What is Mutual Funds Data? Definition, Sources & Datasets to buy

Mutual Funds Data Explained

Examples of mutual funds data attributes include fund names, ticker symbols, net asset values (NAV), expense ratios, and portfolio holdings. This data is used by investors, financial advisors, and researchers to analyze and compare mutual funds, make investment decisions, and monitor fund performance. In this page, you’ll find the best data sources for mutual fund data, including mutual fund databases, mutual fund data providers, and mutual fund investor databases.

How is Mutual 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.