Value Investing

Meson Capital Q3 – The Natural Evolution to Machine Learning & Artificial Intelligence

Meson Capital Partners letter to investors for the third quarter ended September 30, 2017.

Dear Partner,

For the quarter, Meson Capital LP’s performance was 16.9% vs. indices of 2.0% HFRI Equity Market Neutral Index, 5.7% Russell 2000 and 4.5% S&P 500. Shorts contributed 1.2% net of borrow fees while longs added 15.7% for the quarter. Our monthly-liquidity spinout fund, Meson Gravity LP was launched August 1 and after a profitable first couple weeks, performed as expected during a sharp melt-up led by the lowest quality stocks. The Russell 2000 advanced 30 nearly consecutive days in a row beginning Aug 21, notching an 11% advance in a short period. These gravity-defying bursts occasionally happen and are not concerning to us long or even medium term. We discuss below how we continue to improve our strategy to deal with a wider range of market environments. Note below we also updated our HFRI benchmark to the Equity market neutral index to reflect the market neutral long/short positioning of both funds.

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Meson Capital Partners

Major updates during the quarter included the closing of Sevcon’s sale to BorgWarner, launching spinout fund Meson Gravity LP, and two new full time hires to the team as we institutionalize the firm. Sevcon was sold for $22.00 per share less than 4 years after I joined the board when it traded for $5.00 per share. We celebrated this excellent outcome for several minutes because we have been too busy with an even more exciting opportunity. We just closed our investment into our Sevcon electrification “sequel” investment – Software Motor Co. “SMC”. SMC has simultaneously the most efficient, most reliable, and least expensive electric motor in the world and is focused on the HVAC market today. Ultimately has broad applications which we discussed in our Q2 letter. I have assumed the role of Executive Chairman at the company and look forward to working with the excellent and growing team to drive growth.

Greenlight Q3 Letter: Bubble Basket

Meson Gravity LP was created to provide monthly liquidity for institutional investors with this requirement and does not contain any concentrated positions. Meson Capital LP has a 2-year rolling lockup (1/8th per quarter) to align with its ability to take up to 25% positions in less liquid investments. I expect Meson Capital LP should perform somewhat better over time and effectively serves as my family office. This comes at the cost of being less scalable and requiring liquidity restrictions due to the concentrated active positions. I expect both funds to perform similarly well in the event of a stock market decline as they are both positioned market neutral with short exposure being nearly identical as of October. Both funds are uncorrelated with the stock market, other hedge funds or with any other asset class that I am aware of.

Regarding the market environment, the ‘Buffett-metric’ US market cap to GDP stands at 138%, eclipsed in history only for 3 brief months at the 2000 bubble peak where it reached 145% briefly. The volatility index (VIX, aka the “fear index”) also remains at historic lows near 9% - the long term average is over 20%. Retail investor margin debt also has reached new highs. The S&P 500 has not had a 3% drawdown in over 18 months now – a streak not seen since 1929. We wish long-only investors, particularly those with margin debt, the best of wishes at their seemingly never-ending party that we have declined to attend. We have instead been busy working hard on investments that seem to us inevitable regardless of short term market gyrations. We are pleased with our performance this year so far and Meson Gravity LP performed as expected during a rare melt-up rally. We look forward to being able to demonstrate the robustness of our strategy when the environment (finally) becomes more challenging for investors.

This last quarter saw some truly frothy (and entertaining) examples during the small cap melt-up rally of Aug-Sept. A number of obviously worthless companies eclipsed a $1bb market valuation during the period including a defunct and worthless Chinese real estate development that uplisted from the pink sheets to the NASDAQ and a pre-revenue, pre-any-assets-at-all (!), ev-drivetrain company founded by a cocaine trafficker with a retail crowdfunded IPO. A zero gross margin cybersecurity firm filed an 8-K announcing their own stock was likely “worthless” as part of a potential restructuring saw that same stock rise 600% and trade over $100mm of volume (!!). As I said earlier – this was a unique 6 week period even given the context of broadly high valuations today. The bizarre and temporary mania has already begun to reverse as economic gravity re-enters the market.

