convert2semiannual
Description
Examples
Aggregate Timetable Data to Semiannual 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 semiannual series by reporting the final price of each January-to-June period and July-to-December period.
SemiannualPrice = convert2semiannual(DataTimeTable(:,"Price"));
tail(SemiannualPrice)
Time Price ___________ ______ 30-Jun-2018 99.29 31-Dec-2018 84.26 30-Jun-2019 169.77 31-Dec-2019 153.22 30-Jun-2020 224.29 31-Dec-2020 301.04
SemiannualPrice
is a timetable containing the final prices for each reported semiannual period 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 convert2semiannual
to aggregate both intra-day data and aggregated quarterly data, which result in equivalent semiannual 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 semiannual periodicity.
Pass
DataTimeTable
directly toconvert2semiannual
.Aggregate
DataTimeTable
so that the result has a quarterly periodicity by usingconvert2quarterly
, and then pass the result toconvert2semiannual
.
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 semiannual periodicity. For each series, specify the aggregation method that is appropriate for the unit.
aggmethods = ["lastvalue" "sum"]; SemiannualTT1 = convert2semiannual(DataTimeTable,Aggregation=aggmethods); tail(SemiannualTT1)
Time Price Log_Return ___________ ______ __________ 30-Jun-2018 99.29 -0.032501 31-Dec-2018 84.26 -0.16414 30-Jun-2019 169.77 0.70054 31-Dec-2019 153.22 -0.10257 30-Jun-2020 224.29 0.38107 31-Dec-2020 301.04 0.2943
SemiannualTT1
is a timetable containing the semiannual data. Price
is a series of the final stock prices for each January-to-June period and July-to-December period, and Log_Return
is the sum of the log returns for each semiannual period.
Aggregate the data in two steps: aggregate the data so that the result has a quarterly periodicity, then aggregate the quarterly data to semiannual data. For each series, specify the aggregation method that is appropriate for the unit.
QuarterlyTT = convert2quarterly(DataTimeTable,Aggregation=aggmethods); tail(QuarterlyTT)
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
SemiannualTT2 = convert2semiannual(QuarterlyTT,Aggregation=aggmethods)
SemiannualTT2=6×2 timetable
Time Price Log_Return
___________ ______ __________
30-Jun-2018 99.29 -0.032501
31-Dec-2018 84.26 -0.16414
30-Jun-2019 169.77 0.70054
31-Dec-2019 153.22 -0.10257
30-Jun-2020 224.29 0.38107
31-Dec-2020 301.04 0.2943
QuarterlyTT
is a timetable with quarterly periodicity. Price
is a series of the final stock prices for each quarter, and Log_Return
is the sum of the log returns for each quarter.
SemiannualTT1
and SemiannualTT2
are equal.
Input Arguments
TT1
— Data to aggregate to semiannual periodicity
timetable
Data to aggregate to a semiannual 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 = convert2semiannual(TT1,'Aggregation',["lastvalue"
"sum"])
Aggregation
— Aggregation method for semiannual period to semiannual periodicity (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
defining how
data is aggregated over business days in a semiannual period to
semiannual periodicity (inter-day aggregation), 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, convert2semiannual
applies the specified method to all time series in TT1
. If you specify a string vector or cell vector aggregation
, convert2semiannual
applies aggregation(
to j
)TT1(:,
; j
)convert2semiannual
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 100.02 200.04 300.06 400.08 03-Jan-2018 99.96 199.92 299.88 399.84 . . . . . . . . . . . . . . . 28-Jun-2018 69.63 139.26 208.89 278.52 29-Jun-2018 70.15 140.3 210.45 280.60 30-Jun-2018 75.77 151.54 227.31 303.08 01-Jul-2018 75.68 151.36 227.04 302.72 02-Jul-2018 71.34 142.68 214.02 285.36 03-Jul-2018 69.25 138.50 207.75 277.00 . . . . . . . . . . . . . . . 29-Dec-2018 249.16 498.32 747.48 996.64 30-Dec-2018 250.21 500.42 750.63 1000.84 31-Dec-2018 256.75 513.50 770.25 1027.00
TT2
(in which all days are business
days and the 'lastvalue'
is reported on the
last business day of each semiannual period) are as
follows.Time AAA BBB CCC ___________ ______ ______ ________________ 30-Jun-2018 75.77 151.54 227.31 303.08 31-Dec-2018 256.75 513.50 770.25 1027.00
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
— Semiannual data
timetable
Semiannual data, returned as a timetable.
convert2semiannual
reports semiannual aggregation
results on the last business day of June and December. The time arrangement of TT1
and TT2
are the same.
If a variable of TT1
has no business-day records
during an annual period within the sampling time span,
convert2semiannual
returns a NaN
for that variable and annual period in TT2
.
The first date in TT2
is the last business date of
the semiannual period in which the first date in
TT1
occurs, provided
TT1
has business dates in that semiannual
period. Otherwise the first date in TT2
is the next
end-of-semiannual-period business date.
The last date in TT2
is the last business date of
the semiannual period in which the last date in
TT1
occurs, provided
TT1
has business dates in that semiannual
period. Otherwise the last date in TT2
is the
previous end-of-semiannual-period business date.
Version History
Introduced in R2021a
MATLAB Command
You clicked a link that corresponds to this MATLAB command:
Run the command by entering it in the MATLAB Command Window. Web browsers do not support MATLAB commands.
Select a Web Site
Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select: .
You can also select a web site from the following list:
How to Get Best Site Performance
Select the China site (in Chinese or English) for best site performance. Other MathWorks country sites are not optimized for visits from your location.
Americas
- América Latina (Español)
- Canada (English)
- United States (English)
Europe
- Belgium (English)
- Denmark (English)
- Deutschland (Deutsch)
- España (Español)
- Finland (English)
- France (Français)
- Ireland (English)
- Italia (Italiano)
- Luxembourg (English)
- Netherlands (English)
- Norway (English)
- Österreich (Deutsch)
- Portugal (English)
- Sweden (English)
- Switzerland
- United Kingdom (English)