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Mutual funds data provides intelligence on combinations of securities. It is used by investors and traders in the securities markets to identify profitable portfolios and alpha generation opportunities. Datarade helps you access the best mutual funds data feeds and APIs. Learn more →
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US Mutual Funds - Corporate Actions
by Exchange Data International
1 country covered
Reduce risk and ensure you have updated information on US Mutual Funds
US Mutual Funds - Reference Data
by Exchange Data International
1 country covered
Empowering investors with the highest quality reference data
100K Financial security identification number (Exchange Symbol, CUSIP, WKN, SEDOL)
100% End-of-day data released as exchanges and providers do (no lags)
9 countries covered
Intraday datasets and analytics for products trading on European exchanges. Our service provides datasets and measures based on iNAV/iCVs, prices, creations/...
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Barchart
Based in USA
Barchart is a leading provider of market data and services to the global financial, media, and commodity industries. Our solutions makes it easy for you to ...
Institutional Shareholder Services
Based in USA
Founded in 1985, Institutional Shareholder Services (“ISS”) empowers investors and companies by providing high-quality data, analytics, and insight. With nea...
Exchange Data International
Based in United Kingdom
With EDI you get high quality, affordable financial data customized to precisely fit your operational requirements.
ISO9001
Certified
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The Ultimate Guide to Mutual Funds Data 2021

Learn everything about Mutual Funds Data. Understand data sources, popular use cases, and data quality.

What is Mutual Funds Data?

Mutual funds data is information on investment vehicles structured of funds pooled together by numerous investors, in a bid to invest the pooled resources into securities such as bonds, stocks, and money market instruments. Mutual funds data provides information on portfolios and portfolio performances, particularly historical returns, to existing investors, as well as projected yields on returns for potential investors.

How is Mututal Funds Data collected?

Data on mutual funds is usually collected the same way that most financial market data is collected. Various sources are involved in mutual funds data collection, including news aggregators, brokers, investors, traders, securities exchanges and markets, research firms and online services. The information from these sources is recorded and analyzed with a particular focus on the performances of the securities in a mutual fund. There is also the utilization of various tools, methods and measures such as graphs, figures, charts, statistics, news related to securities trading, market analytics, expert opinions, public records (especially information from regulatory instructions).

What are the typical attributes of Mutual Funds Data?

A mutual funds data should include the following:
Day returns: this refers to the change in the fund’s value in the last 24 hours or 2 days. It is updated daily.
Launch date: refers to the day the fund was started. An older fund is of less risk, considering it still exists after a period of time.
Category and sub-category of fund: the category and sub-category of the fund are determined by its main objective for being set up.
Returns 3Y: this is a backward projection of the fund to see how much yield it would have generated three years from the present date.
Risk: the probability of the fund not yielding investment returns.

What is Mutual Fund inflow/outflow?

In the event that a mutual fund or ETF has a higher net inflows, fund managers (mutual funds) are considered to have more cash at their disposal to trade. Mutual funds inflow translates to a rise in the demand for the underlying assets. In contrast, high outflow means that investors have less cash at their disposal, leading to a fall in demand for the underlying assets. In a nutshell, when investors are putting more money into mutual funds, there’s an increase in inflows, reflecting wider investor optimism, while greater outflows tend to show a general mood of apprehension amongst investors.

What is Mutual Funds Data used for?

Mutual funds data is primarily useful in portfolio optimization. Selecting a profitable portfolio that is low in risk can be difficult. With information on risks, returns 3Y, grow ratio, category and sub-category, return and maturity provided by mutual funds data, it is easier to combine securities in a portfolio that yields profitable returns, while at the same time managing risk. Additionally, mutual funds data is essential for making investment decisions, as well as in making stock market predictions. Lastly, for analyzing portfolios, as a stock broker must do, mutual funds data proves extremely useful.

What are the 3 types of Mutual Funds?

The three major types of mutual funds include money market funds, bond funds and stock funds.

• Money market funds – Money market funds are considered the funds with the lowest associated risk, because, by law, they are only able to be invested in standard quality and short term investments that are advanced by the US government, US corporations, and state and local governments. The dividends paid for money market funds are a true indicator of short-term interest rates. Even though they produce smaller returns when compared to other mutual funds, the fact that they are taken on a short term basis significantly reduces risks of loss.

• Bonds funds – Unlike money market funds, bonds funds are not restricted to short-term investments only. The fact that they are can be spread over a longer period of time means that the yields are more likely to be higher even though the risks are also accelerated. Examples include US Treasury and company bonds.

• Stock funds – Also known as equity funds, these mutual funds are valued very highly. Stock funds are the most volatile mutual funds and are considered to pose the highest potential risks for investors because stock prices can rise and fall dramatically depending on various market factors. Stock funds are further classed into growth funds, income funds, index funds and sector funds.

How to calculate risk using Mutual Funds Data?

