Good question. Yes, the Fama-French type factor model works better over long horizons in general. I think, if using monthly data, 15 years could be a good choice. The Q risk model (not Fama-French type) is not designed to capture the influence of these factors in long-term horizon.
About frequency of tradings, let us talk about two relevant cases:
- Intraday trading strategies. The Q risk model is built with daily data, if a strategy is purely intraday trading, I do not think analyzing it with an (EOD) position based performance attribution is a good idea. According to the ideas of strategies, I may try to decompose the daily algo returns to the risk factors directly, then use return-based performance attribution to check. We will have a lecture related to return-based performance attribution soon.
- Daily rebalanced strategies. Rebalancing daily does not mean it is not possible to hold the same assets in one month. Suppose I have 100 assets in my portfolio on day 1, after rebalancing on day 2, I may still have 90 same assets. Even if all of the 100 assets are replaced on day 2, it is ok, since we have the daily factor exposures and factor returns are computed in advance. The performance attribution is just combining them based on the assets’ weights in your portfolio for each day.
https://www.quantopian.com/lectures/risk-constrained-portfolio-optimization and https://www.quantopian.com/posts/introduction-to-the-quantopian-risk-model-in-research are good references about the position based performance attribution and how to access assets’ daily factor exposures in Q research platform.
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