US Investor Flow of Funds into Investment Classes (Bonds, Equities etc)

core

Files Size Format Created Updated License Source
3 57kB csv zip 1 month ago PDDL-1.0 Investment Company Institute (ICI)
Monthly net new cash flow by US investors into various mutual fund investment classes (equities, bonds etc). Statistics come from the Investment Company Institute (ICI). Data Data comes from the data provided on the ICI Statistics pages, in particular: Summary: Estimated Long-Term Mutual Fund read more
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Data Files

File Description Size Last changed Download Other formats
monthly [csv] All figures are in millions of USD 6kB monthly [csv] monthly [json] (19kB)
weekly [csv] All figures are in millions of USD 2kB weekly [csv] weekly [json] (8kB)
datapackage_zip [zip] Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 15kB datapackage_zip [zip]

monthly  

This is a preview version. There might be more data in the original version.

Field information

Field Name Order Type (Format) Description
Date 1 date (%Y-%m-%d)
Total Equity 2 integer
Domestic Equity 3 integer
World Equity 4 integer
Hybrid 5 integer
Total Bond 6 integer
Taxable Bond 7 integer
Municipal Bond 8 integer
Total 9 integer

weekly  

This is a preview version. There might be more data in the original version.

Field information

Field Name Order Type (Format) Description
Date 1 date (%Y-%m-%d)
Total Equity 2 integer
Domestic Equity 3 integer
World Equity 4 integer
Hybrid 5 integer
Total Bond 6 integer
Taxable Bond 7 integer
Municipal Bond 8 integer
Total 9 integer

datapackage_zip  

This is a preview version. There might be more data in the original version.

Read me

Monthly net new cash flow by US investors into various mutual fund investment classes (equities, bonds etc). Statistics come from the Investment Company Institute (ICI).

Data

Data comes from the data provided on the ICI Statistics pages, in particular:

  • Summary: Estimated Long-Term Mutual Fund Flows Data (xls)

Notes for Long-Term Mutual Fund Flows Data:

  • All figures are (nominal) millions of US dollars (USD)
  • Weekly cash flows are estimates based on reporting covering 98 percent of industry assets, while monthly flows are actual numbers as reported in ICI’s "Trends in Mutual Fund Investing."

Preparation

Run the python script:

Install the requirements

pip install -r scripts/requirements.txt

Now run the script

python scripts/process.py

Import into your tool

If you are using R here's how to get the data you want quickly loaded:

install.packages("jsonlite")
library("jsonlite")

json_file <- "http://datahub.io/core/investor-flow-of-funds-us/datapackage.json"
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

# access csv file by the index starting from 1
path_to_file = json_data$resources$path[1][1]
data <- read.csv(url(path_to_file))
print(data)

In order to work with Data Packages in Pandas you need to install the Frictionless Data data package library and the pandas extension:

pip install datapackage
pip install jsontableschema-pandas

To get the data run following code:

import datapackage

data_url = "http://datahub.io/core/investor-flow-of-funds-us/datapackage.json"

# to load Data Package into storage
storage = datapackage.push_datapackage(data_url, 'pandas')

# data frames available (corresponding to data files in original dataset)
storage.buckets

# you can access datasets inside storage, e.g. the first one:
storage[storage.buckets[0]]

For Python, first install the `datapackage` library (all the datasets on DataHub are Data Packages):

pip install datapackage

To get Data Package into your Python environment, run following code:

from datapackage import Package

package = Package('http://datahub.io/core/investor-flow-of-funds-us/datapackage.json')

# get list of resources:
resources = package.descriptor['resources']
resourceList = [resources[x]['name'] for x in range(0, len(resources))]
print(resourceList)

data = package.resources[0].read()
print(data)

If you are using JavaScript, please, follow instructions below:

Install data.js module using npm:

  $ npm install data.js

Once the package is installed, use the following code snippet:

const {Dataset} = require('data.js')

const path = 'http://datahub.io/core/investor-flow-of-funds-us/datapackage.json'

// We're using self-invoking function here as we want to use async-await syntax:
(async () => {
  const dataset = await Dataset.load(path)

  // Get the first data file in this dataset
  const file = dataset.resources[0]
  // Get a raw stream
  const stream = await file.stream()
  // entire file as a buffer (be careful with large files!)
  const buffer = await file.buffer
})()

Install the datapackage library created specially for Ruby language using gem:

gem install datapackage

Now get the dataset and read the data:

require 'datapackage'

path = 'http://datahub.io/core/investor-flow-of-funds-us/datapackage.json'

package = DataPackage::Package.new(path)
# So package variable contains metadata. You can see it:
puts package

# Read data itself:
resource = package.resources[0]
data = resource.read
puts data
Datapackage.json