Can Machines Produce Better Earnings Forecasts Than Humans?

We received many good questions about Euclidean’s last letter, where we discussed how one can use the tools of deep learning to predict companies’ future earnings. The letter referenced a paper we published recently highlighting the opportunity to systematically make earnings predictions that may improve the performance of commercially applied investment strategies that currently rely on backward-looking valuation ratios.

One interesting question we received was, “How far away are we from when machine learning can be used to deliver better earnings predictions than those made by professional Wall-Street analysts?” That is, when will the machines be performing at a level that is demonstrably as good as or even better than human?

earnings forecast

In this letter, we talk about where we are in developing an ability to forecast earnings using machine learning. To explain our motivation for this project, we discuss our progress in the context of how Euclidean has used machine learning to explore various questions, whose answers form the foundation of our investment process.
The Original Question — What Works in Investing?

We have always been interested in whether there are timeless lessons regarding equity investing. That is, are there persistent methods for evaluating whether one company is intrinsically more valuable than another? And, does history provide a guide regarding the prices at which a company with a given set of qualities can be soundly purchased when seeking to compound wealth over long periods?

Our mental model for exploring these questions has been the analogy of how exceptional investors acquire skill through experience. By making successful and poor investments, and learning about investment history, exceptional investors can build a rich foundation of experience from which they make informed decisions about new opportunities.

Euclidean’s focus has been on using machine learning to emulate this process of building investment skill through experience. We do this by enabling our systems to examine thousands of companies and their investment outcomes across a variety of market cycles. By informing our investment process with many more distinct examples than any individual could experience in a lifetime, our goal is to find persistent patterns that lead to successful security selection. We aspire to quantify not only history’s highest-level lessons, which may be widely appreciated by other investors, but also to uncover deep and fruitful patterns that sit in the blind spots of investors’ accepted wisdom.

These goals have us navigating, what has been and remains, an incredible journey. During Euclidean’s tenure, the tools for uncovering meaningful relationships in large datasets have become increasingly sophisticated. At the same time, much more data has become available for analysis, while our experience with what it takes to successfully apply a commercial systematic strategy ever deepens. Thus, the ways Euclidean operates continue to evolve as we seek to capitalize on new insights that may improve our investment process.

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