5.1 Data | EPFR Strategy Notebooks

5.1.1 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 country weightings, using the EPFR Country Allocation database.
Active/Passive tagging at the fund level, using EPFR’s fund classifications.

Active and passive equity funds which report their country allocations to EPFR have grown substantially over time. The figure below shows EPFR’s coverage over time of active versus passive funds used to create this signal.

AUM ($BB) of Equity funds reporting country allocations
	Active	Passive	Total
2002	138	2	140
2003	128	3	131
2004	170	9	179
2005	267	18	286
2006	406	63	469
2007	521	106	627
2008	438	104	542
2009	366	86	452
2010	429	103	532
2011	534	130	663
2012	454	121	576
2013	609	372	981
2014	777	539	1,317
2015	780	678	1,458
2016	700	769	1,470
2017	879	1,119	1,998
2018	998	1,324	2,322
2019	1,181	1,417	2,599
2020	1,070	1,488	2,558
2021	1,459	2,121	3,581
2022	1,024	1,971	2,995
^* 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.

5.1.2 Active/Passive Indicator

To begin calculating the active/passive indicator, we start with our subset of active and passive cross-border equity funds. Then for each country, we compute equally-weighted average allocations. These averages are computed separately across active and passive funds, as shown below.

\[\overline{\text{Active Allocation}}_{c,m} = \frac{\sum^{N}_{i=m}{\text{Country Allocation}_{i,c,m}}}{N}\]

Where:

$\overline{\text{Active Allocation}}$ = the equally-weighted average allocations to a country $c$, across all Active funds in our universe $i$, for month $m$

\[\overline{\text{Passive Allocation}}_{c,m} = \frac{\sum^{N}_{i=m}{\text{Country Allocation}_{i,c,m}}}{N}\]

Where:

$\overline{\text{Passive Allocation}}$ = the equally-weighted average allocations to a country $c$, across all Passive funds in our universe $i$, for month $m$

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

\[\text{Active/Passive Indicatior}_{c,m} = 100 \times \frac{\overline{\text{Active Allocation}}_{c,m}}{\overline{\text{Passive Allocation}_{c,m}}}\] Where:

$\text{Active/Passive Indicatior}$ = the ratio of average active over passive allocations to a country $c$ and month $t$

We repeat this across all different countries for the entire history.

5.1.3 Aggregate Indicator File

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

Users also have the option to use the Active/Passive Country Strategy file EPFR provides, which is updated monthly at 5:00 PM EST with a T+23 day lag, and is available in the user’s EPFR FTP connection under the Strategies folder. The Active/Passive Country Strategy file contains aggregate active/passive indicator data for 55 countries in the ACWI, EAFE and Emerging Market country universes.

For this demonstration, we will be using the file ActPasCtry-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/ActPasCtry-monthly.csv

