For the latest release of QuantCon videos, I've created a notebook that explores the predictive nature of value ratios.
This notebook focuses on a concepts presented by Dr. Wesley Gray of AlphaArchitect. Check out his presentation slides and watch his talk before experimenting with the notebook. In his talk, he explores behavioral bias and long term active investing. This notebook takes these concepts and creates a framework for quantifying the differences in short term and long term predictabilities.
Using the Morningstar fundamentals, we can select the cheapest stocks by their valuation ratios. We would like to show the short term predictability of a factor doesn't necessarily match its long term predictability. To do this, we'll create a pipeline factor and use the Factor Tearsheet to view the returns for a valuation ratio over a various time windows.
If the Information Coefficient(IC) taken at 1,5, and 7 days are near zero, then we can say that a valuation ratio doesn’t predict near-term returns. If the IC taken over 1,2, and 3 years is high (|x| >0.5) , we can conclude that (for the equities sampled) a valuation ratio is a good predictor of future returns in the long term. Finally, if short-term IC is near zero, while long-term IC is high, we can conclude that a long term value strategy is more susceptible to short term noise.
The valuation ratio that I focused on is the same that Dr. Gray uses in his presentation: EBITDA/EV. Other ratios are available in the Morningstar fundamental data. In Dr. Gray's presentation, he found that using value ratios are a simple way for long term positive returns, but don't always predict short term returns.
In this notebook, I reevaluate Dr. Gray's findings with a new metric: calculating variable-window information coefficients in order to quantify this valuation ratio's predictive ability of short and long term price movements.
There are a few open questions left to tackle.
1. Is there a ratio or formulation that has a reasonable information coefficient in both the long and short term?
2. What are the minimum and maximum IC values that you can get using a combination of these ratios?
3. What would an algorithm that applied these findings look like?
Add your own valuation factors to the notebook and find out!