convert2quarterly
Description
Examples
Aggregate Timetable Data to Quarterly Periodicity
Load the simulated stock price data and corresponding logarithmic returns in SimulatedStockSeries.mat
.
load SimulatedStockSeries
The timetable DataTimeTable
contains measurements recorded at various, irregular times during trading hours (09:30 to 16:00) of the New York Stock Exchange (NYSE) from January 1, 2018, through December 31, 2020.
For example, display the first few observations.
head(DataTimeTable)
Time Price Log_Return ____________________ ______ __________ 01-Jan-2018 11:52:48 100 -0.025375 01-Jan-2018 13:23:13 101.14 0.011336 01-Jan-2018 14:45:09 101.5 0.0035531 01-Jan-2018 15:30:30 100.15 -0.01339 02-Jan-2018 10:43:37 99.72 -0.0043028 03-Jan-2018 10:02:21 100.11 0.0039033 03-Jan-2018 11:22:37 103.96 0.037737 03-Jan-2018 13:42:27 107.05 0.02929
DataTimeTable
does not include business calendar awareness. If you want to account for nonbusiness days (weekends, holidays, and market closures) and you have a Financial Toolbox™ license, add business calendar awareness by using the addBusinessCalendar
function.
Aggregate the price series to a quarterly series by reporting the final price in each quarter.
QuarterlyPrice = convert2quarterly(DataTimeTable(:,"Price"));
QuarterlyPrice
is a timetable containing the final prices for each reported quarter in DataTimeTable
.
Specify Aggregation Method for Each Variable
This example shows how to specify the appropriate aggregation method for the units of a variable. It also shows how to use convert2quarterly
to aggregate both intra-day data and aggregated monthly data, which result in equivalent quarterly aggregates.
Load the simulated stock price data and corresponding logarithmic returns in SimulatedStockSeries.mat
.
load SimulatedStockSeries
The price series Price
contains absolute measurements, whereas the log returns series Log_Return
is the rate of change of the price series among successive observations. Because the series have different units, you must specify the appropriate method when you aggregate the series. Specifically, if you report the final price for a given periodicity, you must report the sum of the log returns within each period.
To understand how to maintain consistency among aggregation methods, use two approaches to aggregate DataTimeTable
so that the result has a quarterly periodicity.
Pass
DataTimeTable
directly toconvert2quarterly
.Aggregate
DataTimeTable
so that the result has a monthly periodicity by usingconvert2monthly
, and then pass the result toconvert2quarterly
.
In both cases, specify reporting the last price and the sum of the log returns for each period.
Directly aggregate the data so that the result has a quarterly periodicity. For each series, specify the aggregation method that is appropriate for the unit.
aggmethods = ["lastvalue" "sum"]; QuarterlyTT1 = convert2quarterly(DataTimeTable,Aggregation=aggmethods); tail(QuarterlyTT1)
Time Price Log_Return ___________ ______ __________ 31-Mar-2019 112.93 0.29286 30-Jun-2019 169.77 0.40768 30-Sep-2019 148.97 -0.1307 31-Dec-2019 153.22 0.02813 31-Mar-2020 229.88 0.40568 30-Jun-2020 224.29 -0.024618 30-Sep-2020 246.77 0.095517 31-Dec-2020 301.04 0.19879
QuarterlyTT1
is a timetable containing the annual data. Price
is a series of the final stock prices for each year, and Log_Return
is the sum of the log returns for each quarter.
Aggregate the data in two steps: aggregate the data so that the result has a monthly periodicity, then aggregate the monthly data to quarterly data. For each series, specify the aggregation method that is appropriate for the unit.
MonthlyTT = convert2monthly(DataTimeTable,Aggregation=aggmethods); tail(MonthlyTT)
Time Price Log_Return ___________ ______ __________ 31-May-2020 227.22 -0.029872 30-Jun-2020 224.29 -0.012979 31-Jul-2020 236.4 0.052585 31-Aug-2020 227.5 -0.038375 30-Sep-2020 246.77 0.081306 31-Oct-2020 275.07 0.10857 30-Nov-2020 298.87 0.082983 31-Dec-2020 301.04 0.0072345
QuarterlyTT2 = convert2quarterly(MonthlyTT,Aggregation=aggmethods); tail(QuarterlyTT2)
Time Price Log_Return ___________ ______ __________ 31-Mar-2019 112.93 0.29286 30-Jun-2019 169.77 0.40768 30-Sep-2019 148.97 -0.1307 31-Dec-2019 153.22 0.02813 31-Mar-2020 229.88 0.40568 30-Jun-2020 224.29 -0.024618 30-Sep-2020 246.77 0.095517 31-Dec-2020 301.04 0.19879
MonthlyTT
is a timetable with monthly periodicity. Price
is a series of the final stock prices for each month, and Log_Return
is the sum of the log returns for each month.
QuarterlyTT1
and QuarterlyTT2
are equal.
