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convert2semiannual

Aggregate timetable data to semiannual periodicity

Description

example

TT2 = convert2semiannual(TT1) aggregates data (for example, data recorded daily or weekly) to a semiannual periodicity.

example

TT2 = convert2semiannual(___,Name,Value) specifies options using one or more optional name-value pair arguments in addition to the input argument in the previous syntax.

Examples

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Apply separate aggregation methods to related variables in a timetable while maintaining consistency between aggregated results when converting to a semiannual periodicity. You can use convert2semiannual to aggregate both intra-daily data and aggregated quarterly data. These methods result in equivalent semiannual aggregates.

Load a timetable (TT) of simulated stock price data and corresponding logarithmic returns. The data stored in TT is recorded at various times throughout the day on New York Stock Exchange (NYSE) business days from January 1, 2018 to December 31,2020. The timetable TT also includes NYSE business calendar awareness. If your timetable does not account for nonbusiness days (weekends, holidays, and market closures), add business calendar awareness by using addBusinessCalendar first.

load('SimulatedStock.mat','TT');
head(TT)
ans=8×2 timetable
            Time            Price     Log_Return
    ____________________    ______    __________

    02-Jan-2018 11:52:11    100.71     0.0070749
    02-Jan-2018 13:23:09    103.11      0.023551
    02-Jan-2018 14:45:30    100.24     -0.028229
    02-Jan-2018 15:30:48    101.37       0.01121
    03-Jan-2018 10:02:21    101.81     0.0043311
    03-Jan-2018 11:22:37    100.17      -0.01624
    03-Jan-2018 14:45:20     99.66    -0.0051043
    03-Jan-2018 14:55:39    100.12     0.0046051

Use convert2quarterly to aggregate intra-daily prices and returns to a quarterly periodicity. To maintain consistency between prices and returns, for any given quarter, aggregate prices by reporting the last recorded price using "lastvalue" and aggregate returns by summing all logarithmic returns using "sum".

TT1 = convert2quarterly(TT,'Aggregation',["lastvalue" "sum"])
TT1=12×2 timetable
       Time        Price     Log_Return
    ___________    ______    __________

    29-Mar-2018     108.9      0.08526 
    29-Jun-2018     96.24     -0.12358 
    28-Sep-2018    111.37      0.14601 
    31-Dec-2018     92.72     -0.18327 
    29-Mar-2019      78.7     -0.16394 
    28-Jun-2019    110.54      0.33973 
    30-Sep-2019    180.13       0.4883 
    31-Dec-2019    163.65    -0.095949 
    31-Mar-2020    177.46     0.081015 
    30-Jun-2020    168.96    -0.049083 
    30-Sep-2020    260.77      0.43398 
    31-Dec-2020    274.75     0.052223 

Use convert2semiannual to aggregate the data to a semiannual periodicity and compare the results of two different approaches. The first approach computes semiannual results by aggregating the quarterly aggregates and the second approach computes semiannual results by directly aggregating the original intra-daily data. Note that convert2semiannual reports results on the last business day of June and December.

tt1 = convert2semiannual(TT1,'Aggregation',["lastvalue" "sum"])  % Quarterly to semiannual
tt1=6×2 timetable
       Time        Price     Log_Return
    ___________    ______    __________

    29-Jun-2018     96.24    -0.038325 
    31-Dec-2018     92.72    -0.037261 
    28-Jun-2019    110.54      0.17579 
    31-Dec-2019    163.65      0.39235 
    30-Jun-2020    168.96     0.031932 
    31-Dec-2020    274.75       0.4862 

tt2 = convert2semiannual(TT ,'Aggregation',["lastvalue" "sum"])  % Intra-daily to semiannual
tt2=6×2 timetable
       Time        Price     Log_Return
    ___________    ______    __________

    29-Jun-2018     96.24    -0.038325 
    31-Dec-2018     92.72    -0.037261 
    28-Jun-2019    110.54      0.17579 
    31-Dec-2019    163.65      0.39235 
    30-Jun-2020    168.96     0.031932 
    31-Dec-2020    274.75       0.4862 

The results of the two approaches are the same because each semiannual period contains exactly two calendar quarters.

Input Arguments

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Data to aggregate to a semiannual periodicity, specified as a timetable. Semiannual aggregation results are reported on the last business day of June and December.

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), 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 Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Example: TT2 = convert2semiannual(TT1,'Aggregation',["lastvalue" "sum"])

Aggregation method for TT1 data for semiannual period to semiannual periodicity (inter-day aggregation), specified as the comma-separated pair consisting of 'Aggregation' and a character vector, string, or function handle applied to all time series in TT1, or a cell vector of character vectors, string vector, or cell vector of function handles the same length as the number of variables in TT1.

The aggregation methods define how data is aggregated over business days in a semiannual period to semiannual periodicity aggregation. Available aggregation methods are:

  • 'sum' — Sum the values in each semiannual period or day.

  • 'mean' — Calculate the mean of the values in each semiannual period or day.

  • 'prod' — Calculate the product of the values in each semiannual period or day.

  • 'min' — Calculate the minimum of the values in each semiannual period or day.

  • 'max' — Calculate the maximum of the values in each semiannual period or day.

  • 'firstvalue' — Use the first value in each semiannual period or day.

  • 'lastvalue' — Use the last value in each semiannual period or day.

All methods listed above omit missing data (NaNs) in direct aggregation calculations. However, in situations in which missing values appear in the first row of TT1, missing values can also appear in the aggregated results TT2.

Additionally, you can specify aggregation methods as function handles. To include missing data, specify functions as function handles that include the missing data when aggregating data. Aggregation functions must accept the underlying data stored in TT1 and return an output that is a scalar or a row vector, and must accept empty inputs. Each aggregation function is applied to the corresponding variable and called one at a time. Each variable must contain either a single numeric vector or numeric matrix. 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

The corresponding default semiannual results representing 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

Data Types: char | string | cell | function_handle

Method for intra-day aggregation for data in TT1, specified as the comma-separated pair consisting of 'Daily' and a scalar character vector, string, or function handle applied to all time series in TT1, or a cell vector of character vectors, string array, or cell vector of function handles the same length as the number of variables in TT1.

Data Types: char | string | cell | function_handle

Output Arguments

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Semiannual data, returned as a timetable. The function returns NaNs for variables in TT2 for semiannual periods when no data is recorded on any business days for those variables in TT1. If TT1 is in ascending order, so too is TT2, and if TT1 is in descending order, so too is 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.

Introduced in R2021a