WorldQuant's quest for formulaic alphas

We introduced WorldQuant in Chapter 1, Machine Learning for Trading – From Idea to Execution, as part of a trend toward crowd-sourcing investment strategies. WorldQuant maintains a virtual research center where quants worldwide compete to identify alphas. These alphas are trading signals in the form of computational expressions that help predict price movements, just like the common factors described in the previous section.

These formulaic alphas translate the mechanism to extract the signal from data into code, and they can be developed and tested individually with the goal to integrate their information into a broader automated strategy (Tulchinsky 2019). As stated repeatedly throughout this book, mining for signals in large datasets is prone to multiple testing bias and false discoveries. Regardless of these important caveats, this approach represents a modern alternative to the more conventional features presented in the...