I finished rereading The Laws of Simplicity. It is a short book that can be finished in one sitting, yet it is full of good ideas that take some time to internalize. I try and use these concepts with all of my research, but it is not always easy. Attempts to add alpha seem to be getting more and more complex with a lot of moving parts. The idea of simplicity is to eliminate the clutter and focus on core drivers, the more that must be estimated, the more that is likely to go wrong or subject to estimation error.
The idea of trend-following is one of the simplest strategies that works. We find that momentum also works. The idea is simple, yet implementation can grow in complexity with risk management, positioning and exception analysis. The job of the research is to ask the question, "Is this feature really needed?", or "Can this model be made simpler?".
Below are the ten laws of simplicity by Maeda. Do a walkthrough of any model and ask if it can be adjusted to be simpler.
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