Our Owner Operator Approach and Asset Duration Alignment

I treat Meson Capital like my family office to manage my entire net worth for the long term, not as a typical investment product with an aim of sales traction satisfying an in vogue investor demand. Since founding in 2009, my goal has been to optimize for continuous learning and to build upon timeless and universal first principles of investing and business. Investors earn excess profits (“alpha”) as a consequence of being both correct and non-consensus – or put another way, from having a competitive advantage that others cannot easily copy. Until this year, our resources to execute our strategies have been extremely limited and have consisted merely of myself, occasionally one analyst, and self-made software tools. Despite these limitations, we have compounded 8.2% net to investors since 2014 while positioned market neutral, which is about 5% per year in excess of the much better resourced hedge funds that constitute the benchmark. Today we have 5 full time people (including myself) and highly advanced modern software and computing tools. I believe we can do much better going forward.

Our approach is sometimes hard to explain to investors expecting to check a cleaner “strategy box” in their taxonomy of strategies. If I had to frame it like that, I would say our strategy is to check the “competitive advantage” box when we invest. We have focused on public equities to date as we know this world well but also have private equity investments when appropriate. Investors flock to private equity more than hedge funds during market declines because of PE’s less onerous drawdown reporting requirements where bizarrely: the inability to withdraw capital provides comfort (out of sight out of mind…). But we think our structure at Meson Capital LP combining value-added longs with our diversified and profitable short book achieves the best of both worlds and will do better over the full market cycle. In addition, we created Meson Gravity LP to provide an avenue for institutional investors who require monthly liquidity because of their own stakeholders.

Female Led Startup Designed For Small Investors, Attracts Eye Of Big Institutions

The Natural Evolution to Machine Learning & Artificial Intelligence

Philosophical Quiz:

1) How does an investor learn and become “better” over time?

2) What does it even mean to be a “better” investor?

Let’s take the second question first since it’s easier – as Warren Buffett says, there are no “called strikes” in investing. You don’t need to have an opinion on every stock, you just need to have (valid) confidence in the ones you pick. It ain't what you don't know that gets you into trouble. It's what you know for sure that just ain't so… –Mark Twain. Buffett calls this the “circle of competence” and highlights that the important thing isn’t how large your circle is but rather knowing where the boundary of it is and then only investing inside that circle. The problem for most, especially today, is that the world has changed a lot and many investors are only confident during a certain set of conditions that do not presently exist (e.g. clearly undervalued stocks with good prospects and good management). To summarize a good #2 answer: “The ability to validly define and assess confidence in a wide range of different investment situations.”

Back to the harder question #1, and warning: this will go deep.

Joe v0.0

Let’s start with a v0 model with a hypothetical investor and let’s call him Joe, because Blade Runner 2049... Joe wakes up one day knowing zero about investing and opens today’s newspaper. He picks a company on the front page and looks up the stock: Company XYZ. He researches XYZ intensely – reads all its past SEC financial filings, looks at the historical stock chart, looks through the valuation multiples it trades at today and has traded at in the past, etc. Buffett says “accounting is the language of business” and Joe will learn to read the English language in those filings and conference calls later in v3.0.

Based on this financial data he has now acquired about XYZ, he forms an opinion about its prospect as an investment and decides to buy some shares. Then he waits.

One year later, XYZ stock is up 10%. But S&P is up 12% during that year. While Joe has learned something, it’s clearly not a lot. This has also been a somewhat expensive lesson because Joe actually risked capital in order to learn. Our v0 example is a bit like trying to get good at golf by playing once a year and sadly analogous to how most non-professional investors approach things.

Joe v0.1

Let’s take it a step further. Now v0.1 Joe is a voracious reader. Instead of just looking at one stock XYZ, the day he wakes up with zero knowledge of investing, he looks at every stock that trades in the US (except pink sheets because who even trusts the numbers…). He reads through all the financial numbers in every historical 10-K of every company alive today, looks at every valuation multiple, etc. Then for each of the ~5,000 stocks, Joe decides if he likes it as a long or a short (or neither) and constructs a portfolio of all 5,000 stocks (with some being allocation zero where he has no opinion) based on the data he acquired about each company.

