Roumell Asset Management commentary for the second quarter ended June 30, 2016.
Roumell Asset Management – Forecasting and Deep Value Investing
In the recently published book, Superforecasting, The Art and Science of Prediction, authors Philip E. Tetlock and Dan Gardner report on the rich details of their Good Judgement Project (GJP). The GJP was part of a larger longitudinal study involving thousands of individuals over several years to better understand forecasting with the ultimate goal of increasing the U.S. intelligence community’s forecasting accuracy. The book’s authors are experts at analyzing forecasting abilities and offer their views on whether anyone can forecast meaningfully above average (yes, but it’s a small group) and describe the attributes that they believe help explain these superforecasters’ abilities. Tetlock and Gardner believe that the habits of superforecasters can be codified and taught and end their book with a Ten Commandments of superforecasting for their readers.
RAM has always been forecasting-averse, and for good reason—it’s very difficult and the odds of success are low. In Tetlock and Gartner’s view, about 2% of their participants (a group comprised of engineers, lawyers, artists, scientists, Wall Streeters and Main Streeters, professors and students), qualified as superforecasters. These were well-read, smart people who stay on top of world affairs and know how to research questions. This rather sobering notion is precisely why RAM has long de-emphasized highly liquid markets/securities, growth investments highly dependent on predicting future earnings, forecasting future commodity prices or the direction of interest rates.
To be clear, all investments at some level rely on certain forecasts. RAM’s goal has always been to divide investment narratives into essentially two buckets—what is known today and what may materialize tomorrow. The more an investment thesis rests on the former, “what is,” the more we like it. In our last quarterly letter we discussed, in some detail, buying double-discounted closed-end bond funds possessing a discount to NAV and an NAV itself that is reflective of a portfolio of bonds trading at a deep discount to par value wherein we effectively were able to purchase a diversified portfolio of high yield bonds at seventy cents of par value. These investments are classic “what is” investments. The investment relies far more on a discount to value today as opposed to possible value creation tomorrow that is heavily dependent on forecasting.
Nonetheless, even our double discounted closed-end bond fund investments have some degree of forecasting built into the underlying investment thesis. For instance, we modeled portfolio default rates of 5% to 20% (with zero recovery values), which means a tsunami of defaults exceeding our high-end model would be problematic; a low probability, but something north of zero. Moreover, we predict the portfolio managers will not trade the portfolio in a way that eliminates the value of the double-discount, i.e., terrible bond trading that renders moot an analysis of the existing portfolio. Thus, while all investments do involve forecasting, our approach has been to minimize the requirement to forecast the future as much as possible.
The Intelligence Advanced Research Projects Activity (IARPA) is an agency within the Intelligence Community (IC) that reports to the director of National Intelligence. Its job is to increase the accuracy of American intelligence estimates. Not surprisingly, no one really knows how good the overall intelligence forecasting is because it’s never been measured, likely the result of analysts not wanting the light shined on their significant, but often not useful, efforts. The IC was humiliated by its conviction that Iraq possessed WMD, adding to other big “misses” such as the surprise collapse of the Soviet Union. With a desire to better understand the business of predicting the future, IARPA created a forecasting tournament comprised of five teams led by top researchers to measure forecasting acumen among intelligence professionals and educated, informed, common citizens.
The GJP was one team comprised of 2,800 individuals from varying backgrounds who were selected by the authors. Leveraging their knowledge and prior research on the subject of forecasting, the authors put in place a structure for the GJP team. From September 2011 to June 2015, teams were required to submit daily forecasts for nearly 500 questions about world affairs. Participants were allowed to regularly update and change their forecasts; each change becoming a new forecast. Questions like the following were posited: Will Greece leave the Eurozone?; Will Israel attack Iranian nuclear facilities by September?; and, Will Saudi Arabia cut their oil production output by the end of this year? The study rewarded confidence/conviction levels such that an individual assigning an 80% probability to a potential event occurring received a higher score than someone assigning a 50% probability to the same event if it, in fact, occurred.
The GJP group beat the official control group (comprised of an IC team operating under the same constraints) by 60% after year 1 and by 78% by the end of year 2. The GJP team also beat universityaffiliated teams, including the University of Michigan and MIT, from 30% to 70%, and outperformed professional intelligence analysts with access to classified information. The study’s basic conclusion: generating above average forecasting value is unlikely, but possible. To wit, roughly 2% of the individuals in the study showed themselves to be superforecasters. What were some of the traits and habits shared among this group of superforecasters?
