Dynamic View of Intrinsic Value – Combining Value Investing with Machine Learning (Quantamental)


Meson Capital 1Q17 Letter, May 8, 2017


  • No secret that it has been extremely difficult to find appealing value investments in the current environment
  • In order to stay fully invested, many investors have to reach outside of their value discipline and many have been returning capital to investors
  • Map of 2016 US election outcome approximates the boundary of the amplify vs displace effect
    • Automation and AI are accelerating this trend
    • In 1990, the Big 3 in Detroit (GM, Ford, Chrysler) had a market cap of $65B with 1.2 million employees
    • Today, the Big 3 in Silicon Valley (Apple, Google, Facebook) have a market cap of $1.5 trillion with 190,000 people: a 14,500% increase in value per employee
    • If you thought you could avoid this by excluding tech from your investment universe, good luck: in 2016, there were more $1B+ acquisitions of tech startups by non-tech companies

Today is not a storm to wait out but the best opportunity set with the right skillset

  • Time like 2009 are not just to be weathered through, they are to be taken advantage of aggressively
  • We believe in the importance of preparing for the once-a-decade ‘downpour’ so strongly that we have invested considerable effort and exposure in our ‘ark building’
  • Number of investors who outperform with the traditional value-focused stock-picking methodology has dwindled as the world has changed in deeply structural ways
  • Fundamental change driver is technological and to a lesser degree, demographic – the monetary policy changes are mostly symptomatic, rather than casual as is commonly misunderstood
  • In my view, the clearest and most predictable trend in capitalism are the exponential price/performance curves in technology: Moore’s Law for integrated circuits has now spilled over to solar panels, batteries, sensors, drones, robotics, etc
  • We concentrate our long investments on businesses positioned strategically to benefit from declining input costs and capability advancements
  • My argument is that 1) the market has become more competitive as more parties analyze and make investment decisions in a traditional value investing sense and 2) the business environment itself changes faster and in a more exponential way making ‘intrinsic value’ much harder to assess
    • Understanding the ‘dynamic intrinsic value’ of a company requires real business depth of understanding how things evolve in a market and within the company

Machine Learning Changes the Game

  • New technology has allowed for 1) the ability to work with unstructured data that can be gathered less expensively and 2) nonlinear predictive models
  • Now it’s possible to build a machine learning investing system with a small group of engineers using open source software and low cost cloud computing
    • Add to the formula an activist investor to help direct what data factors are important to predict how a company will perform in the future
  • Our approach, although utilizing computational tools, is fundamentally the same business-focused approach we have been deploying for years employing a long term perspective (“Quantamental”)

Meson Gravity Approach: A Dynamic View of Intrinsic Value

  • Even if you could determine intrinsic value today precisely, what if the company changes?
  • Static view of intrinsic value is so incomplete that it can be a dangerous concept; we can estimate a range for intrinsic values but that too is limited when framed in linear thinking tied to today
  • Core value of businesses is increasingly the intangible component and decreasingly the stack of bricks or factory floor with easy to observe GAAP accounting metrics
  • Core tenets of our machine learning enabled strategy include:
    • Long term fundamental approach over a year or longer, not weeks or months with short term traders
    • Focus on deeper casual factors in our data – people, business quality over time, and supply/demand dynamics in an industry
    • We short for the long term: economic gravity always wins in the end with low quality businesses run by self-motivated people
  • Requirements to follow these tenets and the barriers to entry are:
    • Long term approach requires non-linear models to make investment decisions
    • Knowing what data to look at requires real domain expertise as a long term investor and the ability to translate that into the same language that a machine can understand
    • To maintain long term conviction in shorts, diversification is required so a short going against you doesn’t need to be reacted against adversely solely due to price action
    • Why short at all? 1) Despite the rising index, performance is mostly from the big winners and most stocks perform worse than T-bills; 2) Great businesses are almost always doing something new and harder to predict whereas failure is much easier to predict

A Challenging Market Environment for Long-Only Stock Pickers, Better than Ever for Entrepreneurs

  • On the long side, it has never been better to be an entrepreneurial business builder; cost of growth capital is as low as any point in history and the amplification effects of technology make human willpower and intelligence more economically potent than ever
  • On the short side, increased competition and technological disruption is making the lifecycle of poorly run companies shorter than ever
Image Source: Quantavista Labs

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