Market Cycles, the Entrepreneurial Experience & Perspectives Gained

{ Euclidean Q4 2014 Letter }


We expect to do well over the long term, but our recent results have been below market averages.  Given this, we discuss our performance in the context of our prior experiences and investment process so that you can remain connected to why we are confident about Euclidean’s future prospects.


  • During the 12-month period ending December 31, 2014, Euclidean returned -8.37% after fees to investors.  During this same period, the S&P 500’s Total Return (that is, the index return including dividends reinvested) was 13.69%.
  • During the year, we exited 16 positions that we held for an average of 22 months.  The median total return on these positions was -0.59%.  Since launching Euclidean, we have exited a total of 98 investments, which we held on average for 19 months, and realized a median total return of 26.02%. [1]
  • Since we launched Euclidean, our process has returned 75.94% net of fees, while the S&P 500’s Total Return has been 83.55%.  These gains translate into annualized compounded net returns of 9.23% and 9.95%, respectively.


Realized Gains [2]

We ended the year with realized gains of $7.15M and unrealized gains of $0.19M.  Reflecting the tax-sensitivity of our process, the realized gains comprise $11.60M in long-term capital gains and -$4.45M in short-term losses. 

Our objective is to purchase good companies at low prices and sell them over time for a profit.  Although the market value of our portfolio fluctuates more broadly, our record of making and exiting profitable investments is presented below.


A Formative Experience

As most of you know, before we started Euclidean, we spent a decade building a software-as-a-service company named Employease.  Our experience with that business informs how we invest, and also how we think about Euclidean’s recent results and future prospects.

When you think of Employease, think of (NYSE: CRM) but for the human resource (HR) applications that a company needs to manage its workforce.  Employease was founded prior to the dot-com boom and acquired five years after the bust.  Just as with most high-growth technology companies at the time, however, it was how we navigated that boom and bust cycle that determined our fate.  These experiences cemented certain principles that we would apply years later to Euclidean. 

During the boom years of 1998–1999, everyone seemed to be making a lot of money — at least on paper.  There was plentiful access to large amounts of venture capital, which led to an abundance of unsustainable behavior.  We had numerous competitors willing to burn millions each month doing uneconomic things in pursuit of growth.  We felt a lot of pressure to follow that example.

At the time, there were also unusual developments in the public markets.  The IPO environment was white hot.  Public company valuations were at all-time highs in relation to earnings, driven in part by investor enthusiasm and soaring margin debt.  And there was this pervasive sense that we had entered a new era with a new economy.  It was fun to think that way. 

Then, in late 1999, we saw the text of a speech Warren Buffett made at the Allen Conference in Sun Valley, Idaho. [3] The gist of his talk was that the stock market had become uncoupled from the economic foundation that determines companies’ long-term values.  He provided a number of reasons for why he felt this was not sustainable, including that interest rates had been falling, corporate profits were high and unlikely to grow further as a percentage of GDP, and market valuations were unusually high both in relation to companies’ earnings and to GDP.  In this context, Buffett believed that investors were unwisely projecting the then current bullish conditions into the future, and he anticipated that their high-expectations were unlikely to be realized.  As recounted in Alice Snowden’s Snowball, Buffet’s “sermonizing on the stock market’s excesses at Sun Valley in 1999 was like preaching chastity in a house of ill repute.”

Soon enough, an unwanted reality began to emerge that those excesses were not sustainable.  The public markets dropped and would take almost a decade to return to their prior peaks.  From the perspective of young technology companies, access to capital evaporated. 

Fortunately, our team was early to reluctantly, but successfully, embrace the idea that perhaps the good times might end.  We remember tough meetings where we cancelled a planned, multi-million dollar marketing spend and the gut wrenching decision to reduce our team from 130 to 80 people.  The process of executing those decisions was painful.  It seared memories into our brains that forever colored how we think about business cycles.

The lasting lesson we took away from this experience is that a company’s worth and longevity is ultimately determined not by its market value, but by its ability to generate cash.  Because we acted more aggressively and earlier than many of our peers in embracing this truth, we — unlike many of our competitors — survived and ultimately delivered a good outcome for our people and our investors. 


What We Believe Is True

After that experience, we thought about how to apply those lessons toward managing the money we earned from selling our company.  We wondered if there were timeless lessons regarding the qualities to seek when selecting potential long-term equity investments.  Should those lessons exist, we felt that it would be wise to embed them into a process that would be protected from the very common human tendency to misinterpret market cycles and erroneously extrapolate recent events into the future. 

