6.1 Data

The Active/Passive Sector Strategy developed by EPFR uses the ratio of average allocation of active over passive funds. This section aims to give the reader an understanding of the methodology for constructing the variables used in the strategy.

6.1.1 Aggregations

The first step is defining a subset of data to capture in calculating our signal.

This strategy uses the following portions of the EPFR dataset:

  • Equity fund-level sector weightings, using the EPFR Sector Allocation database.

  • Funds with a geographic mandate confined to the country or region of interest (e.g. for the US sector, we look only at funds that have a mandate to invest in the United States).

  • Active/Passive tagging at the fund level, using EPFR’s fund classifications.

Active and passive equity funds which report their sector allocations to EPFR have grown substantially over time. The figure below shows EPFR’s coverage over time of funds used to create this signal for different countries and regions.

AUM ($BB) of Equity funds reporting sector allocations, by region
Emerging Markets Japan United Kingdom Eurozone United States
2006 113 0 1 24 0
2007 141 0 5 42 0
2008 128 0 4 25 0
2009 175 11 32 23 26
2010 256 11 34 23 28
2011 307 15 53 54 111
2012 249 12 50 52 129
2013 316 59 81 103 1,158
2014 438 113 103 177 1,546
2015 408 185 105 220 1,788
2016 416 186 91 182 2,332
2017 584 260 105 257 3,134
2018 687 317 156 276 3,878
2019 746 364 127 241 4,624
2020 738 406 103 219 5,165
2021 967 603 144 283 7,178
2022 770 504 123 190 6,559
* Updated at the end of July each year

Users looking for more specific detail can customize this aggregation even further using EPFR’s fund-level or share class-level granularity. Some good examples would be to consider only ETFs or mutual funds, geographic mandate, and fund domicile. Users can leverage these tags to different degrees in creating aggregated signals to backtest. Further detail about this is available in the section EPFR Data & Filters.

This can be achieved using fund-level allocation files or reaching out to EPFR’s Quant Team for customized aggregations.

6.1.2 Active/Passive Indicator

To begin calculating the active/passive indicator, we start with our subset of active and passive equity funds with a geographic mandate confined to the country or region of interest. Then, for each sector, we compute equally-weighted average allocations, within our country or region. These equal-weight averages are computed separately across active and passive funds, as shown below.

\[\overline{\text{Active Allocation}}_{s,r,m} = \frac{\sum^{N}_{i=n}{\text{Sector Allocation}_{i,s,r,m}}}{N}\]

Where:

  • \(\overline{\text{Active Allocation}}\) = the equally-weighted average allocations to a sector \(s\), within country or region \(r\), across all Active funds \(i\), for month \(t\)

\[\overline{\text{Passive Allocation}}_{s,r,m} = \frac{\sum^{N}_{i=n}{\text{Sector Allocation}_{i,s,r,m}}}{N}\]

Where:

  • \(\overline{\text{Passive Allocation}}\) = the equally-weighted average allocations to a sector \(s\), within country or region \(r\), across all Passive funds \(i\), for month \(m\)

Finally, to get our active/passive indicator for a sector within our country or region, we express the average allocation of active funds as percentage of that over passive funds.

\[\text{Active/Passive Indicatior}_{s,r,m} = 100 \times \frac{\overline{\text{Active Allocation}}_{s,r,m}}{\overline{\text{Passive Allocation}_{s,r,m}}}\]

Where:

  • \(\text{Active/Passive Indicatior}\) = the ratio of average active over passive allocations to a sector \(s\), within country or region \(r\), across all Passive funds \(i\), for month \(m\)

We repeat this across all different sectors and countries or regions for the entire history.

6.1.3 Aggregate Indicator File

Users may create the active/passive indicator for their desired sector and country aggregations using the methodology described in the previous section.

Users also have the option to use the Active/Passive Sector Strategy files EPFR provides, which are updated monthly at 5:00 PM EST with a T+23 day lag, and are available in the user’s EPFR FTP connection under the Strategies folder. There are five different types of Active/Passive Sector Strategy files available for different countries or geographic regions which include: Emerging Markets (EM), Japan (JP), United Kingdom (UK), Eurozone, and the United States (US). Each of these files contain aggregate active/passive indicator data for 12 sectors (displayed below).

For this demonstration, we will focus on the US so we will be using the file ActPasSector-US-monthly.csv, which can be downloaded from the user’s FTP under the folder Strategies/monthly and can be stored in the user’s local folder EPFR/monthly.

