value Pair Syntax:
factlet=ExtractFormulaHistory&ids=IBM,FDS&items=ff_sales,p_price&dates=0:5:m&period=QTR
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Using multiple FQL codes with Standard FQL Syntax
When using 2 or more FQL items, ie 'ff_sales(QTR,0,-5,M),p_price(0,-5,M), the dates
are not lining up in a traditional way, although the values are correct for the dates that
are being called. Output will produce blanks where data is not available.
factlet=ExtractFormulaHistory&ids=IBM,FDS&items=ff_sales(QTR,0,5,M),p_price(0,-5,M)&format=pipe
Output
Id|Date|ff_sales|p_price
IBM|30-Sep-2010|24271.|
IBM|30-Nov-2010||141.460006713867
IBM|31-Dec-2010|29019.|146.759994506836
IBM|31-Jan-2011||162.
IBM|28-Feb-2011||161.880004882812
IBM|31-Mar-2011|24607.|163.070007324219
IBM|30-Apr-2011||170.580001831055
FDS|30-Nov-2010|173.289|88.669998168945
FDS|31-Dec-2010||93.76000213623
FDS|31-Jan-2011||100.800003051758
FDS|28-Feb-2011|177.635|104.879997253418
FDS|31-Mar-2011||104.730003356934
FDS|30-Apr-2011||109.410003662109
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To backfill the blank values you can add the dates as a date range: dates=0:-5:M or use
the syntax start=0&end=-5&freq=m. This will repeat values when the frequency input is
greater than natural frequency of the data.
factlet=ExtractFormulaHistory&ids=IBM,FDS&items=ff_sales(QTR,0,5,M),p_price(0,-5,M)&dates=0:-5:m&format=pipe
Output
Id|Date|ff_sales|p_price
IBM|30-Nov-2010|24271.|141.460006713867
IBM|31-Dec-2010|29019.|146.759994506836
IBM|31-Jan-2011|29019.|162.
IBM|28-Feb-2011|29019.|161.880004882812
IBM|31-Mar-2011|24607.|163.070007324219
IBM|30-Apr-2011|24607.|170.580001831055
FDS|30-Nov-2010|173.289|88.669998168945
FDS|31-Dec-2010|173.289|93.76000213623
FDS|31-Jan-2011|173.289|100.800003051758
FDS|28-Feb-2011|177.635|104.879997253418
FDS|31-Mar-2011|177.635|104.730003356934
FDS|30-Apr-2011|177.635|109.410003662109
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Using multiple FQL codes with Name/Value Pair Syntax
When using Name/Value Pair syntax, due to the fact that the date range is already being
set by default, the data will automatically be returned with repeated values when
frequency input is greater than the natural frequency of the data. This is the same format
as is returned by the earlier described FQL syntax with a date range.
factlet=ExtractFormulaHistory&ids=IBM,FDS&items=ff_sales,p_price&perio
d=qtr&dates=0:-5:m&format=pipe
Output
Id|Date|ff_sales|p_price
IBM|30-Nov-2010|24271.|141.460006713867
IBM|31-Dec-2010|29019.|146.759994506836
IBM|31-Jan-2011|29019.|162.
IBM|28-Feb-2011|29019.|161.880004882812
IBM|31-Mar-2011|24607.|163.070007324219
IBM|30-Apr-2011|24607.|170.580001831055
FDS|30-Nov-2010|173.289|88.669998168945
FDS|31-Dec-2010|173.289|93.76000213623
FDS|31-Jan-2011|173.289|100.800003051758
FDS|28-Feb-2011|177.635|104.879997253418
FDS|31-Mar-2011|177.635|104.730003356934
FDS|30-Apr-2011|177.635|109.410003662109
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Example 1:
In this example extract the last 5 months EPS for Exxon
Mobile (ticker XOM) using the FQL code FG_EPS.
factlet=ExtractFormulaHistory&ids=xom&item=fg_e
ps(0,-5,m)&dates=0:-5:M&format=pipe
In this example, the date argument is using relative rather
than absolute dates. To specify relative dates, enter the
number of periods and a period code, such as D for days, W
for weeks, or Q for quarters and Y for years. When using
relative dates, "0" refers to the most recent time period.