We researched these topics for some time.  In historical data on domestic public companies and in the writings of Graham, Buffett, and Shiller, among others, we saw that market prices in the short term are often far more volatile than companies’ financial results, reflecting perhaps the variability of investor sentiment.  Conversely, we saw that companies’ long-term market values seemed to be determined by the consistency, growth, and magnitude of their ability to generate cash.  This made sense to us as it resonated with our prior experience. 

The question then was, Is there a way to systematically evaluate companies’ fundamentals in context of their market prices that yields good long-term investment results?  The energy we devoted to answering this question drove us to launch Euclidean in the summer of 2008.  Although there are many topics that we continue to explore and debate, there are two findings that we feel are so consistently validated by our research that we accept them as truth.  They are:

We believe that systematically buying companies at low prices is a robust method for investing

Please review the simulated results below, which show the annualized performance by decade of four simple value-oriented approaches for selecting equity investments. [4]

The results are compelling.  The difference between the best of these simulated portfolios and the S&P500 would have resulted in 14X more wealth creation over the study’s 40 years.  Our takeaway is that a good route to realizing above-average returns would have been adhering to a process — however simplistic — for buying companies at prices that are low in relation to some intrinsic measure about those companies.  Across these four decades, even as there would have been frequent temptations to deviate from these simple strategies, one could have done quite well by ignoring all external developments and sticking to them without modification.

Ah, but there is the catch — sticking to these strategies without modification. The temptation to deviate would surely have become intense during intermediate periods when these approaches yielded below market results.  For example, the best performing of the simulated portfolios shows annualized returns of 7.6% in excess of the S&P500, across 40 years.  However, you would have had to endure a six-year period, from April 1994 to February 2000, when the simulated portfolio’s results fell behind the S&P500’s return by almost 50%. 

Buying companies at low prices certainly appears to a robust method for investing, but it requires conviction to stick to the process.

We believe that buying companies at low prices does best when valuation multiples compress

Given that strategies for buying companies at low prices can underperform for periods, even as these approaches seem to do well over the long-term, it is useful to consider the types of environments in which systematic value strategies are likely to do best.  

We most recently examined this topic in 2013’s second quarter letter to investors. In that letter, we presented a study showing the simulated performance of value portfolios during periods of optimism and pessimism. The results showed a big advantage to investing in the least-expensive companies (i.e., those with the highest earnings yields) across pessimistic periods when price-to-earnings multiples compress.  This study also showed that this advantage diminishes during periods of optimism when valuation multiples expand. 

Euclidean’s investment process is very sensitive to price.  We evaluate the “goodness” of a company by looking at its balance sheet and long-term operating characteristics, and we view these qualities in relation to the company’s earning power normalized over several years.  Given this, we believe that Euclidean’s results are likely to follow a similar general pattern.  We expect to do better in pessimistic periods and less well during times of optimism.  Importantly, however, we expect to deliver good results when viewed across a complete market cycle.


Euclidean’s Process and Performance in Today’s Environment

At the end of 2014, it is relevant to reflect on the wisdom of grounding a process in lessons that we believe would have served investors well across previous market cycles.  This is so because there are elements of today’s environment that are unusual but, in some ways, resemble the boom times of the late 1990s. 

Specifically, like that period, valuation multiples are again considerably above historical averages, [5] and they have swelled at the same time that corporate profit margins have expanded toward record highs. [6] 

Investor enthusiasm for this high-valuation-on-high-margin environment is reflected in investors owning near record amounts of stock on margin. [7] Another similarity is that the IPO market is open for speculative [8] companies and valuations for certain high-growth technology firms resemble the dot-com heights of the late 1990s. [9] This has all occurred in the context of interest rates being at historic lows.  So, what happens next?  Do these factors continue to move away from their long-term averages and justify an ever-higher stock market?  Or, does some combination of these factors eventually move back towards their long-term averages and create market headwinds?

Predicting the future is not our game.  The current conditions may persist for some time, and, should they do so, our portfolio’s performance may continue to be challenged.  Nevertheless, our review of history and our muscle-memory from navigating prior downturns informs our belief that cycles persist.  We reference Howard Marks:

"I think it’s essential to remember that just about everything is cyclical. There’s little I’m certain of, but these things are true: Cycles always prevail eventually. Nothing goes in one direction forever. Trees don’t grow to the sky. Few things go to zero. And there’s little that’s as dangerous for investor health as insistence on extrapolating today’s events into the future." Oaktree Capital Memo, 2002

These words make sense to us as they align with our prior experience.  We remain focused on what we believe would have done well across past market cycles and anticipate that, soon enough, the benefits to adhering to our systematic value strategy will once again be on display.


