Jeremiah Green

Jeremiah Green

Accounting | Stock Market Returns

For many in the population, the U.S. stock market might as well be in a different language. The ever-evolving intricacies and complexities found woven into Wall Street err more on the confusion side of things. Sometimes, even those fluent in stock language can get lost in translation.

There are hundreds, if not thousands, of factors that can influence a company’s stock returns, but how do you figure out which ones to focus on?

This is the ambitious question that Jeremiah Green, Ph.D., and his co-authors decided to tackle. Around the same time that they started their paper, “The Characteristics that Provide Independent Information about Average U.S. Monthly Stock Returns,” there were a number of academics and investment practitioners noting a proliferation of research, investment managers, and “how to” books claiming that something-or-another predicted returns. Most of the resulting questions seemed to be skeptical — “Really, can all of this be true?”

Harnessing the power of using data analytics, Green’s research surveyed a large number of factors that academics and investors have used to predict the cross-section of stock returns – it was essentially a big data collection and replication exercise.

The data showed that operating skeptically is healthy, as a large number of factors that are supposed to predict returns don’t actually seem to work. Yet, even though that large number of factors do not work, businesses are still better off incorporating a broader set of factors into profit prediction. Optimistically, this research will help investors be more skeptical and provide a starting point for them to incorporate valuable big data into their prediction models which will grow shareholder value, advance the world’s prosperity, and create positive social impact.

As a whole, this is a very active research area where some of the world’s top minds are trying to solve very difficult problems. The problems present technological and methodological questions, statistical questions, and sometimes even philosophical questions. While it is unlikely that all of these questions will be answered any time soon, data analytic tools and top-notch research will slowly help the majority of the population translate the U.S. stock market from confusion to clarity.

Learn more about Green:

Published Work

The Characteristics that Provide Independent Information about Average U.S. Monthly Stock Returns