# Measuring Information Coefficient and Sharpe Ratio

I have been looking through / using Quantopians' Alphalens library to measure/create new factors, and I had some questions in evaluating the credibility of the factor.

This is what I have:

• I have created a factor that ranks stocks from ranking 1 to 10.
• Each factor is supposed to have a predictive power for 5-day total return for groups of stocks in each score.

These are the statistics that I have gathered using above score's return over 3-year period.

• factor with score = 1

• number of data points = 12,659

• number of trading days: 850

• average return over 5 days: 0.0026

• standard deviation of return over 5 days: 0.06

• factor with score = 10

• number of data points = 11,397

• number of trading days: 850

• average return over 5 days: -0.01

• standard deviation of return over 5 days: 0.058

From the above, I am not sure what is the correct way of measuring the effectiveness of the factor. I know that t-statistics can be calculated using:

sqrt[number_of_samples] * (average return over horizon) / (sample standard deviation over horizon)


However, from the strategy's sharpe-ration perspective, below are used:

sqrt[252 trading days / 5 - because we are talking about 5 day return?] * (average return over 5 days) / (sample standard deviation over 5 days).


Is this correct way of evaluating the signal in above example?

To summarize, my question is the confusion coming in the process of identifying / validating the effectiveness of the factor that I have constructed.