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Burr Q2: Adding Machine Learning to a Latticework of Mental Models

By VW Staff. Originally published at ValueWalk.

Welcome to your Q2 2017 quarterly letter from Burr Capital LLC.

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During the first half of 2017, Burr Capital LLC aggregate accounts posted 23.9% compared to 9.3% for the S&P 500. Our objective is to achieve long-term performance superior to that of the S&P 500. Since Jan ‘16, the annual compounded rate for Burr Capital LLC is 32.3%, which is almost 18 points higher than the 14.4% for the S&P 500. Please remember that past performance may not be indicative of future results and individual accounts may vary significantly (please see the Disclosures section at the end of this letter for more information). Exhibit 1 provides a summary of results to date:

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Burr Capital

  1. Concentrated portfolio: Our portfolio, with 18-20 stocks and five-year holding periods, requires only 3-4 high quality investment ideas per year. In contrast, the institutional imperative to own hundreds of stocks, in the name of diversification, can lead to a large number of low quality investments to fill the pipeline.
  2. High active share: We don’t own the tech giants—Facebook, Amazon, Apple, Google, and Microsoft—which accounted for about 40% returns for the S&P 500 this year. According to a Wall Street Journal report, 80% of actively managed funds own two or more of these stocks. We believe that owning a portfolio that differs materially from the S&P 500 index can lead to long-term outperformance, whereas a fund that “hugs” the index underperforms, after fees and commissions.
  3. Options hedging: When markets are elevated, as they are today, hedging the portfolio is imperative to protect capital. Options have an elegant symmetry that provide certain benefits, such as the ability to define risk and lower borrow costs, over shorting stocks.

Portfolio Review

As a reminder, your portfolio is structured in three parts:

  • A growth sub-portfolio, where we hope to achieve a 15-25% return over 3-5 years.
  • An income sub-portfolio, with high-yielding preferred and common equity that produces income and stabilizes the portfolio.
  • Options to hedge the portfolio.

We mainly invest in US domestic businesses where we’re comfortable with the financial reporting and management incentives, and when required, we can pick up the phone or hop on a plane to meet with management. We see no need today to venture to other locations to find good investments.

Noteworthy contributors to performance in the second quarter were our investments in Liberty Interactive (QVCA) and CommerceHub (CHUBK).

  • QVC is a wonderful recurring cash flow shopping-as-entertainment media business with a loyal consumer base and quirky entrepreneurial suppliers (Shark Tank anyone?) trading at an attractive valuation. Shareholder-friendly management continues to aggressively buy back shares, effectively increasing our proportional ownership in the business.
  • CommerceHub is an asset-light arms dealer in the e-commerce supply chain with no direct comparable firms and a massive untapped addressable market. If the business isn’t acquired, it could grow to multiples of its current size.

Detractors to performance were Discover Financial (DFS) and our oil investment.

  • Discover Financial is the Rodney Dangerfield of the credit card industry. It gets no respect even though Discover cards have nearly the same US merchant acceptance coverage as Visa/MasterCard. They also charge lower fees to merchants and offer superior rewards to consumers. Discover has a strong balance sheet, the shares are attractively priced and management continues to buy backshares with its free cash flow, effectively increasing our proportional ownership in the business. Discover is also a beneficiary of rising interest rates and domestic tax reform.
  • Someone I greatly respect said the key to being a good investor is when you’re wrong, which you’ll invariably be, you quickly own up to your mistakes and cut your losses. We made an ill-timed investment in oil, described in our previous letter, which has cost us around 1% in performance this year. Energy has been a difficult area to invest in over the last 3-4 years and may remain that way for a while. For now, we hold on to our investment to collect the income, which lowers our cost basis, and plan to sell this loser by year-end to harvest the tax loss.

Adding Machine Learning (ML) to a Latticework of Mental Models

ML was the topic du jour in the last quarter and inspired this section. NVIDIA CEO Jensen Huang gave a powerful two-hour (unscripted?) keynote address at the GPU Technology Conference. Please watch it, if you haven’t done so, and follow up with Stanford University Prof. Fei-Fei Li’s computer vision talks on teaching computers to understand pictures. It’s an exciting time to be a data scientist.

Why do fundamental investors fear machines?

A recent WSJ article featured a debate on machines versus humans. A fundamental investor argued that “Brains Are More Reliable Than Machines” and a quantitative hedge fund manager claimed “Quants Are Better Than the Brains.”

The fundamental investor wrote “investment success involves untangling behavioral problems through slow, hard, qualitative analysis.” Let’s unpack this claim. Business value is determined by its underlying cash flows. Stock prices can be distorted by behavioral biases in the short term. Without these biases, value and price would never diverge. Ben Graham, the father of value investing, said in the short run the market is a “voting machine” (i.e., a beauty contest) and in the long run a “weighing machine.” Stock prices tend to converge with business value over time. So a machine trained to derive business value and wait for a large divergence between value and price to buy/sell could become a successful fundamental investor without a need to untangle “behavioral problems.” There’s also no requirement that any such analysis be “slow” or “hard.” The fundamental investor goes on to claim “great investors don’t require great quantities of information.” This seems to fly in the face of everything we know about great investors who all have one thing in common—they’re information sponges (Warren Buffett spends 80% of his day reading!).

The quantitative manager argued that “use of the scientific method helps us combat a wide range of harmful but common cognitive and emotional biases” and “the availability of massive amounts of data and cutting edge technology only magnifies the power of the scientifc approach.” The kind of information he refers to (e.g., tracking satellite data, web traffic, sensors) is great for short-term trading. But what’s missing today is a large enough dataset of great long-term investments (such as Amazon, Apple, Google, Coca Cola, and American Express) to train a machine. According to research by Prof. Hendrik Bessembinder of Arizona State, less than 1.1% of all stocks in the period 1926-2015, created three-quarters of the stock market’s cumulative dollar gains relative to cash in that period. Think about that for a minute. ML needs data, lots of it. But the great investments are the outliers where the data is sparse. In the world of investing, we aren’t in Lake Wobegon, yet.

Undoubtedly, in time, as we gather more data, fundamental investing will also be conquered by ML. Till then, fundamental investors and machines will continue to operate in orthogonal planes with no overlap, creating large white spaces for opportunisitic investors to thrive.

How did we get here?

Artificial intelligence (AI) research was born in 1956 at Dartmouth. Machine learning (ML), a path to AI, came into

The post Burr Q2: Adding Machine Learning to a Latticework of Mental Models appeared first on ValueWalk.

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