What is Alpha Risk?

Alpha risk is the risk in a statistical test that a null hypothesis will be rejected when it is actually true. This is also known as a Type I error. The null hypothesis in a statistical test usually states that there is no difference between the value being tested and a particular number, such as zero or one. When the null hypothesis is rejected, the person conducting the test is saying there is a difference between the tested value and the particular number. Essentially, alpha risk is the risk that a difference will be detected when no difference actually exists. The best way to decrease alpha risk is to increase the size of the sample being tested with the hope that the larger sample will be more representative of the population.

Key Takeaways

  • Alpha risk refers to the risk inherent in rejecting the null hypothesis when it is actually true.
  • This type of risk can also be thought of the danger of assuming a difference exists when there is actually no difference.

Understanding Alpha Risk

An example of alpha risk in finance would be if one wanted to test the hypothesis that the average yearly return on a group of equities was greater than 10%. So the null hypothesis would be if the returns were equal to or less than 10%. In order to test this, one would compile a sample of equity returns over time and set the level of significance. If, after statistically looking at the sample, you determine that the average yearly return is higher than 10%, you would reject the null hypothesis. But in reality, the average return was 6% so you have made a Type I error. The probability that you have made this error in your test is the alpha risk. This alpha risk could lead you to invest in a group of equities when the returns do not actually justify the potential risks.