Stats: Determining significance level in problem

In summary, the strong form of the efficient market hypothesis states that studying financial information about stocks is a waste of time because stock prices already reflect all relevant information. However, a study of 450 stocks found that only 8% showed price movements that could be explained by this hypothesis. The significance level for accepting this hypothesis is 50%, which may seem too large. However, there is a larger issue of the Joint Hypothesis Problem, where it is unclear whether the model used to test the hypothesis is correct. Additionally, the strong form of the efficient market hypothesis is not widely accepted, as it would mean that no one could make a profit trading on insider information. Overall, the question about significance testing and the Efficient Market Hypothesis
  • #1
physicslady123
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TL;DR Summary
The answer is 50% but why isn't it 5%?
Question:
In finance, the strong form of the efficient market hypothesis states that studying financial information about stocks is a waste of time since all public and private information that might affect the stock price is already reflected in the price of the stock. However, a study of 450 stocks found that only about 8% had price movements that could be accounted for in this way. At what significance level could you accept the strong form of the efficient-market hypothesis?

I know that significance levels are usually 5%, 1%, or 10% (as confidence levels are 95%, 99%, and 90%). So, why is the answer 50%? Isn't this too large of a significance level?
 
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  • #2
I don't think the question has a meaningful answer.
Giving up and saying "what if it's even?" might be an answer but I don't think it is a good one.
 
  • #3
physicslady123 said:
Summary:: The answer is 50% but why isn't it 5%?

However, a study of 450 stocks found that only about 8% had price movements that could be accounted for in this way.
Hint: what is the p-value of this data set?
 
  • #4
I'm confused by the question too. If the strong market hypothesis is that 100% of stocks should "have price movements that could be accounted for this way", and only 8% of them do, isn't it just wrong? Where does a significance level even come into play here?
 
  • #5
You have a more fundamental issue - the Joint Hypothesis Problem. Who is to say that your model is correct? To test the EMH you need a model of what ‘efficient’ values should be. However if you find violations of this model, to whatever significance level you choose, you don't know whether the EMH or your model is incorrect.

however, nobody believes the strong form of the EMH, which states that stock prices reflect even non-public information. If that was true, no one could make an economic profit trading on insider information
 
  • #6
I think there is some confusion that this is a question about the Efficient Market Hypothesis; it isn't, it is a (rather poorly constructed) question on significance testing and it belongs in the homework section.
 
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1. What is a significance level in statistics?

A significance level in statistics is a threshold that is used to determine whether the results of a study or experiment are statistically significant. It is typically represented by the symbol α and is usually set at 0.05 or 0.01. This means that if the p-value of a study is less than the significance level, the results are considered statistically significant and the null hypothesis can be rejected.

2. How is the significance level chosen?

The significance level is typically chosen before conducting the study or experiment. It is often set at 0.05, which means that there is a 5% chance of falsely rejecting the null hypothesis. However, the significance level can also be adjusted based on the specific goals and context of the study. For example, a lower significance level of 0.01 may be chosen for studies with high stakes or large sample sizes.

3. What is the relationship between significance level and p-value?

The significance level and p-value are both measures of the likelihood of obtaining a result by chance. The significance level is chosen before conducting the study, while the p-value is calculated after the study is completed. If the p-value is less than the significance level, it means that the results are statistically significant and the null hypothesis can be rejected.

4. Can the significance level be changed after the study is conducted?

Changing the significance level after the study is conducted is generally not recommended. This is because it can lead to bias and make the results less reliable. It is important to determine the significance level before conducting the study and stick to it throughout the analysis process.

5. How does sample size affect the significance level?

The significance level is not directly affected by sample size. However, a larger sample size can result in a lower p-value, which may make the results statistically significant even if the significance level is set at a lower threshold. This is because a larger sample size can increase the power of the study, making it more likely to detect a true effect.

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