The information provided by mutual funds data can be used to determine the investment risks that are associated with stocks, bonds and mutual fund portfolios. There are five major indicators of investment risks for mutual funds that include Alpha, Beta, R-squared, Standard Deviation and the Sharpe ratio. As key statistical measures, these five major historical forecasters of investment risk and volatility forms the basic components of Modern Portfolio Theory (MPT). Once the five indicators are determined, they form the basis for MTP which is a standard financial methodology that is applied in the assessment of the performance of equity, fixed-income and mutual fund investments.

Do Mutual Funds have maturity dates?

Unless an investor buys Equity Linked Savings Schemes (ELSS) or Fixed Maturity Plans (FMP), mutual funds do not usually have a maturity date. However, mutual funds lack liquidity. This means that they allow an investor to request that their shares be converted into liquid cash at any given moment. Nonetheless, unlike stocks which trade at any time of the day, in the majority of cases, mutual funds redemption can only take place at close of the trading day.

How can I interpret Mutual Funds Data?

Upon buying mutual funds data from a data marketplace, one of the hurdles that an investor will most likely face is the interpretation of data. The first step to interpretation of mutual funds data is in understanding that they offer a great deal of information pertaining to their portfolio and historical returns to current and potential investors. The most important aspects of the data include:

• A day returns – how much the fund’s value has changed in the last 1 day.
• Launch data – the date when the fund was launched.
• Category – tells you about the fund’s core purpose.
• Growth rating – historical data analysis of the mutual funds.
• Returns 3Y – backdated returns an investor could have earned if they invested three years ago.
• Min SIP amount – the lowest amount of money an investor can inject into the fund that is needed in a monthly basis.

Interpreting mutual funds data usually involves tackling each of these data points separately to derive the insights necessary for decision-making.

What is Mutual Funds Data analysis?

Mutual funds collect money from public investors and use it to acquire other securities that may include stocks or bonds. Therefore, the value of a mutual fund company is tied down on the overall performance of the stocks the company consciously decides to buy. Buying a mutual fund therefore simply means investing in its portfolio performance. Given that a share of a mutual fund involves investments in many differing stocks, there is need for analysis of data to determine the best performing portfolios before the common pool of public funds can be used to buy mutual funds. Therefore, mutual funds data analysis is the primary analysis of the fund’s growth or value, median market cap, rolling returns, standard deviation and a further narrowing down of its portfolio performance by sector, geographical location, or industry. Through proper data analysis of mutual funds, it is possible for investors to highlight a given fund’s attractiveness when compared to others.

What is a Fund screener?

Investors looking to buy mutual funds data online or who are searching for mutual funds data subscriptions services are most likely to encounter mutual funds screeners on countless websites and data trading platforms. These fund screeners give users the opportunity to only select trading instruments that are best suited for a given mutual fund profile. They enable users to sieve out mutual fund data as per market capitalization, price, prospective dividend yield, available volume of funds and the nominal return on investment. Because the pool of mutual funds data is very large, fund screeners enable investors to evaluate thousands of mutual funds within a short space of time. Hence, investors have the opportunity to save time by selecting the funds that do not meet their requirements and separating them from those that meet their user-defined metrics.

How can a user assess the quality of Mutual Funds Data?

Like any other financial market data, mutual funds data must be reliable. Users of this data must know they can rely on its accuracy to make decisions based on mutual funds information. Mutual funds data must also be timely. It must be updated regularly to reflect market conditions so as not to give outdated information. Mutual funds data must also be precise, leaving no room for errors or mistakes, as important investment decisions are to be made based on its information. Lastly, it must be relevant to the user’s desired use case.

Who are the best Mutual Funds Data providers?

Finding the right Mutual Funds Data provider for you really depends on your unique use case and data requirements, including budget and geographical coverage. Popular Mutual Funds Data providers that you might want to buy Mutual Funds Data from are Barchart, Institutional Shareholder Services, Exchange Data International, FACTSET, and Goldbaum.

Where can I buy Mutual Funds Data?

Data providers and vendors listed on Datarade sell Mutual Funds Data products and samples. Popular Mutual Funds Data products and datasets available on our platform are US Mutual Funds - Corporate Actions by Exchange Data International, US Mutual Funds - Reference Data by Exchange Data International, and Intraday Data Analytics for ETF/ETP/ETCs traded on European exchanges - 1 Year by Goldbaum.

How can I get Mutual Funds Data?

You can get Mutual Funds Data via a range of delivery methods - the right one for you depends on your use case. For example, historical Mutual Funds Data is usually available to download in bulk and delivered using an S3 bucket. On the other hand, if your use case is time-critical, you can buy real-time Mutual Funds Data APIs, feeds and streams to download the most up-to-date intelligence.

What are similar data types to Mutual Funds Data?

Mutual Funds Data is similar to Stock Market Data, Alternative Data, ESG Data, Merger & Acquisition Data, and Proprietary Market Data. These data categories are commonly used for Portfolio Optimization and Mutual Funds Data analytics.

What are the most common use cases for Mutual Funds Data?

The top use cases for Mutual Funds Data are Portfolio Optimization.