One of the big takeaways from the authors’ history of studying forecasting is comparing one group they describe as hedgehogs to another who they describe as foxes. Superforecasting, their research shows, is highly correlated with how one thinks, not what one thinks. Hedgehogs tend to think around “big ideas” and present as highly confident people; to their forecasting detriment. Referring to this group, the authors state, “They sought to squeeze complex problems into the preferred cause-effect templates and treated what did not fit as irrelevant distractions…they were unusually confident and likelier to declare things ‘impossible’ or ‘certain.’ Committed to their conclusions, they were reluctant to change their minds even when their predictions clearly failed. They would tell us, ‘Just wait.’”
The other group, foxes, was comprised of much more pragmatic thinkers, who were often humble in their assessments. Referring to this group, the authors noted, “These experts gathered as much information from as many sources as they could. They talked about possibilities and probabilities, not certainties. And while no one likes to say ‘I was wrong,’ these experts more readily admitted it and changed their minds.”
While reading Superforecasters one is reminded of the difficulty of forecasting, particularly the macroeconomic variety, and why RAM has tried to stay clear of being overly dependent on such forecasting as much as possible. We are in the business of forecasting at some level, but the goal has always been to keep it to a minimum while pursuing existing embedded value. That means not owning investments wholly dependent on rising/falling commodity prices, interest rates predictions, estimating GDP growth and the like.
On an individual security basis, it means minimizing our dependence on growth projections and preferring to emphasize “what is”, while hopefully owning optionality without paying for it. It’s why we owned a gold streaming company (on two separate occasions) when it provided a 12% plus free cash flow yield, but sold it when that yield contracted toward 6%, materially increasing the investment’s dependence on forecasting gold prices, which was something we did not feel competent to pursue. Now, with the price of gold steadily rising, this security has been upgraded by sell-side analysts—after a 100% rise from its low—based on their sudden conviction that gold prices are headed higher, evidently based on nothing other than that gold prices are headed higher. Joining in with the average opinion of the average opinion is not what we do.
To sum, the authors offer up the habits of superforecasters which they believe can materially increase one’s forecasting accuracy. Here are a few examples among the authors’ Ten Commandments of the superforecasters:
- Triage. Focus on questions where your hard work is likely to pay off. Don’t waste time either on easy “clock-like” questions (where simple rules of thumb can get you close to the right answer) or on impenetrable “cloud-like” questions (where even fancy statistical models can’t beat the dart-throwing chimp). Concentrate on questions in the Goldilocks zone of difficulty, where effort pays off the most.
- Strike the right balance between under- and over-reacting to evidence. Belief updating is to forecasting as brushing and flossing are to good dental hygiene. It can be boring, occasionally uncomfortable, but it pays off in the long term.
- Strike the right balance between under- and over-confidence, between prudence and decisiveness. Superforecasters understand the risks both of rushing to judgment and of dawdling too long near “maybe.” They routinely manage the trade-off between the need to take decisive stands and the need to qualify their stands.
- Bring out the best in others and let others bring out the best in you. Master the fine arts of team management, especially perspective taking (understanding the arguments of the other side so well that you can reproduce them to the other’s satisfaction), precision questioning (helping others to clarify their arguments so they are not misunderstood), and constructive confrontation (learning to disagree without being disagreeable).
Top Three Purchases
GSI Technology, GSIT. Founded in 1995, GSIT is a provider of high performance semiconductor memory solutions to networking, industrial, medical, aerospace and military customers. The company is headquartered in Sunnyvale, CA. GSIT is an extremely well-capitalized chip company, comprised of a legacy chip business that is stable and what we believe is a free option on its associated processing technology, which is part of the artificial intelligence revolution.
At our purchase price of under $4/share, the company’s total market capitalization was roughly $90 million, equal to its book value (tangible book value is 87% of book value). After subtracting out its $66 million in cash and liquid investments, the company’s enterprise value was about $25 million at our purchase price. GSIT stock has low market liquidity. We were fortunate to benefit from what we believe was a large shareholder hedge fund’s decision to sell its shares. Although the company burned small amounts of cash in recent years, we expect the company to, at a minimum, breakeven in cash this year (even after significant R&D investment in associated processing technology) having settled costly litigation with Cyprus Semiconductor in May 2015. The company spent $8.6 million and $6.7 million in litigation related expenses in fiscal 2015 and 2016, respectively.
The company’s roughly $53 million in annual revenue comes primarily from its static random access memory (SRAM) chip, possessing near 50% gross margins. The overall SRAM market is declining by about 10% annually because the chip’s functionality has increasingly been embedded into components, replacing the need for standalone SRAM chips. Its largest customer, Alcatel-Lucent, accounted for 25% of the company’s 2015 revenue. The company believes its relationship with Alcatel is very strong since it’s a sole source provider on high-end platforms. The company’s stable revenues are the result of its leadership in discrete markets, such as defense, that require standalone chips. In fact, while the overall SRAM chip market declines, GSIT believes its revenues will grow as a result of being a leader in a niche market.
