Information coefficient

Information coefficient is the correlation between predicted and actual stock returns, sometimes used to measure the value of a financial analyst. It is a measure of the correlation between expected and actual returns. The IC is used internally within a firm to judge the performance of individual financial forecasters. The IC is measured on a scale between 0 to 1, with 1 indicating no difference between expected and actual returns. An IC of 1.0 indicates a perfect linear relationship between predicted and actual returns, while an IC of 0.0 indicates no linear relationship.

Extrapolative statistical models

Extrapolative statistical models are models that attempts to use past trends in data in order to predict future trends. This may be used in any number of business or non-business situations. They are the models that apply a formula to historical data and project results for a future period. Such models include the simple linear trend model, the simple exponential model, and the simple autoregressive model.

The technical analysts commonly use extrapolative statistical models in order to predict future prices of securities. This can be quite important in the futures and option markets.

Decile rank

Decile rank is a rating of performance over time. It is rated on a scale of 1-10, where 1 is best and 10 is worst. For performance of mutual funds, 1 indicates that a mutual fund's return was in the top 10% of funds being compared, while 3 means the return was in the top 30%.

Statistical hypothesis test

A one-sided test is a statistical hypothesis test in which the values for which we can reject the null hypothesis, H0 are located entirely in one tail of the probability distribution. In other words, the critical region for a one-sided test is the set of values less than the critical value of the test, or the set of values greater than the critical value of the test.

A two-sided test is a statistical hypothesis test in which the values for which we can reject the null hypothesis, H0, are located in both tails of the probability distribution.

Kurtosis

Kurtosis is a measure of the peakedness of the probability distribution of a real-valued random variable. It is the fourth central movement of a distribution. The first three movements are mean, standard deviation, and skewness.  It measures the distribution’s peakedness and the thickness of its tails.

Higher kurtosis means more of the variance is the result of infrequent extreme deviations, as opposed to frequent modestly sized deviations. Leptokurtosis, or positive excess kurtosis, indicates a distribution that is more peaked at the center and has fatter than normal tails. Platykurtosis, or negative excess kurtosis, indicates a relatively flatter top and thinner tails.