Euclidean letter for the first quarter ended March 31, 2016; titled, “Deep Learning & Value Investing.”

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In recent letters, we showed the merits of adhering to simple forms of value investing over time. In particular, we highlighted the potential that value strategies have demonstrated following periods resembling the past few years — when value investing has underperformed the broad market and more speculative forms of investing.

Now we bring our focus back to Euclidean’s approach to systematic value investing, which was formed using machine learning. Machine learning is, perhaps most simply, the discipline of teaching machines (i.e., computers) how to do things through experience, in a manner that resembles how people learn. We have written about machine learning and how it relates to Euclidean in previous letters.

Over the past several years there have been many advances in machine learning, particularly within the field of “deep learning.” Now, more than ever, computers can uncover complex and fruitful patterns that lay hidden in highly complex environments. Deep learning is behind the much talked about achievements of self-driving cars, image recognition technology that performs better than humans, impressive language translation, and voice recognition.

It is also behind the recent achievement of a computer named AlphaGo beating the world’s best players at the ancient game of Go. This development is interesting because, unlike chess, where each move affords about 40 options, Go has up to 200. The permutation of outcomes quickly compounds to a bewildering range of choices — more than the total number of atoms in the entire observable universe. [1] Thus, mastering Go cannot be achieved by a computer through brute force as it can for games like checkers and chess, but rather requires the pattern recognition and intuitive skills many have felt to be an exclusively human capability. Here’s a comment from a January article in Nature that describes deep learning’s step forward. [2]

“The IBM chess computer Deep Blue, which famously beat grandmaster Garry Kasparov in 1997, was explicitly programmed to win at the game. But AlphaGo was not preprogrammed to play Go: rather, it learned using a general-purpose algorithm that allowed it to interpret the game’s patterns…”

The very same technologies that have made this advance possible have opened potentially fruitful avenues of exploration for us to further develop Euclidean’s investment process.

Euclidean: Flashback To 1994 - From John’s Perspective

Before we started our first company and began to ponder questions regarding business quality and valuation, I (John) got immersed in the world of machine learning. My first job after college was with a crack team of fifteen engineers at the consulting firm Booz, Allen, and Hamilton doing machine learning projects for several government agencies. One project was an early attempt at applying machine learning to computer vision: we sought to train models to classify the types of vehicles found in satellite images. The goal was to distinguish hostile vehicles like tanks from benign vehicles like school buses.

One quality of how we approached machine learning then, that has resembled our work at Euclidean since, is that we needed to provide a great deal context about the information from which we wanted our computers to learn. For example, our computer vision project required us to extract features of images so that they could be fed into the machine learning environment. There were, however, many different features of these images that we could use. Choosing the right ones required a lot of effort, and also made the process inherently subject to our biases.

Though our efforts were successful, there’s a good chance our assessments could have been better. It was ultimately only summary detail and not the image itself that our algorithms had access to during the learning process. Maybe this summary detail missed important qualities about the images or the context in which they resided. Perhaps a more powerful approach – if it had been possible – would have been simply to feed raw satellite images to our machines. Then, through trial and error and recurrent learning, perhaps they could teach themselves to find what was important.

This ability to work with raw information, instead of information that is heavily pre-processed, is in fact one of the important advantages of deep learning. It is part of the reason deep-learning-powered image recognition programs are now performing better than humans in many contexts.

Euclidean - Machine Learning through Today

Euclidean’s ambition has always been to identify the best methods for distinguishing between companies that are likely to be good investments and those that are likely to disappoint. In our initial efforts to do this using machine learning, we separated companies from the past into two groups. Those that outperformed the market made up group 1 and those that underperformed populated group 2. This step was pretty easy, with the caveat that we needed access to good data that mitigates survivorship bias and lets us “see” companies and market prices from the past as they actually existed.

The hard work began when we strove to find the qualities that provide the most information regarding whether a company’s shares are likely to be a winning investment. A good metaphor for describing this step would be to imagine the way that an exceptional investor might evaluate a potential opportunity. After becoming familiar with how a company serves customers, manages expenses, and deploys capital, this investor would be equipped to compare the company with his experiences involving similar investments from the past. To the extent that those analogs, or “comparables,” in the past had done well, his confidence in the new opportunity would be high, and the converse would also be true.

To execute against this metaphor in the context of machine learning, our challenge was to determine the lens that would be most useful in comparing current opportunities with ones from the past. There are, after all, many different qualities that could form the basis for that comparison.

Would earnings yield prove better than price to earnings when evaluating whether a company is inexpensive? What about price-to-book or price-to-sales? How has a company’s historical rate of growth tended to relate to its intrinsic value? What would prove to be the best way to consider one-time charges when evaluating earnings? What do measures such as debt-to-equity, return-on-capital, and gross profitability tell us about companies’ relative quality? Should we look at just the last twelve months of data on a given company, or should we look at how it has evolved over the last several years, or even since the company’s inception? Which of these measures are best, which are redundant? And so on, and so on.

Traditional machine learning techniques require us to put a great deal of energy into these types of questions. If we don’t get the input factors right, machine learning doesn’t work. Thus, we devoted much of our energy to this area of factor selection, and we believe Euclidean operates today focused on qualities that provide a strong signal regarding when a good company is offered at an attractive price.

Perhaps, though, there are other ways of looking at companies that could be more fruitful? Maybe the tools of deep-learning could open up new areas of analysis that have been previously outside of our grasp?

New opportunities for Euclidean

Over the next few years we plan to

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