What makes GSIT compelling to us is the associative computing technology and the patented intellectual property acquired with MikaMonu, an Israeli company purchased in November 2015 for $5 million with a $2.5 million four-year retention payment plus a potential earn-out. The acquisition was conditional on the retention of MikaMonu co-founder and chief technologist Dr. Avidan Akerib, who has been working in the field of associative processing for decades. According to Lee-Lean Shu, GSIT CEO, “With the vast amount of data being generated, and the increasing demand for faster processing of that data, existing systems that move data back and forth between processor and memory are no longer adequate. Memory bus speeds are not keeping up with CPU speeds, and this is causing a bottleneck at the IO between the CPU and memory. MikaMonu’s cutting-edge in-place associative computing technology addresses this issue by changing the concept of computing from serial data processing—where data is moved back and forth from the processor to the memory—to parallel data processing, computation and search directly in the main processing array. This new computing model will greatly expedite computation and response times in ‘big data’ applications.” Amazon was shown the company’s associative computing capabilities and it was “very impressed” after running multiple simulations. We’ve been told that Amazon would like these chips in hand as soon as possible. GSIT does not expect MikaMonu’s technology to generate revenue until late 2018. In the meantime, however, it is expected that there will be milestone announcements introducing capabilities and applications.
What was particularly noteworthy to us about the MikaMonu acquisition was that the sellers turned down more money up front from a much larger chip company in exchange for an override on revenues generated from its technology with GSIT over the next ten years. MikaMonu shareholders will receive nearly 6% of revenue over the next ten years up to $615 million of revenue. The GSIT/MikaMonu idea came to us from a very successful Israeli entrepreneur technologist we’ve known well for over ten years.
Insiders own over 30% of GSIT stock. The company did a $25 million tender in August 2014 at $6.50/share.
Our GSIT investment underscores our approach to buy “what is” cheap now. We seem to be paying nothing for the MikaMonu option (free growth investing). Given the company’s exceptional balance sheet strength, we have time for the story to play out. Is there an element of forecasting? Yes. At our purchase price, is it significant? Not in our minds.
CSI Compressco 7.25% Senior Notes maturing 8/15/22. CSI Compressco (CCLP) is a provider of compression services and equipment for the natural gas and oil industry. It also provides aftermarket services and compressor parts. Approximately 90% of CCLP’s revenue is from the United States. Natural gas compression is a mechanical process in which the pressure of a given volume of natural gas is increased. It is essential to the production and movement of natural gas. Compression is typically required numerous times in the natural gas production and sales cycle, including (i) at the wellhead, (ii) throughout gathering and distribution systems, (iii) into and out of processing and storage facilities and (iv) in natural gas pipelines. Compression is also utilized for gas lift, an artificial lift technique for producing oil that has insufficient reservoir pressure.
Although CCLP is an industry leader and generates attractive levels of cash flow from operations, we choose not to invest in the common stock, but instead determined that the company’s debt provided a better risk/reward opportunity. As such, we approached our review of this investment from the prospective of a debt investor. Our focus was on CCLP’s 7.25% coupon Senior Notes. There is $350 million par of these notes outstanding and they mature on August 15, 2022.
Based on our analysis, we initiated a position in the 7.25% Senior Notes at roughly $70.60. At this purchase price, we are generating a current yield of about 10% and expect a yield to maturity of about 14.5%. This is equity-like return with the protections afforded to a senior debt security.
Our analysis concluded that the probability of refinancing and full repayment in August 2022 is very high. It is possible that the market was mispricing these notes due to the general concern surrounding the energy industry. We believe the market was missing the significant protective cushion that was available to the senior note holders from the company’s ability to cut capital expenditures and the common equity distributions. CCLP pays a very high common equity distribution of approximately $50 million a year and had been spending on capital expenditures at a high rate to fund growth. Maintenance capital expenditures are a small fraction of recent capital spending.
Cash flow from operations was $101.9 million for the year ended December 31, 2015. However, capital expenditures were $95.3 million resulting in $6.6 million of free cash flow. CCLP has since reduced its capital expenditures and that strategy is reflected in the more favorable March 31, 2016 quarter cash flow results. Cash flow generated from operations and free cash flow for the quarter ended March 31, 2016 were $15.1 million and $13.7 million, respectively. The $95.3 million of 2015 capital expenditures noted above included only $11.3 million of maintenance capital expenditures. CCLP has decreased its capital expenditures plans for 2016, with estimated levels ranging from $20.0 million to $30.0 million, including approximately $12.0 million of estimated maintenance capital expenditures.
