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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.
Sampling-related Issues: Biases:Sampling-related Issues:Data-mining:biasLook-ahead[PracticeA. look-aheadbias.
Sampling:sampling,Samplinggroups[PracticeC.Then
Sampling-related Issues: Biases:Sampling-related Issues:Data-mining:biasLook-ahead[PracticeA. look-aheadbias.
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