Again, Joe waits a year.

After a year goes by, he has 5,000 outcomes – some stocks up, some down, some bankrupt, some acquired… Joe can start really building a framework of what “works” as an investor – at least with respect to what the world was like over the last year, in an up 12% market… And again it was somewhat expensive because he had to risk some capital and time. How many MBA grads does it take to do 5,000 case studies? How much do they cost?

Joe v0.2

Joe v0.2 is a bit wiser. He realizes that Joe v0.1 was onto something by looking at more stocks at a time but is less patient with this waiting idea. So Joe v0.2 goes to the Library of Congress which has all the historical public SEC filings of companies that have existed over time – even if they don’t exist today because they were bought out or went bankrupt. After waking up with zero knowledge of investing, he starts in the 1996 section (when the SEC went electronic with EDGAR) and starts reading 10-K and 10-Q filings for every stock in the US. He’s very careful to note the date on it when it was published & publicly available (Dec 31 reports are usually published by March 15 the following year for example). Then after reading all the numbers in each filing, Joe looks up the historical stock price on the day after the published filing date, the valuation metrics (anything that incorporates the stock price: P/E, P/Book, EV/EBITDA, etc.) and for good measure the stock chart prior to that date, while taking extreme care to not peek into the future after that date! Next, based on the data Joe now knows about each company on that date, he makes a prediction for each stock – will it go up, down or no idea over the next year?

Then, Joe v0.2 turns over the page in the stock market listing for the answer! Where was he correct? Where was Joe wrong? He repeats this deliberate practice, case study method while carefully not biasing himself by viewing any future information and works his way from 1996 up to Oct 23, 2016 (i.e. 1 year ago today since the “correct answer” to the case study is 1 year in the future) and only then does he actually build a portfolio on real money. For those counting: that’s about 7,500 companies (there used to be 10,000 public companies in the US, now only 5,000) * 21 years * 4 filings per year = 588,000 case studies just using quarterly numbers. You could even snapshot the investing world every trading day or more frequently since price changes influence valuation metrics every instant. Each case has 50+ important metrics (revenue, margins, growth, return on assets, valuation, momentum, etc.) just on the GAAP financials and we haven’t even got to the conference calls, industry specific metrics, insider trading form 4’s, analyst coverage, executive and board track records, etc.

Joe v0.2 comes up with a prediction for every case study and importantly – he writes down his confidence in that prediction. Really screaming shorts he’s 90% certain the stocks will go down over the next year, other stocks that seem unclear he puts down 51/49 because he can’t form a strong opinion. As you may have guessed: Joe v0.2 does not have time for a girlfriend, or any friends, or a body. Joe v0.2 is literally a machine whose sole purpose is to learn how to invest.

But now back to #2: how do we know that Joe is learning the right things? How do we know that Joe is becoming a better fundamental investor over time? Unlike short term daily trading strategies – we need to wait at least a year to know if we’re “correct” about any particular fundamental long or short pick. Good thing Joe v0.2 keeps track of his confidence levels for each prediction he ever makes…

Joe v1.0

Now we get to Joe v1.0, a full version upgrade! Joe v1.0 is just like v0.2 but he’s not just a machine but now he’s completely made of software. Because he’s just made of software, we can do a couple things: 1) We can make exact copies of Joe and 2) We can simulate Joe’s ‘world’ and he can’t even tell he’s in the Matrix because it’s 100% realistic. Joe v1.0 wakes up one day and its January 1, 2007. He starts just like Joe v0.2 by going down to the library of congress and reading through all the financial metrics, valuation metrics, etc. for every single US public company since 1996 and does his case study learning method on all of them, all 330,000 of them or so (11y * 7500co’s * 4Qs/yr). Then he takes a look at all the companies that trade today, there are fewer than before because of all the PE buyouts and lack of IPOs lately but still over 6,000 stocks to choose from (there were more stocks then than today…). Based on his experience with all the historical case studies he makes his predictions, including his confidence levels. Joe has honed and calibrated those confidence levels after 330k+ case studies to be verifiably accurate in the past – but what if the world changes in the future?