We utilized projected cash flows under highly stressed assumptions to assess the safety of this debt investment. As of December 31, 2015 and March 31, 2016, the horsepower utilization rates were 82% and 77.2%, respectively. Historically these are low utilization rates and reflect the generally depressed oil and natural gas industry. In our stressed cash flow analysis, we dropped the utilization rate to 65% and assumed a 30% reduction from the already stressed fourth quarter 2015 cash flow. Under that stressed scenario, we further assumed that the company paid $35 million in common distributions and $25 million in capital expenditures. This stressed scenario resulted with the company still generating $11.3 million in free cash flow. As noted, the company would eliminate the common distribution and cut capital expenditures before it would ever miss a debt payment. This illustrates the significant cushion we believe exists for the benefit of the senior debt holders.
In our minds, CCLP debt clearly falls into the “what is” bucket and our price requires little need to make commodity price forecasts. The flexibility surrounding capital spending and common dividends provides substantial support for the ability to adequately service the debt. In addition, CCLP also always has the option of issuing additional equity capital.
WCI Communities (WCIC). WCI Communities (“WCI”) is a lifestyle community developer and luxury homebuilder of single and multi-family homes in the coastal Florida markets. WCI develops amenityrich, lifestyle-oriented master-planned communities, catering to move-up, second-home and active adult buyers. Headquartered in Bonita Springs, Florida, WCI is a fully integrated homebuilder and developer with complementary real estate brokerage and title services businesses. During 2015, WCI re-entered the tower business when they began construction of a 75-unit luxury high-rise tower in Bonita Springs, Florida.
There are several attractive financial characteristics that impacted our decision to make an investment in WCI (this is the second time in the past two years). Its overall financial position and results of operations are strong and the stock trades below reported book value. We also believe that reported book value is understated as certain valuable real estate assets are recorded on the company’s balance sheet at an amount below the current re-sale value. These assets are carried at values established in 2009 as it exited bankruptcy benefiting from fresh start accounting. One is a set of 12 undeveloped condo-tower pads, on the balance sheet at $0.65 per share but worth closer to $3 based on comparable sales. Another is a block of 3,500 home sites that also reflect fresh-start accounting from 2009. The thesis is the same today as it was in our first go-round—purchase at a substantial discount to adjusted book value.
WCI is well-financed and the leverage risk is low. The net debt to equity ratio is only 29% and WCI held $107 million of cash at March 31, 2016. WCI is profitable and, after adjusting for real estate purchases, generates good cash flow from operations. In addition to the positive balance sheet, income and cash flow characteristics, the stock is priced at a low earnings multiple and, as mentioned, a discount to book value. WCI earned net income of $6.7 million and $35.4 million for the first quarter of 2016 and the year ended 2015, respectively. The trailing twelve month price to earnings ratio is about 12x.
As of March 31, 2016, WCI had a backlog of 625 units contracted for sale at an aggregate purchase price of $316 million, compared to a backlog of 569 units at an aggregate purchase price of $271 million as of December 31, 2015. The sales agreements are generally not conditioned on the buyer securing financing. Given its target buyer demographics, on average, its homebuyers tend to rely less on mortgage financing for their purchases and typically provide higher deposits and down payments, compared to homebuyers nationally. During the years ended December 31, 2015, 2014 and 2013, approximately 49%, 58% and 44%, respectively, of WCI’s homebuyers were all-cash buyers. This was a contributing factor to its low cancellation rates of 7.4%, 6.8% and 4.7% of gross orders during 2015, 2014 and 2013, respectively.
WCI includes the purchase of real estate as a use of cash in the operating section of the cash flow statement. In 2015, WCIC increased its real estate inventory just over $100 million to $554 million. Adjusting for this $100 million increase in real estate inventory would result in positive cash flow from operations of about $62 million. Operating cash flow for the first quarter of 2016 was $13.2 million, after adjusting for $39.3 million of net real estate purchases.
At our purchase price of about $16, we had a 12% discount to March 31, 2016 reported book value per share of $18.21. As discussed above, we believe that the reported book value is conservative as it does not include the appreciation in value of certain valuable real estate holdings.
In summary, we believe WCI is a well-financed company with impressive financial results and is trading at a discount to its intrinsic value. It has significant focus and expertise in one of the best real estate markets in the country (favorable climate, caters to aging demographics in the U.S., significant and consistent population growth, etc.). For the year ended 2015, WCI grew book value per share by 8.3%. Assuming a reasonably consistent economy, WCIC should be able to continue to grow book value. We will continue to methodically and diligently search for out-of-favor, overlooked and misunderstood investments and stay true to being balance sheet focused, opportunistic, and thoughtful while gathering detailed information to make well-informed investment decisions.
Disclosure: The specific securities identified and described do not represent all of the securities purchased, sold, or recommended for advisory clients, and the reader should not assume that investments in the securities identified and discussed were or will be profitable. The top three securities purchased in the quarter are based on the largest absolute dollar purchases made in the quarter.
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