8.1 Data

The Flow-Percentage Daily Fixed-Income Strategy developed by EPFR is based on the percentage flow into fixed-income asset classes via funds reporting Premium Daily flows. In this section, the reader will gain an understanding of the aggregations which can be used to create the signal as well as the methodology behind daily percentage flow calculations.

8.1.1 Aggregations

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

This strategy requires:

Premium Daily Fixed-Income Fund Flow Data. To ensure that we can effectively use the signal to trade at market open, we will use 7 AM EST. cut of premium daily data.
Funds with a geographic mandate confined to the country or region of interest (e.g. for the US fixed-income market, we look only at funds that have a mandate to invest in the United States).

8.1.2 Daily Percentage Flow

To begin calculating the daily percentage flow, we start with our subset of Premium Daily Fixed-Income Funds which have a geographic mandate confined to our country or region of interest. To calculate the total dollar flow into an asset class, sum the flows into the funds investing in that asset class.

\[\text{Total Flow}_{a,t} = \sum^{N}_{i=m} \text{Flow}_{i,a,t}\] Where:

\(\text{Total Flow}\) = the total dollar flow into an asset class \(a\), across all funds in our universe \(i\), for day \(t\)

To normalize this figure, we repeat the process across the same fund’s Assets under Management (AuM) to get the total AuM held in the asset class.

\[\text{Total AuM}_{a,t} = \sum^{N}_{i=m} \text{AuM}_{i,a,t}\] Where:

\(\text{Total AuM}\) = the total assets held in an asset class \(a\), across all funds in our universe \(i\), for day \(t\)

Finally, to get the daily percentage flow for an asset class, we divide the total dollar flow by the total AuM held in an asset class.

\[\text{Percentage Flow}_{a,t} = 100 \times \frac{\text{Total Flow}_{a,t}}{{\text{Total AuM}_{a,t}}}\] Where:

\(\text{Percentage Flow}\) = the scaled flow as a percentage into an asset class \(a\), across all funds in our universe \(i\), for day \(t\)

We repeat this across all different asset classes for the entire history.

8.1.3 Aggregate Flow File

Users may create flow percentages for their desired asset class aggregations using the methodology described in the previous section.

Users also have the option to use the readily available Premium Daily Flow-Percentage Fixed-Income Strategy file EPFR provides, which are available in the user’s EPFR FTP connection under the PremiumDaily Fund Flows Strategies folder. The strategy file contains aggregate percentage flow data for the following list of fixed-income asset classes categorized by duration, corporate/sovereign, quality, and style:

Duration - Short:	Duration - Intermediate:	Duration - Long:	Quality:	Style:
Short Term Bond	Intermediate Term	Long Term Bond	High Yield	Bank Loan
Short Term Corporate	Intermediate Term Corporate	Long Term Corporate		Inflation Protected
Short Term Government	Intermediate Term Government	Long Term Government		Mortgage Backed
				Total Return

For this demonstration, we will be using the file FloPctBond-US-PremDaily700.csv, which can be downloaded from the user’s FTP under the folder PremiumDailyFundFlows/Strategies/PremDaily and can be stored in the user’s local folder under EPFR/PremDaily.

Below shows a snippet of what the files contain:

PremiumDailyFundFlows/Strategies/PremDaily/FloPctBond-US-PremDaily700.csv

	Bank Loan	High Yield	Inflation Protected	Intermediate Term	Intermediate Term Corporate	Intermediate Term Government	Long Term Bond	Long Term Corporate	Long Term Government	Mortgage Backed	Short Term Bond	Short Term Corporate	Short Term Government	Total Return
20230201	0.1836310	0.1929212	-0.0971693	0.1186617	0.1130243	0.0855356	0.0447080	0.5962806	-0.9581940	0.2402161	0.0780067	-0.0463523	-0.1463537	0.2008935
20230202	-0.0117408	0.6353015	-0.0680494	0.0026641	0.1649227	0.4094269	0.0202825	-0.3418080	0.3193136	0.0834837	-0.0966520	-0.1456450	0.1065072	0.0113280
20230203	-0.0218654	-0.3839466	-0.2486920	0.0231541	0.0617302	-0.1828118	0.1510689	0.8521460	0.4402582	0.1288934	-0.0615493	-0.0494237	-0.4343966	0.0953736
20230206	-0.0317222	-0.3295717	-0.1642024	-0.1078678	-0.0865859	0.0710580	0.0611091	0.0547083	-0.0022680	0.0876904	-0.0906320	0.0930718	-0.2099392	0.1593086
20230207	-0.1420902	0.1629358	-0.1588086	0.1042206	-0.0393643	0.2269358	-0.0117878	-0.9237513	0.4944916	0.0642555	-0.1202199	-0.2440847	0.1651146	0.0368734
20230208	-0.0583384	-0.0487813	0.0058634	0.1161807	0.0193067	0.0545304	0.2836733	0.3901266	0.0504965	0.0531934	-0.0663551	0.0101742	-0.2204929	0.1127592

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

flow.file <- "C:\\EPFR\\PremDaily\\FloPctBond-US-PremDaily700.csv"

8.1.4 Return File

The return file for this strategy should contain open to open daily fixed-income ETF returns for the respective asset classes contained in the flow file, over the period of time the user wants to backtest. For convenience, save the path to the return file you choose to use as ret.file. Example shown below:

ret.file <- "C:\\EPFR\\returns\\USBond_ETFS.txt"

The following ETFs were used to measure the daily performance for each of the US Bond asset classes.