Now, on Jan 1, 2007, Joe takes his 6,000 * 2 predictions (one confidence probability that it’s a good long and one confidence % that it’s a good short net of borrow costs) and builds a portfolio with his $10mm in his brokerage account of his highest conviction 2% longs and the highest conviction 4% shorts of the market. He’s careful to keep his exposure market neutral and not attempt to put a lot of money into illiquid or impossible to borrow stocks. He puts in his trades for the week, trying to minimize market impact by never being more than 5% of the volume: some get executed fully, some partially because the price moves outside his limit order. The next week Jan 8, 2007 he incorporates what he learned the previous week (he’s got a whole week of new case studies now!) and then rebalances his portfolio to be the best 2% longs and best 4% shorts (with a slight buffer to minimize trading costs and we track his returns on “real” money – in the simulated, Matrix-like world. Nearly 11 “years” go by and Joe v1.0 has compounded money at 22% per year with his worst drawdown being about 15% when there was a really sharp rally that hurt his short book especially.

Remember though, Joe v1.0 is pure software and we built The Matrix and rewind or fast-forward time (though Joe has no memory of anything in the future so he isn’t the wiser) and re-run history as many times as we want. How do we measure how good of an investor Joe v1.0 is? Well watching his entire career in the super realistic Matrix is pretty good and about the most you could ever hope to ask from any investment manager. But let’s take advantage of the tools available and go one step further. Let’s say you meet an investment manager and he’s got great returns but you’re skeptical: he’s had a big chunk of his portfolio in Amazon for the last 10 years! That’s been the best performing stock – was he super smart or just lucky? The simulation of the entire investment process is extremely important to do correctly for a strategy that incorporates shorting. As legendary investor Howard Marks reminds us about the “6 foot tall man who drowned in the 5 foot average depth river” – even having a valid 90% confidence in a short does not protect from it increasing 10X along the way and bankrupting you.

Let’s try this: let’s make 100 parallel Matrix simulated worlds and dropout a random 20% of all companies that ever existed in each one. No two are exactly alike. In one world there is no Coca-Cola case study for Joe to learn from, another one doesn’t have an Amazon stock that Joe can buy because it doesn’t exist and he’s never even heard of it. How is Joe’s performance in each of these slightly different parallel simulated worlds? Well if he just got lucky with his performance: you’d expect the outcomes to be wildly different right? If he in fact is a deeply good fundamental investor then the outcomes should be pretty similar.

The opportunity set is a bit different so it will never be precisely identical but if his long term track record is always between 17% CAGR and 19% CAGR in ALL 100 worlds (always lower than 22% because the entire opportunity set is smaller than the “real world” of course) then that’s a pretty good sign of a robust investment process. To be conservative, let’s take the worst track record out of the 100 parallel Joe v1.0’s as our metric for how good Joe v1.0 as an investor. The only tools and information we gave Joe when he was ‘born’ Jan 1, 2007 in each world was telling him what we thought the important data points were that we as human fundamental investors believe to underlie actual cause-effect relationships in investing. So net margins are in, first letter of CEO’s last name is out. Joe had access to all the case studies to learn on his own and let the actual data speak for itself about what works to predict the next investment.

We also could look more deeply at Joe’s specific predictions to see if they really make sense to us based on the cause-effect relationships in business and investing. Here’s an example of a stock Joe picked: 1) higher than average revenue growth, 2) higher than average margins, 3) lower than average P/E multiple, and 4) really high and growing accounts receivable. Looks like a lot to like here – especially to a simple ‘value’ investor but what about the A/R issue? Joe is short this stock and 70% sure it’s a good short. Interesting – but think about it: is growth good or bad if you’re not getting paid by your customers? Pretty insightful. And literally an impossible relationship to mathematically capture with a linear model like those used by traditional ‘quant’ investors.

