We constantly learn more about investing, build disciplined strategies to capture the best of what we've learned, and share our broad findings with other curious investors.
The idea of applying machine learning to finance and investing has become a popular topic of discussion recently, and for good reason. As its use becomes widespread, machine learning (ML) has the potential to change almost every part of society, both by automating routine activities and by improving performance in difficult activities. In all likelihood, investing will be no exception.
In Factors from Scratch, we showed that value investing works through a re-rating process. The process begins when the market develops an expectation that the earnings of certain companies will decline or grow at depressed rates into the future. The market then prices those companies at a discount relative to their current earnings, turning them into "value stocks." Over the short-term, the market usually ends up being right in its expectations: value stocks usually do go on to experience declines or slowdowns in their earnings, particularly in comparison with the rest of the market. But over the long-term, they usually recover and return to normal growth. When the market prices value stocks, it tends to underestimate the likelihood and extent of their eventual recoveries.
In this quarter’s letter, we describe the more interesting, next stage of factor investing: alpha within factors. This simple idea describes what we’ve pursued at OSAM over the years, and what we continue to pursue through our research agenda today.