Category	Ticker	Name
Bank Loan	BKLN	Invesco Senior Loan ETF
High Yield	JNK	SPDR Bloomberg Barclays High Yield Bond ETF
Inflation Protected	TIP	iShares TIPS Bond ETF
Intermediate Term	IUSB	iShares Core Total USD Bond Market ETF
Intermediate Term Corporate	IGIB	iShares Intermediate-Term Corporate Bond ETF
Intermediate Term Government	IEI	iShares 3-7 Year Treasury Bond ETF
Long Term Bond	AGG	iShares Core U.S. Aggregate Bond ETF
Long Term Corporate	IGLB	iShares Long-Term Corporate Bond ETF
Long Term Government	TLH	iShares 10-20 Year Treasury Bond ETF
Mortgage Backed	MBB	iShares MBS ETF
Short Term Bond	SHY	iShares 1-3 Year Treasury Bond ETF
Short Term Corporate	IGSB	iShares Short-Term Corporate Bond ETF
Short Term Government	SHV	iShares Short Treasury Bond ETF
Total Return	BND	Vanguard Total Bond Market ETF

To conduct our analysis we were able to use library(quantmod) to extract historical daily returns for each of these ETFs from www.yahoofinance.com.

library(quantmod)
library(dplyr)

etf_tickers <- data.frame(
  "BKLN" = "Bank Loan",
  "JNK" = "High Yield", 
  "TIP" = "Inflation Protected", 
  "IUSB" = "Intermediate Term", 
  "IGIB" = "Intermediate Term Corporate", 
  "IEI" = "Intermediate Term Government",
  "AGG" = "Long Term Bond",
  "IGLB" = "Long Term Corporate", 
  "TLH" = "Long Term Government",
  "MBB" = "Mortgage Backed", 
  "SHY" = "Short Term Bond",
  "IGSB" = "Short Term Corporate", 
  "SHV" = "Short Term Government", 
  "BND" = "Total Return" 
)


etf_data <- NULL

# Loop through each ETF and retrieve the data
for (ticker in names(etf_tickers)) {
  
  # Fetch data from Yahoo Finance
  etf <- getSymbols.yahoo(ticker, auto.assign = FALSE)
  
  # Extract the "Open" prices
  etf_open_prices <- Op(etf)
  
  # Calculate daily returns (open to open)
  etf_returns <- 100 * (etf_open_prices / lag(etf_open_prices) - 1)
  names(etf_returns) <- etf_tickers[ticker]
  
  # Add the returns to the data frame
  if (is.null(etf_data)) {
    etf_data <- data.frame(Date = index(etf_returns), etf_returns, check.names = F)
  } else {
    etf_data <- merge(etf_data, data.frame(Date = index(etf_returns), etf_returns, check.names = F), by = "Date", all.x = TRUE)
  }
}

  # format date
  etf_data$Date <- txt.replace(etf_data$Date, "-", "")
    
  # write output to desired destination
    mat.write(etf_data, ret.file, "\t", F)

Below shows a snippet of what the output file contains:

	Bank Loan	High Yield	Inflation Protected	Intermediate Term	Intermediate Term Corporate	Intermediate Term Government	Long Term Bond	Long Term Corporate	Long Term Government	Mortgage Backed	Short Term Bond	Short Term Corporate	Short Term Government	Total Return
20230201	0.3800472	0.0859865	0.1102358	-0.0215528	0.1358162	0.0512012	0.0299509	0.2807440	0.2610926	-0.0417241	-0.1223373	-0.0395344	-0.3083028	0.0539678
20230202	0.0473272	1.8041240	0.6331389	0.9271244	1.0463106	0.6141259	0.8483865	1.7917116	1.1892385	0.8765484	0.2572257	0.4745930	0.0363843	0.8629981
20230203	0.0946095	-0.8122408	-0.9483003	-1.0681478	-1.1121799	-0.7968824	-1.0787846	-2.1268790	-1.8529605	-0.9310040	-0.3054368	-0.4329880	0.0000000	-1.0427893
20230206	-0.0472601	-0.7763434	-0.7640562	-0.6262166	-0.6399031	-0.7007347	-0.6403196	-0.7118791	-0.9964225	-1.0128366	-0.3063726	-0.4348710	-0.0181890	-0.6889956
20230207	0.0472824	-0.3322671	0.0092713	-0.1521071	-0.2732303	-0.1118737	-0.1611077	-0.4716981	-0.2825106	-0.0738393	-0.0245810	-0.0198498	0.0181923	-0.1632468
20230208	-0.1417802	0.3118625	0.4359533	0.0000000	-0.0782722	0.0000000	-0.0100879	-0.1706164	-0.2301855	0.2111231	0.0000000	0.0198537	0.0181821	0.0000000