The principle of parsimony relates to being frugal with resources such as money or the use of computing time. It is closely tied to the principles of simplicity, elegance and efficiency. It also complements the philosophical theory of Occam’s Razor which states that the simplest explanation with the fewest assumptions is often closest to the truth. Whether doing statistical modelling or building trading systems, it would be wise to respect the power of this principle. Parsimonious models or trading systems are often robust, while overly complex models with too many assumptions are not. The difficulty is in telling the difference- which is not obvious even to a talented and experienced developer. The ability to distinguish between parsimony and excess complexity is virtually invisible to almost everyone else.
The backtest is the problem and great distractor in the quest for parsimony. It is like a picture of a beautiful woman that is scantily clothed beside a paragraph of important text– no one is interested in the fine print. A beautiful backtest is admittedly just as satisfying to look at (perhaps even more so for quants!) and can blind us from the details that got us to the end point. And while we all appreciate some good “chart porn”, there are some important questions to consider: What universe did we select and why? Why did we omit certain assets or choose certain parameters over others? Why did we choose one indicator or variable over another-and how do we know it is superior? Why do we trade at a certain rebalancing frequency versus another and is this relevant to the model? Most importantly is: Can I create a trading system with similar results with far fewer assumptions and with less computational power? That should be your goal- to achieve the maximum results with the least number of assumptions and resource usage.
For example, I am well aware than the Minimum Correlation Algorithm does not mathematically optimize the correlation matrix or find the most diversified portfolio. The Minimum Variance Algorithm does not minimize variance either relative to a true MVP solution. But they both use an intuitive and simple method that meets or often exceeds the results of the more complex solutions with less resources, and hence can be considered parsimonious. They are also less dependent on estimates for optimization inputs. Such systems are more likely to work in the uncertain and messy world that we actually live in. Cooking is a hobby of mine, and more recently I have strived to achieve the most with the least, and ensuring that all of my marginal choices of ingredients or differences in traditional technique are actually adding value. There is no point sounding fancy by adding exotic ingredients or using fancy techniques if they don’t change the taste for the better. These give the illusion of expertise to the unsophisticated, but to top chefs judging these dishes on FoodTV they only serve to highlight their deficiencies as cooks. My advice is to work with things that you can understand or intuitively grasp and be very careful when trying newer and more complex methodologies. Master what you can with the tools you have at your disposal instead of reaching for latest and greatest new toy. This may sound strange coming from a blog that was built around offering new ideas and concepts- but rest assured this is some of the best advice you will ever receive.
All of the questions I posed above relating to trading systems are quite material, and many cannot be answered quantitatively. Unfortunately for the quantitatively inclined, the principles of good logic often get lost while decoding proofs, cleaning data, or debugging computer code. Furthermore, the elegance of complex math is like comfort food for those that are highly intelligent and it is easy to forget that the assumptions of these models are a far cry from describing reality. Even for the more experienced developers that are aware of these problems, they may arrive at the wrong approach to system development. The solution is not to avoid making ANY decisions or assumptions (although relying less on specific parameters or universes is desirable for example), but rather to make sensible choices with few assumptions. Another alternative is to build a methodology that directly makes choices quantitatively to create a parsimonious model. Both methods have their strengths and weaknesses.
At the end of the day, there is no point making something more complicated than it needs to be unless the benefits are material. The same is true for the length of time/complexity of the run for the computer program that runs the trading. My brother is a professional hiker and has traversed extreme mountain terrain. Unlike most amateurs, he does not pack everything under the sun that might be useful for his trip. Instead he focuses only on the essentials and on minimizing weight. More importantly, he focuses on planning for what can go wrong and makes his choice of gear and specific hiking route accordingly. The black and white realities of survival bring these questions to the forefront. In contrast the more comfortable and forgiving world of offices and computers make trading system decisions seem almost like a video game. Rest assured, it is not…..