Roger Templeton, an analyst for Bridgetown Capital Management, is studying past market data to identify risk factors that produce anomalous returns. He tests monthly data on each of 60 financial and economic variables over a 15-year period to find which ones are related to stock index returns. Based on this research,
Templeton identifies three variables that show statistically significant relationships with equity returns. He presents his results to Bridgetown's managers and recommends implementing a trading program based on changes in these three variables. What is the most likely reason why Bridgetown's management should be skeptical of the anomalies Templeton has identified? The results suffer from:
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A. B. C.Explanation
The most likely reason why Bridgetown's management should be skeptical of the anomalies identified by Templeton is that the results suffer from data mining bias (option A). Data mining bias refers to the potential for false discoveries or spurious relationships when conducting numerous tests on a dataset.
In this case, Templeton tested 60 financial and economic variables over a 15-year period to find those that are related to stock index returns. By conducting multiple tests, there is a higher chance of finding statistically significant relationships by random chance alone. The more tests conducted, the greater the likelihood of uncovering relationships that appear significant but are actually just coincidental.
When conducting extensive tests on a large dataset, it is essential to account for the possibility of chance correlations. Without accounting for this, there is a risk of overfitting the data, where the identified relationships may not hold up in future data or in different market conditions. Essentially, if enough variables are tested, some of them will likely exhibit statistically significant relationships purely due to chance.
Therefore, Bridgetown's management should be skeptical of the anomalies identified by Templeton because the results may not represent true relationships that can be exploited for investment purposes. Without further validation and confirmation through out-of-sample testing or additional robust analysis, the identified anomalies may not hold up in the real world and could lead to poor investment decisions.
Survivorship bias (option B) refers to the potential bias that arises when only considering the sample of assets that have survived until the present time. It is not directly applicable to the given scenario as Templeton is analyzing variables rather than assets or investments.
Small sample bias (option C) refers to the potential bias that arises when drawing conclusions from a small sample size that may not be representative of the overall population. While it is not explicitly mentioned in the scenario, it is plausible that the sample size used by Templeton could be small, introducing some degree of bias. However, data mining bias is a more directly relevant concern in this case.