We greatly value the privilege of managing a portion of your assets and want you to be an informed Euclidean Investor.  To this end, please review the following pages for an update on our portfolio management practices, look-through earnings, and holdings.  We are available to discuss the content shared here, individual positions in our portfolio, or any questions you might have.  Please call us at any time.  We enjoy hearing from you. 

Best Regards,

John & Mike


We devote substantial time to reviewing our investment process and examining practices that may have improved our recent results.  As a reminder on how we make decisions, we use machine learning to seek principles and practices that would have been successful across multiple market cycles.  Part of our research reviews methods for ranking individual companies as potential investments.  Another area of focus are the rules that define how we manage our portfolio, such as how much we invest in each company, when we add to positions, and when we sell them. 

As we reflect on Euclidean’s performance, we note two portfolio rules that have detracted from returns.  The first relates to our investment universe.  The second relates to the concentration of our portfolio.

Small Caps and Investment Universe

Euclidean is market capitalization agnostic.  We seek attractive combinations of company fundamentals and price, and we initiate positions in companies ranging from very large enterprises to those with market capitalizations as low as $100M.  In 2014, our review suggests that Euclidean’s results would have been improved if we had limited our universe to invest in larger companies.  

As we look at simulated portfolios from the 1970s into 2014, however, we find that it would be unwise to materially limit our investment universe to larger companies, even though such a practice would have been helpful more recently.  We will continue to invest in small capitalization companies and maintain a large investment universe because we believe that doing so increases our prospective returns.

Portfolio Size

Historically, we have operated with a practice of investing 1/30th of fund assets in new positions.  We intend to change this practice such that we will increase the number of holdings in our portfolio. 

Here is the context.  Back in 2008 and 2009, our research showed a decline in expected returns as simulated portfolios increased toward and beyond 100 holdings.  This reduction in performance made sense as, with larger portfolios, fewer dollars were channeled into high-ranking opportunities.  Likewise, portfolios with fewer than 20 positions showed potential for higher returns but even greater potential for additional volatility. From 20-100 positions, there were small but measurable differences, with the best performing portfolios containing around 30 holdings.  That result informed our practices.

We now have the benefit of more history. Euclidean’s recent results would have been better had we spread our capital across more positions.  Moreover, when we run long-term simulations that go through today, we see some of the same dynamics we observed several years ago.  That is, both tightly concentrated portfolios and widely diversified portfolios appear to do less well than portfolios of intermediate size.  But, it is no longer clear that 30 positions would have yielded better results than having 40 or 50. 

For this reason, we will move to a more diversified portfolio.  We believe that doing so gives us similar, long-term return expectations but in the context of being less susceptible – for better or worse – to the outlier outcomes from individual holdings and sectors that have more recently detracted from performance.  We plan to target 50 positions [10] and migrate in this direction during 2015.

[1] Different measures tell different stories.  The average return was 14.0% for the 16 positions exited during 2014 and 33.10% across all 98 realized positions since fund inception.  The weighted average return was 3.67% for positions exited during 2014 and 16.86% across all realized positions since fund inception.  The measures differ because our returns have not been evenly distributed, we are not always able to establish full positions, and our fund has grown over time.

[2] Estimated. 2014 financial information for Euclidean Fund I, LP are pre-audit estimates and subject to adjustment.  

[3] Mr. Buffett on the stock market. Fortune Magazine. November 1999. 

[4] In the simulations, Standard & Poor’s COMPUSTAT database was used as a source for all information about companies and securities for the entire simulated time period.  The S&P 500 return is the total return of the S&P 500, which refers to the Standard & Poor's 500 Index with dividends reinvested.  Simulated returns also include the reinvestment of all income.  In each simulation, NYSE, AMEX, and NASDAQ companies were ranked according the stated criteria such as Market Value to Book Value.  Non-US-based companies, companies in the financials sector, and companies with a market capitalization that, when adjusted by the S&P500 Index Price to January 2010, is less than $400M were excluded from the ranking.

The simulation results reflect assets-under-management (AUM) at the start of each month that, when adjusted by the S&P500 Index Price to January 2010, is equal to $100M. Portfolios were constructed by investing equal amounts of capital in the top decile of companies represented by each value factor and then rebalancing monthly to equally weight the evolving constituents of the top decile.  The amount of shares of a security bought or sold in a month was limited to no more than 10% of the monthly volume for a security.  During the period 1983 to present, the purchase and sale price of a security was based on volume weighted daily closing price of the security during the first ten trading days of each month. Prior to 1983, when daily pricing is not available for all securities, the purchase and sale price of a security was based on the monthly closing price of the security.

