Money Talks: Can Math Really Crack the Stock Market? (Encore)
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Deep Dive
Why did the Fama-French data set become so important in the business law community?
The Fama-French data set became crucial in the business law community because it is used to calculate damages in securities litigation. When a company makes a mistake and the stock price drops, the Fama-French factors provide an alternate universe version of how the stock would have performed, which helps in estimating the damages.
Why did the numbers in the Fama-French data set change over time?
The numbers in the Fama-French data set changed over time due to various factors, including underlying data changes from data providers, arbitrary choices in constructing the data set, and methodological adjustments. These changes, while small individually, can compound over time and significantly affect long-term returns.
Why did the changes in the Fama-French data set raise suspicion?
The changes in the Fama-French data set raised suspicion because they consistently improved the returns of the value factor, which Fama and French are famous for promoting as outperforming the market. This consistent improvement seemed fishy and led to questions about the reliability and transparency of the data.
How does the concept of p-hacking relate to the Fama-French model?
P-hacking relates to the Fama-French model because it involves finding statistically significant results in data by making multiple comparisons or adjustments. The garden of forking paths concept suggests that researchers can unintentionally find and promote factors that appear significant but are actually just noise. This raises questions about the robustness of the value factor identified by Fama and French.
What is the significance of Dimensional Fund Advisors in this story?
Dimensional Fund Advisors is significant because it manages $677 billion in assets and is founded by a former student of Eugene Fama. Dimensional charges high fees and bases its investment strategy on the Fama-French factors, particularly the value factor. The firm's success and the reliability of its data set are central to the controversy.
Why did Fama and French's response to the noisy factors paper seem dismissive?
Fama and French's response to the noisy factors paper seemed dismissive because they wrote an 18-page paper explaining their methodology without directly acknowledging or naming the noisy factors paper. They ended with a warning about the unreliability of asset pricing models, suggesting a lack of concern and a middle-finger attitude towards the criticism.
What is the broader implication of the changes in the Fama-French data set?
The broader implication of the changes in the Fama-French data set is that it raises questions about the reliability of financial models and the potential for bias or noise in empirical finance. It highlights the need for transparency and rigorous methods in constructing and maintaining financial data sets, especially those used in high-stakes decisions like litigation and investment.
- Fama-French model for stock market prediction
- Numbers in Fama-French model keep shifting
- Cultural schism between practitioners and academics regarding the model
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