Joe v1.0 has one main limitation – he only knows how to read the numbers. And while accounting is the language of business, the performance of a business is driven by the people running it. And the stock price moves up and down with the supply/demand dynamics that are external to it. So while Joe may be a highly optimized fundamental investor, there’s more to the world than just the company specific fundamentals.
Joe v2.0 has access to more than just the Library of Congress, he also has all the insider trading track records, corporate transaction records, track records of every executive and director at every other company they’ve been at before, and all the other structured data that’s publicly available for US public companies over the last 20 years. Joe v2.0 also has ‘brothers’ – actually thousands of them that all work as hard as him but specialize in a certain type of stock (i.e. industry or market segment). We invest based on a confidence weighted algorithm that synthesizes their different predictions.

We’ll introduce Joe v3.0 and our v4.0 platform that incorporates the domain expertise from dozens of experienced fundamental investors in our next letter.

Gravity Platform Today and Roadmap

Many quant investors are very cagey about their specific strategies and highly secretive about their “black boxes”. I lay out the transparency of our process in the above because we take a different, more integrative approach to investing with domain expertise synthesized with software systems. This is very hard to build: there are no shortcuts or simple tricks or formulas as can be the case with simpler traditional linear models used by quants in the past. My view has always been that anything easily copied (even if secret) will be in relatively short order so the only valid approach is to solve truly hard problems and to be organized in a way to continuously improve. The roadmap above is actually quite literal and we implemented “v1.0” as of June 1, we are moving to the v2.0 architecture in November and moving from dollar-market-neutral to beta-market-neutral then as well. A dollar-neutral positioning becomes excessively negatively correlated with the market during sharp moves up or down where our low quality shorts move sharply together as the “tail of the whip” – great for us when the market declines but irritating in melt-ups. Our up front infrastructure investments have been specifically designed to scale such that by just coming into work every day we are continuously experimenting and improving while maintaining the core strategy is automated.

Prior to launching Gravity v1.0 June 1 internally and as a standalone spinout fund August 1, we held 49 short positions. Our track record was that of 49 positions, 35 or 71% were profitable with an average decline of 57% and 14 or 29% were unprofitable with an average increase of 27%. Keep in mind this was during an S&P rising 47%! Based on our simulations we believe our new short book with approximately 200 names will be both much less volatile by virtue of diversification and simultaneously more accurate with an 80% accuracy rate. The alpha available from stock picking is in our opinion much stronger on the short side, even net of borrow costs. Consider that when some company is singular and a remarkably good long – Amazon, Tesla, Netflix… it by definition cannot be following a historical recipe and is creating a totally new case study. If it were, it wouldn’t be so unique and a standout performer.

In contrast – this economic law does not apply to the short side. There is no fundamental force standing in the way of poor management teams making the same predictable mistakes as have played out in the past and these patterns are much sharper to identify.

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

The current environment of high valuations and rapid technology change should provide tailwinds for both sides of our strategy. 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. The inflated valuations across the market allow for attractive short entry points, decreasing upside risk for these companies going forward.

Ever since I founded Meson in 2009, I have taken the path knowing that I would be doing this for 50 years. I have always reinvested our management and incentive fee revenues for the long term to improve our investment process and am tremendously excited for this new chapter. I’m proud to be building the team as we work to institutionalize the firm - we are now up to 5 full time professionals and another 3 part time. The firm has approximately $30mm AUM and has over $1mm of working capital in the firm to ensure long term stability for investors and our employees. I believe we are uniquely positioned to take advantage of the current and future market environment.

I continue to have virtually all of my investable net worth committed alongside investors in the Partnership. Please email me at rmorris@mesoncapital.com or call at 415-322-0486 if you have any questions or are interested in investing. Our minimum investment is $250,000 in Meson Capital LP and $1,000,000 in Meson Gravity LP and we are offering a founder’s class reduced fee structure for our first $50mm of AUM that we are fast approaching. As always, thank you for reading.

Sincerely,

President

Meson Capital Partners, LLC

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