Therefore, 0Q refers to the most recent quarter end, while 1Q refers to two quarters ago.
Output
xom|31-Aug-2013|7.9485
xom|30-Sep-2013|7.9485
xom|31-Oct-2013|7.9485
xom|30-Nov-2013|7.9485
xom|31-Dec-2013|7.6492
xom|31-Jan-2014|7.6492
Note: When specifying the items in the request syntax, to
ensure most effectively that that the dates for the items, such
as price and sales, align correctly with the dates field, enter
the dates in the items portion of the request syntax as
specified above. For example fg_eps(0Q,-5Q,Q).
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Example 2
In this example extract the price for a number of securities,
i.e. Microsoft and IBM using the FQL code FG_PRICE.
factlet=ExtractFormulaHistory&ids=msft,ibm&items
=fg_price&format=pipe&orientation=ETI&dates=201
01215:20110115:D
Output
msft|15-Dec-2010|27.85
msft|16-Dec-2010|27.9875
msft|17-Dec-2010|27.9025
msft|20-Dec-2010|27.81
msft|21-Dec-2010|28.07
…
ibm|15-Dec-2010|144.72
ibm|16-Dec-2010|144.55
ibm|17-Dec-2010|145.
ibm|20-Dec-2010|144.51
ibm|21-Dec-2010|145.74
…
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Example 3
In this example, extract the last 5 periods of pricing and
sales data for Statoil (ticker STL-NO) using the pricing
database with the FQL code P_PRICE and the FactSet
Fundamentals database for sales data with the FQL code
FF_SALES. Both P_PRICE and FF_SALES in this example
are used in the item parameter.
factlet=ExtractFormulaHistory&ids=stlno&item=p_price(-5,0,Q,USD),ff_sales(QTR,5,0,Q,,USD)&dates=-5:0:Q&format=pipe
Output
stl-no|30-Sep-2012|25.834962844849|27953.548530563712
stl-no|31-Dec-2012|24.976417541504|28009.955109968781
stl-no|31-Mar-2013|24.209171295166|28808.781078457832
stl-no|30-Jun-2013|20.520631790161|25487.603285908699
stl-no|30-Sep-2013|22.696098327637|26884.329052269459
stl-no|31-Dec-2013|24.23003578186|26814.917802810669
Note: Because these are database specific codes and not
the most commonly used FG_ codes demonstrated in
Examples 1 and 2, their arguments for elements such as
currency are positioned differently in the codes. For
example, in the P_PRICE code, currency is the 4th
argument and in the FF_SALES code, currency is the 6th
argument. See Table 1 in Appendix for the FQL syntax for
the top 25 most frequently used codes from the FactSet
Fundamentals database.
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Example 4:
In this example, extract the price for IBM and Google for the
date range 12/31/1989 until 12/31/2001 on a monthly
frequency. Since there is no available price data for Google
starting in 1989, the data would be NA. When using the
ExtractFormulaHistory function the data type can be
specified for treatment of NA’s, for example as a double or
integer.
By default, the data type returned is determined by the first
value of the items being returned. In the following case the
p_price code returned as a string by default because the
values for GOOG are NA (if the request is made for just IBM
with the same date range the p_price data is returned as a
double since the data is available for IBM). But with the
addition of the datatype= optional argument, it is possible to
specify how the data is returned.
factlet=ExtractFormulaHistory&ids=goog&items=p_
price(12/31/1989,12/31/2001,m)&format=pipe&dataty
pe=double
Output
Entity|Date|Double
Id|Date|p_price
goog|29-Dec-1989|
goog|31-Jan-1990|
goog|28-Feb-1990|
goog|30-Mar-1990|
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Example 5:
In this example, we are using the MTD frequency to display
a Sunday date which is February 2, 2014. By default, the
Sunday date will not be displayed, so we must use the
cal=Sevenday calendar setting.
