GM Asset Management and Martingale: Low Volatility Strategies
Answer the question and the related reading all in the file.Questions:
1. Describe the Low Volatility strategy proposed by Martingale.
2. What are the results in the empirical finance literature that support (or not) the
idea behind this strategy ? Present a brief review of this empirical literature.
3. What could be the theoretical justification for the result that idiosyncratic
volatility is priced and affects expected excess returns ?
a. Is it possible to justify this strategy using neoclassical rational models
with no frictions (CAPM)?
b. Is it possible to justify these empirical results (thus this strategy) using
economic models with frictions? Be as precise as possible in describing
these frictions.
c. Is it possible to justify these empirical results on the basis of (irrational)
behavioral biases? If so, which one?
4. Many of the performance numbers shown by Martingale Asset Management to
the General Motors’s pension fund are based on back-tests. A hardcore believer in more traditional models may argue : «This is a good example of data-dredging (or data-snooping). It is always possible to find a pattern in a small sample of historical data, this does not imply that this patter will show up again in the future!» Discuss the merit of this argument in the context of this specific strategy.
From Wiki : «Data dredging (data fishing, data snooping, equation fitting) is the use of data mining to uncover relationships in data. The process of data mining involves automatically testing huge numbers of hypotheses about a single data set by exhaustively searching for combinations of variables that might show a correlation. When large numbers of tests are performed, some produce false results, hence 5% of randomly chosen hypotheses turn out to be significant at the 5% level, 1% turn out to be significant at the 1% significance level, and so on, by chance alone. When enough hypotheses are tested, it is virtually certain that some falsely appear statistically significant, since almost every data set with any degree of randomness is likely to contain some spurious correlations. If they are not cautious, researchers using data mining techniques can be easily misled by these apparently significant results.”
5. If you were a potential investor, would you invest your own capital in this fund/strategy? Why and why not ?