How to measure mutual fund risks – five statistical tools

Are you a mutual fund investor? Do you want to invest in mutual fund schemes while remaining aware of the associated risks? Read this article which describes the five statistical tools which are used to understand the risks associated with mutual fund schemes.

Many investors rely upon mutual funds for short-term and long-term investments. Generally it has been seen that on long-term basis, mutual funds give better return than that of various fixed rate investments like provident funds. However, mutual funds carry a higher amount of risk. So it is imperative for an investor to measure risks associated with mutual funds. There are five statistical tools to check the risk associated with mutual fund investments. These five tools are Standard Deviation, Alpha, Beta, Sharpe ratio and R-Squared. In this article, we will discuss about these five statistical tools.

Standard Deviation- a very common statistical tool

Standard Deviation is a very well known statistical tool. This is widely known to the students of Statistics and Mathematics. This tool can be utilized for checking the volatility of particular mutual fund(s). It measures the dispersion of data from the mean. The more the data is spread apart, the higher the difference is from the norm. In respect of mutual funds, the Standard Deviation tells the investor how much the return is deviating from the expected return based on the fund's historical performance.

Alpha to measure risk-adjusted return

Alpha is a very important tool to measure the risk adjusted return of Mutual Fund. It is a measure of a mutual fund's performance on a risk- adjusted basis. The excess return of the investment relative to the return of the benchmark index is its Alpha. For example, a positive Alpha of 1.0 means the fund has outperformed its benchmark index by 1%. Similarly a negative Alpha of 2.0 indicates under-performance by 2%. So the more positive the Alpha is, the more better it is.

Beta-a measure of volatility

Beta is a measure of the volatility, or systematic risk, of a mutual fund in comparison to the market as a whole. Beta is calculated using regression analysis, and it is the tendency of an investment's return to respond to swings in the market. By definition, the market has a Beta of 1.0. Individual security and portfolio values are measured according to how they deviate from the market. A Beta of 1.0 indicates that the investment's price will move exactly with the market. A Beta of less than 1.0 indicates that the investment will be less volatile than the market; and, a Beta of more than 1.0 indicates that the fund's price will be more volatile than the market.

Sharpe ratio developed by William Sharpe

Sharpe ratio has been developed by the Nobel laureate economist, William Sharpe. This ratio measures risk-adjusted performance. It is calculated by subtracting the risk-free of return from the rate of return for an investment and dividing the result by the investment's standard deviation of its return. The Sharpe ratio tells the investors whether an investment's returns are due to specific investment decisions, or, the result of excess risk taken by the fund manager. The greater is a fund's Sharpe ratio, the better is its risk-adjusted performance.

R-squared-another important statistical tool

R-Squared is a statistical measure that represents the percentage of fund portfolios or security movements that can be explained by the movements of the benchmark index. Mutual fund investors should generally avoid actively managed mutual funds with high R-Squared ratios.

Concluding comments

The mutual fund analysts and experienced investors use these five statistical tools to measure the risk associated with a mutual fund scheme. These ratios are available in various websites which deal with mutual fund schemes. However, new investors must remember that all the data regarding these five tools are some ratios. So, for the sake objective comparison, the corresponding ratios of similar nature of funds must be taken into account.

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