factlet=ExtractFormulaHistory&ids=pgsno&items=fe_estimate(eps,nest,quarterly_roll,2013/
1c,20121231,20140202,MTD,'currency=usd'),P_PRIC
E(20121231,20140202,MTD)&dates=20121231:20140
202:MTD&cal=sevenday&format=pipe
Output:
pgs-no|31-Dec-2012|7.|95.349998474121
pgs-no|31-Jan-2013|8.|96.900001525879
pgs-no|28-Feb-2013|15.|90.5
…
pgs-no|31-Oct-2013|16.|72.199996948242
pgs-no|30-Nov-2013|16.|73.400001525879
pgs-no|31-Dec-2013|16.|71.449996948242
pgs-no|31-Jan-2014|16.|64.849998474121
pgs-no|2-Feb-2014|16.|64.849998474121
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Example 5:
In this example, retrieve the 60 month beta coefficient for
Exxon Mobile relative to the S&P 500. The FactSet BETA
function measures a security or portfolio's volatility relative to
an index. If a security has a beta coefficient greater than
one, it is considered more volatile. If a security has a beta
coefficient of less than one, its price can be expected to rise
and fall more slowly. In the FQL syntax, the BETA function
returns the coefficient relative to an index and over any
period of time that you specify.
factlet=ExtractFormulaHistory&ids=XOM&item=BET
A('SP50',0,-59M,M)&format=pipe
Output
XOM|6-Oct-2011|0.510051488876
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Example 6:
This example is using some of the FQL functions that are
available, such as AVG, MAX, MIN, STD, etc. FactSet offers
hundreds of functions and a comprehensive alphabetical list
of functions is available on Online Assistant page 1410.
Extract the average, maximum, minimum, and standard
deviation of earnings per share (EPS) values for General
Electric, for the last 6 quarters of history. The EPS values
are coming from the FactSet Fundamentals database using
the FQL code FF_EPS(QTR,0,-5,Q).
factlet=ExtractFormulaHistory&ids=GE&item=AVG(
FF_EPS(QTR,0,-5,Q)),MAX(FF_EPS(QTR,0,5,Q)),MIN(FF_EPS(QTR,0,5,Q)),STD(FF_EPS(QTR,0,-5,Q))&format=pipe
Output
GE|6-Oct-2011|0.3|0.36|0.21|0.050596442563
Note: The FactSet AVG function returns the average, or
mean, value of the data values requested and ignores nonavailable data over the specified time period. For example,
when calculating the average annual sales over the last 10
years, if one out of the 10 years is unavailable, you will still
get an average using the 9 years of available data.
The FactSet STD function returns the standard deviation by
first finding the average of the data set. Each data item is
then subtracted from the average, and the difference is
squared. The sum of the squared values is divided by the
number of data items minus one. The function returns the
square root of that value.
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Example 7:
In this example, retrieve the last 6 quarters of EPS and price
for the current constituents of the S&P 500 using the FQL
codes FF_SALES and P_PRICE and the names of the
companies using the FQL code PROPER_NAME. The
universe, which is specified as the S&P 500, is based on the
FQL code ISON_SP500, returns the current constituents of
the S&P 500.
factlet=ExtractFormulaHistory&ison=sp500&item=p
roper_name,ff_eps(qtr,0,-5,Q),p_price(0,5,q)&format=pipe
Output
17290810|31-Aug-2012|Cintas
Corporation|0.6|40.419998168945
17290810|30-Nov-2012|Cintas
Corporation|0.63|41.439998626709
…
41308610|28-Sep-2012|Harman International Industries,
Incorporated|0.79|46.159999847412
41308610|31-Dec-2012|Harman International Industries,
Incorporated|0.68|44.639999389648
…
80589M10|28-Sep-2012|SCANA
Corporation|0.91|48.270000457764
80589M10|31-Dec-2012|SCANA
Corporation|0.78|45.639999389648
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Example 8:
In this example, retrieve proprietary data stored uploaded
into FactSet and stored in an Open FactSet Database
(OFDB). OFDB is a high-performance multi-dimensional
database system used to securely store proprietary numeric
and textual data on FactSet.