Below shows a snippet of what this file contains:

Strategies/monthly/ActPasSector-US-monthly.csv

CDisc CStpls Engy Fins HCare Indls Tech Matls Telco Utes REst FinsExREst
202201 -0.0433309 -0.2569239 -0.0847072 -0.0257469 0.0903608 0.0596510 -0.0019699 -0.0933515 1.0445337 -0.4266538 -0.6044792 0.1126693
202202 -0.0409194 -0.2444632 -0.1180361 -0.0344230 0.1048389 0.0656965 0.0052952 -0.1229965 0.9884343 -0.4413073 -0.6168956 0.1026448
202203 -0.0321449 -0.2341893 -0.0948149 -0.0500640 0.1074406 0.0747911 0.0012635 -0.1240600 0.9621974 -0.4181823 -0.5910677 0.0834273
202204 -0.0299786 -0.2415415 -0.1160230 -0.0595987 0.1177913 0.0611670 0.0134044 -0.1411849 0.9257296 -0.4332796 -0.5914524 0.0828742
202205 -0.0220213 -0.2374495 -0.1246013 -0.0375359 0.1032653 0.0726302 0.0091783 -0.0900981 0.7769177 -0.4187418 -0.5691501 0.0965970
202206 -0.0332674 -0.2413157 -0.1121087 -0.0483676 0.1201985 0.0606431 0.0013251 -0.0841842 0.7326374 -0.4030974 -0.5752789 0.0890789

note: all strategy files represent indicator as a % figure, i.e. 0.1 is 0.1%

For convenience, save the path to the active/passive indicator file you choose to use as indicator.file. Example shown below:

indicator.file <- "C:\\EPFR\\monthly\\ActPasSector-US-monthly.csv"

6.1.4 Return File

The return file for this strategy should contain monthly passive equity returns for each of the sectors in the aggregate indicator file, over the period of time and within the country or region the user wants to backtest.

The user can choose to use return data found with their own resources, or they have the option to use a file EPFR provides of Sector Returns, which is available in the user’s EPFR FTP connection under the Returns folder (more information available in Returns Information). The return file that EPFR provides contains Fund Return data and can be used as a proxy to equity market returns. The user can recreate these files using EPFR’s daily flow data with the following equation:

\[\text{Fund Return}_{s,r,t} = 100 \times \frac{\sum^{N}_{i=m} \text{Portfolio Change}_{i,s,r,t}}{\sum^{N}_{i=m} \text{Assets Start}_{i,s,r,t}}\] Where:

  • \(\text{Fund Return}\) = the percentage return of sector \(s\), for country or region \(r\), across all funds in our universe \(i\), for day \(t\)

However, since this signal is limited to monthly granularity, it is important to ensure that returns are also indexed by month. If the user’s return file is indexed daily or weekly, the function mat.daily.to.monthly(, T) from library('EPFR.r') should be used when implementing the strategy.

For this demonstration, we will focus on the US so we will be using the file PsuedoReturns-Sector-US-daily.csv, which can be downloaded from the user’s FTP under the folder Returns/daily and can be stored in the user’s local folder EPFR/returns.

Below shows a snippet of what this file contains:

Returns/daily/PsuedoReturns-Sector-US-daily.csv

CStpls Engy Fins HCare Indls Tech Matls Telco Utes REst
202201 2.4614 0.8773 1.0816 1.7560 1.4368 3.9725 1.4354 2.4248 1.8801 1.4120
202202 -0.2091 3.1671 -0.9604 -0.5535 1.1053 0.1163 -0.4423 -0.1498 0.5049 -1.5710
202203 -1.4729 -1.3324 -1.7606 -1.0156 -1.5969 -1.8327 -1.0746 -1.8588 -0.2614 -1.2084
202204 -3.6917 -2.5322 -2.7660 -2.4791 -2.7227 -4.4142 -2.2345 -3.3863 -2.9428 -4.6633
202205 -0.2772 -1.5009 -0.3149 -1.6843 -0.8919 -0.9466 -1.8780 -0.1713 -1.3320 -1.1871
202206 -0.7274 -2.0990 -0.7200 -0.4972 0.5389 -1.5574 -1.4392 -1.1923 1.0359 -0.2166

For convenience, save the path to the return file you choose to use as ret.file. Example shown below:

ret.file <- "C:\\EPFR\\returns\\PsuedoReturns-Sector-US-daily.csv"