Sampling-related Issues: Biases

Data-mining bias refers to the errors that arise from overuse of the same or related data.

To investigate: out-of-sample test.

Intergenerational data mining involves using information developed by previous researchers using a dataset to guide current research using the same or a related dataset.

Sample selection bias. Data availability leads to certain assets being excluded from the analysis. (eg. survivorship bias)

Look-ahead bias. A test uses information that was not available on the test date.

Time-period bias. A test design is based on a time period that may make the results time-period specific.

[Practice Problems] A report on long-term stock returns focused exclusively on all currently publicly traded firms in an industry is most likely susceptible to:

A.       look-ahead bias.

B.        survivorship bias.

C.        intergenerational data mining.

[Solutions] B

A report that uses a current list of stocks does not account for firms that failed, merged, or otherwise disappeared from the public equity market in previous years. As a consequence, the report is biased. This type of bias is known as survivorship bias.