factlet=ExtractFormulaHistory&ofdb=5002_EAGLE&
item=P_PRICE(0D)&format=pipe
Output
4002121|25-Feb-2014|30.75
4012250|25-Feb-2014|53.069999694824
4061412|25-Feb-2014|138.75
4232445|25-Feb-2014|5.909999847412
4588364|25-Feb-2014|88.75
4598589|25-Feb-2014|169.149993896484
…
NOTE:By default, when using the ofdb parameter, the ids
from the ofdb are based on the most recent date. To return
the securities from the ofdb from a specific date, use the
ofdbDate= parameter.
factlet=ExtractFormulaHistory&ofdb=5002_EAGLE&
item=P_PRICE(0D)&ofdbDate=20080101&format=pi
pe
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Example 9:
In this example, extract the last 5 month end prices for a
universe of 500 securities. The US_UNIV function is used to
narrow down that universe of 500 companies. This function
returns the companies (including research companies and
dually-listed Canadian companies) that pass given screening
parameters.
In this example, the securities are screened on specified
criteria, as of the most recent date for each screening item.
The 500 selected companies are ranked using the URANKX
function, which returns the absolute rank of the formula
evaluated for each company in the report relative to the
companies in the specified universe. The companies are
ranked according to having the following criteria met: having
a market value greater than 10 million USD, price greater
than 5 USD and the primary exchange which they are listed
on is in the United States.
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factlet=ExtractFormulaHistory&universe=URANKX((
FS_PARENT_EQUITY=CUSIP AND EC_MKT_CAP(0,
'CUR=USD')>10 AND P_PRICE(0,USD)>5 AND CONT
AINS(P_EXCOUNTRY,'UNITED STATES'))=1, EC_M
KT_CAP(0,'CUR=USD'))<=500S&items=p_price(0,4,M)&format=pipe
Output:
50242410|31-May-2012|68.190002441406
50242410|30-Jun-2012|74.01000213623
50242410|31-Jul-2012|70.889999389648
50242410|31-Aug-2012|70.23999786377
50242410|30-Sep-2012|71.709999084473
Note: This function is most effectively used for extracting a
smaller universe of securities with the
ExtractFormulaHistory factlet.There is a limit of 500
securities that can returned from a universe using the
US_UNIV function that is used within the
ExtractFormulaHistory factlet to fetch the universe. When
using the US_UNIV function with the ExtractDataSnapshot
factlet, the data is returned as of one specified date but there
is no limit on the number of securities that can be returned
from a universe that passes given screening criteria.
However, using the US_UNIV function with the
ExtractFormulaHistory function allows for history to be
extracted.
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Example 10:
By default, the values returned by ExtractFormuaHistory will
display a significant number of decimal points. To customize
the number of decimals returned, you can set them
positionally by the use of the decimals parameter
Default
Factlet=ExtractFormulaHistory&ids=ibm&items=p_price,
p_volume&format=pipe
ibm|26-Jun-2013|194.860000610352|3320.201904296875
W/Decimals= parameter
Factlet=ExtractFormulaHistory&ids=ibm&items=p_price,p_vo
lume&format=pipe&decimals=,,2,3
ibm|26-Jun-2013|194.86|3320.202
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Example 11:
In this example, for the securities IBM, Microsoft and Nokia,
extract the total return from 2 trading days before latest
quarterly EPS report date until 2 days after the latest
quarterly EPS report date. In this example demonstrating the
use of addition, however, subtraction, division and
multiplication can be performed as well.
factlet=ExtractFormulaHistory&ids=ibm,xon,nok&it
ems=P_TOTAL_RETURN(FF_EPS_RPT_DATE(QTR,
0) -2D,FF_EPS_RPT_DATE(QTR,0) +
2D)&format=pipe
Output:
ibm|19-Feb-2013|5.561578273773
xon|19-Feb-2013|-1.025700569153
nok|19-Feb-2013|-8.008652687073
NOTE:This will only be available if the client has the hex
symbol %26 stored internally to escape the “+” character by
its IT or web development department. If this escape
character is not included, the values will be displayed as O.
