Fundamentals such as price to earnings ratio and price to book ratios have often been used in alpha factors that represent the ratio of market price to intrinsic value. We could use these fundamental factors by themselves to indicate whether a stock is underpriced or overpriced. One thing to be aware of though when dealing with fundamentals is that the data change much less frequently than price. Fundamental values come from financial statements which, in the United States, are only updated quarterly. This means that the turnover for fundamental Alphas will be likely much lower than it is for price data-driven Alphas. Recall that this can be a positive because lower turnover means less trading cost but it also could be a negative. We won’t be as responsive as the data does not arrive frequently. As a general rule, fundamental Alphas have high capacity but have lower Sharpe ratio than compared to their price data-driven cousins. Some firms embrace this as it allows them to deploy much larger assets for clients. Other firms though can be wary of using fundamentally driven Alphas. One middle ground is to use fundamental data as a conditioning factor along with price-driven data or to use a fundamental factor with low weight in a combined Alpha vector that includes more responsive Alphas. In this case, since higher idiosyncratic volatility can indicate instances of higher mispricing, we can combine iVol with a fundamental factor. We can think of iVol as a conditioning factor since by itself it doesn’t indicate whether to go in the long or short direction. This is an important idea, the conditional information doesn’t need to be an alpha factor on its own. The conditioning iVol factor can potentially be used to enhance the signal of any other factor. Perhaps you can try this out in your own work.