	AU	BR	CA	CN	ID	IN	JP	KR	MX	MY	PH	RU	SG	TH	TR	TW	ZA	AR	CL	CO	CZ	EG	HU	IL	NZ	NO	PE	PL	SE	CH	GB	AT	BE	DK	FI	FR	DE	GR	IE	IT	NL	PT	ES	HK	US	JO	MA	PK	AE	QA	VE	SA	KW
202201	-0.4116589	0.3215365	-0.4395098	0.2033904	0.6814724	0.4359012	-0.4390273	0.4637412	0.4790104	-0.7655074	0.0506318	0.8326907	0.5432593	-0.3565337	-0.0885959	0.2369512	-0.2751302	5.015702	-0.1436980	-0.4264640	0.2227321	2.269794	1.759860	-0.3421111	-0.3968119	-0.1757148	1.1429755	0.4494854	-0.1866656	-0.1539115	-0.0712242	0.0931108	-0.4833248	0.0320463	-0.4019757	-0.0861903	-0.1956867	1.5642416	0.8871501	-0.2181529	-0.1235379	0.1192964	-0.4029589	0.1356841	0.0619282	-0.3309885	0.3617819	1.336750	-0.2530996	-0.2681152	NA	-0.3396732	-0.5408369
202202	-0.3903412	0.3025222	-0.4225272	0.2086961	0.8346743	0.4481174	-0.4344863	0.4338277	0.4957880	-0.7170507	0.1166151	0.2536770	0.6315095	-0.2658749	-0.0945730	0.2137518	-0.2825992	4.950402	-0.0976879	-0.4403187	0.2610183	2.137616	1.957246	-0.2530923	-0.4229628	-0.0924395	1.1403516	0.7415435	-0.1708561	-0.1436105	-0.0530148	0.0119293	-0.5128763	0.0592285	-0.3967172	-0.1061223	-0.1857912	1.6285870	0.9143402	-0.2320151	-0.1179328	0.1480624	-0.3816681	0.2046359	0.0566413	-0.3410773	0.3278588	3.415418	-0.3185905	-0.4135812	NA	-0.3550474	-0.6430551
202203	-0.3688560	0.2805165	-0.4101576	0.2130056	0.9772176	0.4236433	-0.4208925	0.4320335	0.4824436	-0.7213895	0.0990333	2.0298302	0.7424796	-0.2424444	-0.1239329	0.1876749	-0.2388349	3.783801	-0.2466231	-0.4549257	-0.1252377	1.595001	1.325004	-0.2543773	-0.2641990	-0.1344421	1.3196491	0.2755998	-0.1513770	-0.1460110	-0.0560442	-0.0339794	-0.5163832	0.0114068	-0.4324152	-0.1140916	-0.2140291	1.3045336	0.9964926	-0.2666780	-0.1303510	0.1535921	-0.3935603	0.2816546	0.0743059	-0.3084517	0.2464617	2.497610	-0.2623263	-0.3940539	NA	-0.4000018	-0.6861364
202204	-0.3687968	0.3185151	-0.4228410	0.1550255	0.7892834	0.3386751	-0.4307848	0.3677196	0.5230830	-0.7305293	-0.1046205	-0.2413952	0.6288219	-0.2760861	-0.2448614	0.1259917	-0.2605456	4.307477	-0.2994163	-0.3512995	0.0845009	1.215186	2.770224	-0.2637296	-0.3177815	-0.1463344	0.8749594	0.6741988	-0.1256812	-0.1496520	-0.0667768	0.0192244	-0.4876611	0.0354283	-0.4200972	-0.0827129	-0.1956528	1.1511023	1.0684993	-0.2438731	-0.0809011	0.0970967	-0.3569473	0.2033025	0.0830815	-0.4963108	-0.0593894	2.127893	-0.1883872	-0.3809750	NA	-0.3812137	-0.7135296
202205	-0.3249846	0.3724780	-0.4114695	0.1905780	0.8704149	0.4266375	-0.4054981	0.3958469	0.5815749	-0.6398179	-0.0349082	-0.2690386	0.8008241	-0.1764559	-0.2346205	0.1914458	-0.2733041	2.754161	-0.2627486	-0.3475267	0.0143530	1.225950	2.988442	-0.1929273	-0.3614982	-0.1143957	0.8995259	0.6297235	-0.1119129	-0.1712639	-0.0724077	-0.0228316	-0.5338787	0.0560249	-0.4237451	-0.1154279	-0.2119235	0.8590158	0.9323932	-0.2412536	-0.1130822	0.1693429	-0.3634012	0.2639463	0.0492678	-0.3985578	-0.0424219	1.870296	-0.1153921	-0.3414538	NA	-0.3858441	-0.7030830
202206	-0.3155353	0.3446796	-0.3982346	0.2677924	0.8230942	0.4334032	-0.4136115	0.3311540	0.5725105	-0.6247500	-0.0960704	-0.1732658	0.7694420	-0.1765785	-0.3187803	0.1387018	-0.2167914	3.801217	-0.3088773	-0.3191568	-0.0639615	1.132402	2.504805	-0.1836117	-0.3493510	-0.0881084	0.8910945	0.5377157	-0.1594006	-0.1762541	-0.0893935	-0.1267178	-0.5137351	0.0860649	-0.4322122	-0.1106705	-0.2105706	0.9199082	0.9540571	-0.2610024	-0.1213421	0.2150883	-0.3659797	0.2774824	0.0490680	-0.3892771	0.0136632	1.366859	-0.0071518	-0.3459011	NA	-0.3127394	-0.6181641

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\\ActPasCtry-monthly.csv"

5.1.4 Return File

The return file for this strategy should contain monthly passive equity ETF returns for each of the countries in the aggregate indicator file, over the period of time 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 ETF Country 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:

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

$\text{Fund Return}$ = the percentage return of country $c$, 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 be using the file PsuedoReturns-Country-ETF-daily.csv, which can be downloaded from the user’s FTP under the folder Returns/daily and can be stored in the users local folder EPFR/returns.