Input Arguments
TT1
— Data to aggregate to quarterly periodicity
timetable
Data to aggregate to a quarterly periodicity, specified as a timetable.
Each variable can be a numeric vector (univariate series) or numeric matrix (multivariate series).
Note
NaN
s indicate missing values.Timestamps must be in ascending or descending order.
By default, all days are business days. If your timetable does not account for nonbusiness
days (weekends, holidays, and market closures), add business calendar awareness by using
addBusinessCalendar
first. For example, the following command adds business calendar logic to include only NYSE
business
days.
TT = addBusinessCalendar(TT);
Data Types: timetable
Name-Value Arguments
Specify optional pairs of arguments as
Name1=Value1,...,NameN=ValueN
, where Name
is
the argument name and Value
is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.
Example: TT2 = convert2quarterly(TT1,'Aggregation',["lastvalue"
"sum"])
Aggregation
— Aggregation method for TT1
data for intra-quarter or inter-day aggregation
"lastvalue"
(default) | "sum"
| "prod"
| "mean"
| "min"
| "max"
| "firstvalue"
| character vector | function handle | string vector | cell vector of character vectors or function handles
Aggregation method for TT1
data defining
how to aggregate data over business days in an intra-quarter or
inter-day periodicity, specified as one of the following methods,
a string vector of methods, or a length
numVariables
cell vector of methods,
where numVariables
is the number of variables
in TT1
.
"sum"
— Sum the values in each year or day."mean"
— Calculate the mean of the values in each year or day."prod"
— Calculate the product of the values in each year or day."min"
— Calculate the minimum of the values in each year or day."max"
— Calculate the maximum of the values in each year or day."firstvalue"
— Use the first value in each year or day."lastvalue"
— Use the last value in each year or day.@customfcn
— A custom aggregation method that accepts a table variable and returns a numeric scalar (for univariate series) or row vector (for multivariate series). The function must accept empty inputs[]
.
If you specify a single method, convert2quarterly
applies the specified method to all time series in TT1
. If you specify a string vector or cell vector aggregation
, convert2quarterly
applies aggregation(
to j
)TT1(:,
; j
)convert2quarterly
applies each aggregation method one at a time (for more details, see retime
). For example, consider a daily timetable
representing TT1
with three
variables.
Time AAA BBB CCC ___________ ______ ______ ________________ 01-Jan-2018 100.00 200.00 300.00 400.00 02-Jan-2018 100.03 200.06 300.09 400.12 03-Jan-2018 100.07 200.14 300.21 400.28 . . . . . . . . . . . . . . . 31-Mar-2018 162.93 325.86 488.79 651.72 . . . . . . . . . . . . . . . 30-Jun-2018 223.22 446.44 669.66 892.88 . . . . . . . . . . . . . . . 30-Sep-2018 232.17 464.34 696.51 928.68 . . . . . . . . . . . . . . . 31-Dec-2018 243.17 486.34 729.51 972.68
TT2
(in which all days are business
days and the 'lastvalue'
is reported on the
last business day of each quarter) are as
follows.Time AAA BBB CCC ___________ ______ ______ ________________ 31-Mar-2018 162.93 325.86 488.79 651.72 30-Jun-2018 223.22 446.44 669.66 892.88 30-Sep-2018 232.17 464.34 696.51 928.68 31-Dec-2018 243.17 486.34 729.51 972.68
All methods omit missing data (NaN
s) in direct aggregation calculations on each variable. However, for situations in which missing values appear in the first row of TT1
, missing values can also appear in the aggregated results TT2
. To address missing data, write and specify a custom aggregation method (function handle) that supports missing data.
Data Types: char
| string
| cell
| function_handle
Daily
— Intra-day aggregation method for TT1
"lastvalue"
(default) | "sum"
| "prod"
| "mean"
| "min"
| "max"
| "firstvalue"
| character vector | function handle | string vector | cell vector of character vectors or function handles
Intra-day aggregation method for TT1
, specified as an aggregation method, a
string vector of methods, or a length numVariables
cell vector of
methods. For more details on supported methods and behaviors, see the
'Aggregation'
name-value argument.
Data Types: char
| string
| cell
| function_handle
Output Arguments
TT2
— Quarterly data
timetable
Quarterly data, returned as a timetable. The time arrangement of TT1
and TT2
are the same.
convert2quarterly
reports quarterly aggregation
results on the last business day of March, June, September, and
December.
If a variable of TT1
has no business-day records
during a quarter within the sampling time span,
convert2quarterly
returns a NaN
for that variable and quarter in TT2
.
If the first quarter (Q1
) of
TT1
contains at least one business day, the
first date in TT2
is the last business date of
Q1
. Otherwise, the first date in
TT2
is the next end-of-quarter business date
of TT1
.
If the last quarter (QT
) of
TT1
contains at least one business day, the
last date in TT2
is the last business date of
QT
. Otherwise, the last date in
TT2
is the previous end-of-quarter business
date of TT1
.
Version History
Introduced in R2021a
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