The stat packages Matlab and R have this capability already
builti in to its application.
[email protected]
(22)
Copyright © 2014 FactSet Research Systems Inc. All rights
reserved.
Appendix
Table 1: Top 25 most commonly used FactSet Fundamentals formulas
The table lists the top 25 most commonly used FactSet Fundamentals formulas,
displaying the arguments for retrieving the data for one date, a time range and
with a currency argument.
Description
FF_ExtractFormulaHistory Syntax
Acquisition of Business
(Cash Flow)
Capital Expenditures
(Fixed Assets)
Capital Expenditures
(Other Assets)
Cash Dividends Paid
Cash Flow from
Financing Activity - Net
(Cash Flow)
Cash Flow from
Investing Activity - Net
(Cash Flow)
Cash Flow from
Operating Activity - Net
(Cash Flow)
Changes in Working
Capital
Common Equity (Total)
Cost of Goods Sold
Excluding Depreciation
& Amortization
Depreciation &
Amortization (Cash
Flow)
Extrordinary Items
(Cash Flow)
Funds from Operations
FF_ACQ_BUS_CF(QTR,0,-4,,,USD)
Issuance of LT Debt
Long Term Debt
excluding Capital Lease
Obligations
Minority Interest Accumulated
Prederred Stock Carrying Value
FF_DEBT_LT_ISS_CF(ANN,0,-5AY,,,USD)
FF_DEBT_LT_XCAP(ANN,0,-5AY,,,USD)
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FF_CAPEX_FIX(ANN,0,-5AY,,,USD)
FF_CAPEX_OTH(ANN,0,-5AY,,,USD)
FF_DIV_CF(ANN,0,-5AY,,,USD)
FF_FIN_CF(ANN,0,-5AY,,,JPY)
FF_INVEST_CF(ANN,0,-5AY,,,USD)
FF_FUNDS_OPER_GROSS(ANN,0,-5AY,,,USD)
FF_WKCAP_CHG(ANN,0,-5AY,,,USD)
FF_COM_EQ(QTR,-10,0,,,EUR)
FF_COGS_XDEP(ANN,0,-5AY,,,JPY)
FF_DEP_EXP_CF(ANN,0,-5AY,,,USD)
FF_XORD_CF(ANN,0,-5AY,,,CAD)
FF_FUNDS_OPER_GROSS(ANN,0,-5AY,,,USD)
FF_MIN_INT_ACCUM(ANN,0,-5AY,,,USD)
FF_PFD_STK(QTR,0,-10,,,USD)
(23)
Copyright © 2014 FactSet Research Systems Inc. All rights
reserved.
Price - Close for
Calendar Period End
Purchase of
Investments
Reduction of LT Debt
Repurchase of Common
& Pref Stock
Sale of Common & Pref
Stock
Sales of Fixed Assets &
Businesses
Sales or Revenues - Net
Short-Term Debt (incl.
Current Portion of LTD)
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FF_PRICE_CLOSE_CP(ANN,0,-5AY,,,USD)
FF_INVEST_PURCH_CF(ANN,0,-5AY,,,USD)
FF_DEBT_LT_REDUCT_CF(ANN,0,-5AY,,,USD)
FF_STK_PURCH_CF(ANN,0,-5AY,,,USD)
FF_STK_SALE_CF(ANN,0,-5AY,,,USD)
FF_SALE_ASSETS_BUS_CF(ANN,0,-5AY,,,USD)
FF_SALES(ANN,0,-5AY,,,USD)
FF_DEBT_ST(ANN,0,-5AY,,,USD)
(24)
Copyright © 2014 FactSet Research Systems Inc. All rights
reserved.