Main Content

convert2annual

Aggregate timetable data to annual periodicity

Description

example

TT2 = convert2annual(TT1) aggregates data (for example, recorded daily or weekly data) to annual periodicity.

example

TT2 = convert2annual(TT1,Name,Value) uses additional options specified by one or more name-value arguments.

Examples

collapse all

Load the simulated stock price data and corresponding logarithmic returns in SimulatedStockSeries.mat.

load SimulatedStockSeries

The timetable DataTable 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(DataTable)
ans=8×2 timetable
            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

DataTable 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 an annual series by reporting the final price in each year.

AnnualPrice = convert2annual(DataTable(:,"Price"));

AnnualPrice is a timetable containing the final prices for each reported year in DataTable.

This example shows how to specify the appropriate aggregation method for the units of a variable. It also shows how to use convert2annual to aggregate both intra-day data and aggregated intra-day-to-monthly data, which result in equivalent annual 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 convert2annual maintains consistency among aggregation methods, use two approaches to aggregate DataTable so that the result has an annual periodicity.

  1. Pass DataTable directly to convert2annual.

  2. Aggregate DataTable so that the result has a monthly periodicity by using convert2monthly, and then pass the result to convert2annual.

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 an annual periodicity. For each series, specify the aggregation method that is appropriate for the unit.

aggmethods = ["lastvalue" "sum"];
AnnualTT1 = convert2annual(DataTable,Aggregation=aggmethods)
AnnualTT1=3×2 timetable
       Time        Price     Log_Return
    ___________    ______    __________

    31-Dec-2018     84.26     -0.19664 
    31-Dec-2019    153.22      0.59797 
    31-Dec-2020    301.04      0.67537 

AnnualTT1 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 year.

Aggregate the data in two steps: aggregate the data so that the result has a monthly periodicity, then aggregate the monthly data to annual data. For each series, specify the aggregation method that is appropriate for the unit.

MonthlyTT = convert2monthly(DataTable,Aggregation=aggmethods);
tail(MonthlyTT)
ans=8×2 timetable
       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 

AnnualTT2 = convert2annual(MonthlyTT,Aggregation=aggmethods)
AnnualTT2=3×2 timetable
       Time        Price     Log_Return
    ___________    ______    __________

    31-Dec-2018     84.26     -0.19664 
    31-Dec-2019    153.22      0.59797 
    31-Dec-2020    301.04      0.67537 

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.

AnnualTT1 and AnnualTT2 are equal.

Input Arguments

collapse all

Data to aggregate to an annual periodicity, specified as a timetable.

Each variable can be a numeric vector (univariate series) or numeric matrix (multivariate series).

Note

  • NaNs 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) and you have a Financial Toolbox™ license, 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 = convert2annual(TT1,'Aggregation',["lastvalue" "sum"])

Aggregation method for TT1 defining how to aggregate data over business days in a year to an annual 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, convert2annual applies the specified method to all time series in TT1. If you specify a string vector or cell vector aggregation, convert2annual applies aggregation(j) to TT1(:,j); convert2annual applies each aggregation method one at a time (for more details, see retime). For example, consider an input daily timetable 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
          .             .         .         .         .
          .             .         .         .         .
          .             .         .         .         .
      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
By default, convert2annual applies the aggregation method "lastvalue", which reports for each variable the values of the last business day of each year. The aggregated annual results are as follows:
TT2 = convert2annual(TT1)
TT2 =

  1×3 timetable

          Time         AAA       BBB            CCC       
      ___________    ______    ______    ________________
      31-Dec-2018    256.75    513.50    770.25   1027.00

All methods omit missing data (NaNs) 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

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

Month that ends annual periods, specified as a value in this table.

ValueMonth Ending Each Year
"January" or 1January
"February" or 2February
"March" or 3March
"April" or 4April
"May" or 5May
"June" or 6June
"July" or 7July
"August" or 8August
"September" or 9September
"October" or 10October
"November" or 11November
"December" or 12December

Data Types: double | char | string

Output Arguments

collapse all

Annual data, returned as a timetable. 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, convert2annual returns a NaN for that variable and annual period in TT2.

If the first annual period (year1) of TT1 contains at least one business day, the first date in TT2 is the last business date of year1. Otherwise, the first date in TT2 is the next end-of-year-period business date of TT1.

If the last annual period (yearT) of TT1 contains at least one business day, the last date in TT2 is the last business date of yearT. Otherwise, the last date in TT2 is the previous end-of-year-period business date of TT1.

Introduced in R2021a