Transaction costs are factored as $0.02 per share plus an additional slippage factor that increases as a square of the simulation’s volume participation in a security.  Specifically, if participating at the maximum 10% of monthly volume, the simulation buys at 1% more than the average market price or, conversely, sells at 1% less than the average market price.  Other than these transaction costs, the simulated results do not reflect the deduction of any management fees or expenses. Historical simulated results presented herein are for illustrative purposes only and are not based on actual performance results. Historical simulated results are not indicative of future performance.

[5] Shiller PE Ratio for the S&P 500 (current & historical)

[6] FRED Graph, 2015 Research - Federal Reserve Bank of St. Louis

[7] Historical Securities Market Credit - NYSE Data 

[8] By speculative, we mean companies that have yet to demonstrate a track-record of earnings and, given this, their valuations are not justified by prior earning power.

[9] Venture Capitalist Sounds Alarm on Startup Investing. WSJ.  Yoree Koh and Rolfe Winkler Interview with Bill Gurley.  September 2014.

[10] We will reduce our Standard Investment Unit, or SIU, from 1/30th of assets to 1/50th of assets.  This implies investing 2% of fund assets into new positions.  In practice, due to partial positions, we will generally have more than 50 holdings at any given time.  

We share these numbers because they are easy-to-communicate measures that show the results of our systematic process for buying shares in historically sound companies when their earnings are on sale. [11] [12]

It is important to note that Euclidean uses similar concepts but different measures to assess individual companies as potential investments.  Our models look at certain metrics over longer periods and seek to understand their volatility and rate of growth.  Our process also makes a series of adjustments to company financial statements that our research has found to more accurately assess results, makes complex trade-offs between measures, and so on.  These numbers should, however, give you a sense of what you own as a Euclidean Investor.  In general, higher numbers for these measures are more attractive.  The key measures are:

1) Earnings Yield – This measures how inexpensive a company is in relation to its demonstrated ability to generate cash for its owners. A company with twice the earnings yield as another is half as expensive; therefore, all else being equal, we seek companies with very high Earnings Yields.  Earnings Yield reflects a company’s past four-year average earnings before interest and tax, divided by its current enterprise value (enterprise value = market value + debt – cash).

2) Return on Capital – This measures how well a company has historically generated cash for its owners in relation to how much capital has been invested (equity and long-term debt) in the business.  At its highest level, this measure reflects two important things.  First, it is an indicator of whether a company’s business is efficient at deploying capital in a way that generates additional income for its shareholders.  Second, it indicates whether management has good discipline in deciding what to do with the cash it generates.  For example, all else being equal, companies that overpay for acquisitions, or retain more capital than they can productively deploy, will show lower returns on capital than businesses that do the opposite.  Return on Capital reflects a company’s four-year average earnings before interest and tax, divided by its current equity + long-term debt. 

3) Equity / Assets – This measures how much of a company’s assets can be claimed by its common shareholders versus being claimed by others.  High numbers here imply that the company owns a large portion of its figurative “house” and, all else being equal, indicates a better readiness to weather tough times.

4) Revenue Growth Rate – This is the annualized rate a company has grown over the past four years.  

[11] All Euclidean measures are formed by summing the values of Euclidean’s pro-rata share of each portfolio company’s financials.  That is, if Euclidean owns 1% of a company’s shares, it first calculates 1% of that company’s market value, revenue, debt, assets, earnings, and so on.  Then, it sums those numbers with its pro-rata share of all other portfolio companies.  This provides the total revenue, assets, earnings, etc. across the portfolio that are used to calculate the portfolio’s aggregate measures presented here. 

[12] The S&P 500 measures are calculated in a similar way as described above.  The market values, revenue, debt, assets, earnings, etc., for each company in the S&P 500 are added together.  Those aggregate numbers are then used to calculate the metrics above.  For example, the earnings yield of the S&P 500 is calculated as the total average four-year earnings before interest and taxes across all 500 companies divided by those companies’ collective enterprise values (all 500 companies’ market values + cash – debt).

Euclidean’s Largest Holdings as of December 31, 2014

We provide this information because many of you have expressed an interest in talking through individual positions as a means of better understanding how our investment process seeks value. 

We are available to discuss these holdings with you at your convenience.  We are happy to explain both why our models have found these companies to be attractive as well as our sense of why the market has been pessimistic about their future prospects. 

Euclidean’s Ten largest positions as of December 31, 2014 (in alphabetical order)

  1. Artic Cat – ACAT
  2. Avnet – AVT
  3. The Buckle – BKE
  4. CARBO Ceramics – CRR
  5. Coach – COH
  6. ePlus – PLUS
  7. Humana – HUM
  8. Magellan Health – MGLN
  9. NetGear – NTGR
  10. Preformed Line Products – PLPC