Python for Finance, 3rd Edition

Chapter 22. Efficient Markets and Hypothesis Testing

A Note for Early Release Readers

With Early Release ebooks, you get books in their earliest form—the author’s raw and unedited content as they write—so you can take advantage of these technologies long before the official release of these titles.

This will be the 22nd chapter of the final book. Please note that the GitHub repo will be made active later on.

If you’d like to be actively involved in reviewing and commenting on this draft, please reach out to the editor at [e-mail].

The Efficient Market Hypothesis (EMH) provides a baseline for thinking about predictability in financial markets. This chapter treats EMH as a working null hypothesis and shows how hypothesis testing connects research signals, asset management strategies, and algorithmic trading ideas. It links the asset management arc in Chapter 17 through Chapter 21 to the trading workflows developed in the rest of this part.

This chapter establishes EMH as the default benchmark for return predictability and frames quantitative research as a sequence of hypothesis tests against that benchmark. It links probabilistic models of returns to practical diagnostics, such as autocorrelation checks, regression tests, and Granger causality for signals, that inform whether a signal deserves a place in a portfolio or trading strategy. The examples assume a working Python environment with numpy, pandas, and statsmodels, familiarity with return calculations, time series ...