Part 1: Deep Learning and Long-Term Investing

By: John Alberg and Michael Seckler

Seventy-five years ago, Benjamin Graham – the father of security analysis – wrote that in the short run the market behaves like a voting machine, but over the long run it more closely resembles a weighing machine. Graham’s point was that fear, greed, and other emotions (the voting machine) can drive short-term market fluctuations which in turn cause disconnects between the price and true value of a company’s shares. Over long periods of time, however, the weighing machine kicks in as a company’s fundamentals ultimately cause the value and market price of its shares to converge.

Traditionally, investors have performed long-term fundamental analysis by studying the income statements, balance sheets, and other publicly available information about a company’s operations. Then, they use this information in the context of the company’s market value to make an informed decision about its prospects as a long-term investment.

The automation of this process, systematic value investing, has become possible with the emergence of high-quality data on company fundamentals and the ever-increasing computational power available to researchers. The attractiveness of an automated approach is that rigorous statistical techniques can be applied to the assessment of thousands of opportunities, and that a systematic process can protect investors from well-documented behavioral biases that often detract from investment performance.

In a recent investor letter, we described why deep learning, and in particular recurrent neural networks, might be well suited to the application of long-term systematic value investing. This is the first in a series of blog posts that describes some of our explorations in this area.

Background

Recent applications of deep learning and recurrent neural networks have resulted in better-than-human performance by computers in many domains. However, there has been very little work in the application of these technologies to investment management. Nonetheless, there are several reasons why deep learning might achieve better results than traditional statistical methods or non-deep machine learning approaches when applied to long-term investing. These reasons include:

Machine learning approaches are typically structured in such a way that the goal is to predict something from a fixed number of inputs. However, in the investment world, the input data typically come in sequences (for example, how a company’s operating results evolve over time), and the distribution of investment outcomes are conditioned by the evolution of those sequences. Recurrent neural networks, which have claimed many successes in recent years, are designed precisely for this type of sequenced data.

In the quantitative investment field, a great deal of effort is put into “factor engineering” – the process of determining which features of a company are most valuable to forecasting its future stock price. Deep learning provides the potential opportunity to let the algorithms discover the features based on raw financial data. That is, the “deep” in deep learning means that successive layers of a model are able to untangle important relationships in a hierarchical way from data as found “in the wild,” and these relationships may be stronger than the ones found via traditional approaches to factor engineering.

Some of the greatest progress in deep learning has been in the area of text processing. This capability opens the door for the possibility of leveraging the enormous corpus of non-structured, qualitative textual data related to companies that can be found in SEC filings, news reports, blog posts, social media, and earnings transcripts.

In our research, we are exploring these types of opportunities created by deep learning.