Below shows a snippet of what this file contains:

Returns/daily/PsuedoReturns-Country-ETF-daily.csv

	AU	BR	CA	CN	ID	IN	JP	KR	MX	MY	PH	RU	SG	TH	TR	TW	ZA	AR	CL	CO	CZ	EG	HU	IL	NZ	NO	PE	PL	SE	CH	GB	AT	BE	DK	FI	FR	DE	GR	IE	IT	NL	PT	ES	HK	US	MA	PK	AE	QA	SA
202201	NA	0.973288	1.594462	0.721983	-1.040899	1.961128	1.001474	0.183599	1.475952	-0.366419	1.593265	2.045048	NA	0.591932	1.577889	0.627262	1.843685	3.597642	1.377244	0.373134	0.672721	0.931811	-0.028029	4.223976	NA	0.600713	1.608620	1.452223	1.713156	1.138853	0.237582	0.440324	0.854906	2.788030	1.604763	0.613942	1.038684	1.651220	1.438685	1.281007	1.812263	1.657264	0.086309	NA	2.092986	NA	0.580811	-0.744660	0.136978	0.734779
202202	NA	0.182113	0.014783	0.416247	-0.094755	-0.003842	0.418073	0.764757	1.650140	0.922147	1.559230	-46.019314	NA	-0.327717	1.038502	0.216502	2.125181	-1.054379	1.495421	0.586551	-2.011041	0.828879	5.963952	1.909268	NA	1.325298	-0.022470	-0.826146	-0.852734	0.128872	-0.322077	-3.576741	-1.697044	2.334923	-1.311457	-1.597290	-0.697487	-4.404241	0.296405	-1.724568	-0.158158	2.521658	-0.209912	NA	-0.113051	NA	0.916556	2.360021	2.010872	1.551618
202203	NA	0.071897	-0.908407	0.579772	-0.335717	-0.067537	-0.914519	0.460916	1.256393	0.186215	0.869384	0.032630	NA	-0.417681	0.076819	-0.460366	-0.244657	0.032465	0.756969	2.129047	0.642939	-0.547137	-1.763254	-1.177646	NA	-1.894339	-0.401140	-2.589791	-1.665078	-0.806791	-0.793096	-1.480329	-1.244692	-1.096372	-0.960619	-1.339336	-1.485924	-0.807996	-1.417892	-1.627780	-1.534142	0.088995	-1.571247	NA	-1.484202	NA	0.623718	0.719788	-0.211767	-0.195446
202204	NA	-1.541044	-1.593062	1.422457	-0.273129	-1.761420	0.215833	1.329540	-2.150028	0.126980	-2.328931	0.052179	NA	-0.095365	-0.912833	-0.213889	0.211383	-2.826396	-0.175111	-3.381722	0.000000	-0.111242	3.324691	-2.222141	NA	0.484269	0.068209	-1.071967	0.738303	0.450629	0.294224	-0.049707	0.030536	1.639640	0.112571	0.195111	0.298014	-0.071251	-0.875882	0.609969	0.181146	-0.282852	0.087847	NA	-3.445825	NA	0.000000	-0.142623	-0.035102	-0.460521
202205	NA	-0.305397	-0.500174	1.964952	2.110841	0.525171	-0.487210	0.904827	-0.770603	1.566863	-0.322944	-0.312938	NA	0.864324	2.443169	1.437357	0.442004	-0.892265	-1.059192	4.172838	-0.717241	-1.099910	0.224026	-0.237217	NA	1.232678	-1.569897	0.292242	-1.341100	-1.086531	-0.042435	-0.842522	-1.303960	-0.288684	-1.806816	-1.351950	-1.114083	0.184820	-0.458887	-1.271046	0.476686	0.088109	-0.878720	NA	-0.785360	NA	1.215882	2.312961	0.393958	1.360315
202206	NA	-1.492936	-1.001419	-0.682016	-0.769543	-0.176466	-1.190865	-2.255140	-1.025601	-0.663440	-1.780365	-3.007181	NA	-1.455299	-0.747612	-2.566657	-2.251371	-2.192744	-0.464337	-1.934374	-2.252519	-0.553796	-1.435090	-1.643945	NA	-2.411276	-2.335358	-2.019114	-1.870610	-0.563988	-1.878609	-2.068821	-0.940736	-0.896080	-2.042964	-1.784398	-1.688862	-1.894195	-0.719822	-2.581643	-1.360115	-1.697039	-0.762954	NA	-0.852413	NA	0.924146	-0.765203	-0.131060	-1.915281

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-Country-ETF-daily.csv"