Universa Decennial Letter 2018

MARCH 16, 2018 UNIVERSA INVESTMENTS L.P.

DECENNIAL LETTER MARCH 2008 - FEBRUARY 2018

W HAT HAT S P AST IS PROLOGUE ’

Dear Universa investors, This month marks the ten-year ten- year anniversary of Universa’s tail hedging program (the “Black Swan Protection Protocol”).. As we ring the bells and reflect on how far we’ve come, I am reminded of an old Russian proverb that Protocol”) warns, “Dwell on the past, lose an eye. Forget the past, lose both eyes.” What a diverse decade it has been, spanning a great bust and boom, some very high volatility and some very low. It was a superb test for us, and a little retrospection retrospection is in order. So let’s review how we performed as a risk mitigation strategy for you, including in comparison with other strategies that also presumed to serve such a function. Risk mitigation performance must of course be measured by its “portfolio effect”— specifically, effect”— specifically, the impact it has on the compound annual growth rate (CAGR) of the entire portfolio whose risk it is trying to mitigate. As I will discuss later in this letter, this is all that really matters in risk mitigation, and has always been our focus. It is where the rubber meets the road. Below is Universa’s ten-year ten-year life-to-date legacy of risk mitigation performance. We paired our actual net performance (monthly administrat administrator-provided or-provided net net returns, using using yours from from your start start date, expressed as returns on a standardized capital investment) with an SPX position (a realistic proxy for the systematic risk being mitigated) to create a hypothetical “risk “risk -mitigated portfolio.” That portfolio’s net performance is summarized below, along with the similarly-c similarly-constructed onstructed portfolio portfolio performance performance of five five other standard-beare standard-bearers rs in risk mitigation. Consider this a risk mitigation scorecard for the past decade. A DECADE OF RISK MITIGATION (March 2008 – February 2018) 400

Portfolio CAGR (%)

Universa 12.3%

1Y

5Y

10Y

10Y min

Universa Tail Hedge (3.33%) + SPX (96.67%)

15.7

12.2

12.3

0.6

iShares 20Y+ Treasury (25%) + SPX (75%)

12.8

12.0

9.7

-15.1

CBOE Eurekahedge Long Volatility (25%) + SPX (75%)

10.9

10.4

9.1

-13.8

Gold (25%) + SPX (75%)

14.2

10.6

8.5

-25.4

Hedge Fund Index (25%) + SPX (75%)

14.6

12.2

8.2

-27.5

CTA Index (25%) + SPX (75%)

12.4

11.2

7.9

-21.5

Strategy 200

100

50 2008

2010

2012

2014

2016

2018

2601 South Bayshore Drive | Suite 2030 | Miami, FL 33133 tel 786.483.3140 fax 786.483.3141 universa.net

UNIVERSA INVESTMENTS L.P.

The equity curve shows each risk-mitigated portfolio’s portfolio ’s growth of capital capital over over the last ten years. The table shows each ea ch portfolio’s latest latest one-year trailing 12-month 12-month return (labeled “1Y”), last five -year and ten-year CAGR (or “geometric mean” annual return, labeled “5Y” and “10Y”, respectively), and lowest annual return over the last ten years (labeled “10Y min”). Green cells indicate the highest two returns among the six portfolios in any given column, and red cells indicate the lowest two. In our ten-year life-to-date, a 3.33% portfolio allocation of capital to Universa’s tail hedge has added 2.6% to the CAGR of an SPX portfolio (the SPX total CAGR over that period was 9.7%). To put this in perspective, this is the mathematical equivalent of that same 3.33% allocated to a ten-year annuity yielding about 76% per year. In contrast, each of the other risk mitigation strategies actually subtracted value value over the same period, regardless of their allocation sizes. (Other risk mitigation strategies omitted from this comparison basically produced results within the ranges of these five standard-bearers, or worse; they are shown in the Appendix.) Moreover, during the last one-year and five-year periods, when the SPX experienced very positive returns, Universa’s risk mitigation strategy also outperformed these alternative risk mitigation strategies. It is common for people to simplistica simplistically lly view tail hedging, hedging, the way we do it here here at Universa, Universa, as a “drag” on their portfolio portfolio in the absence of a market crash. However, when framed correctly, as we have tried to do here with our risk mitigation scorecard, the picture changes. It becomes apparent what a real portfolio drag most other risk mitigation strategies have actually been. (This is like the joke about the camper who only needs to outrun his friend, rather than the bear that’s chasing them. Over Over the past decade, Universa Universa’s ’s risk mitigation mitigation strategy strategy needed needed to outrun outrun only these these other strategies — but we also also outran the the SPX.) Despite our firepower in a market market crash (and (and my scorn scorn for monetary-bubbles), we have always been truly agnostic as to the direction of the stock market. The 3.33% portfolio allocation size to Universa was chosen because it is (and has always been) the approximate effective allocation size recommended in practice at Universa (relative to a client’s total equity exposure). The 25% portfolio allocation size to the other risk mitigation strategies was chosen to be meaningful and realistic for an average investor (relative to their total equity exposure). That turned out to be insufficient for any of those strategies to provide a level of downside protection anywhere close to the level Universa provided. This is clearly evidenced in the “10Y min” column, column , a good proxy for the systema systematic tic risk remaining in each portfolio (thanks to the 2008 data point in our time series). The HFRI allocation, for instance, actually resembled adding more SPXlike risk to the portfolio. If we were to try to calibrate ex post the the allocation sizes for each of the risk mitigation strategies in order to maximizee their ten-year portfolio CAGRs, maximiz CAGRs, the optimal size for each strategy (with the exception of Universa’s and the Treasury strategy) would actually be 0%. That means for best results, you shouldn’t have added any of those strategies to your SPX portfolio at all. Despite our efforts, the risk mitigation scorecard remains something something of an apples-to-oranges apples-to-ora nges comparison. The source of Universa’s risk mitigation outperformance is no secret: It was driven by our “convexity”— the “convexity”— the degree of portfolio loss- protection protection provided provided for a given capital capital allocation, allocation, or the “bang “bang-for-the-for-the- buck”— buck”— that that is, of course, our particula particularr modus operandi at Universa. This translates into a lower capital allocation of only 3.33% needed to produce a meaningfully larger protective crash profit. And, that 3.33% (even when experiencing losses) poses less “drag” relative relative to the far greater greater amount amount of capital capital invested invested in the SPX the rest of the time.

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While the past can be an imperfect gauge of future risk mitigation value, it can nonetheless provide insight that prevents us from “flying blind,” which which seems seems to describe describe many of these more orthodox risk mitigation efforts today. A strategy strategy that worked in the past naturally isn’t guaranteed to work again in the future. Yet, if a strategy didn’t work in the past, isn’t there something inherently unscientific about expecting that it will in the future? In other words, Popper’s falsificationism falsificationism applies: Though we cannot necessarily accept a strategy as always effective, we recognize when we must reject it. Heeding the warnings of the old Russian proverb, we at Universa don’t like the thought of losing even one eye! So, of course, we don’t rely re ly just on the past in demonstrating our risk mitigation. As Universa investors, you have had the benefit of knowing first-hand (either from the risk reports we provide regularly or your own stress tests of your transparent portfolio) that we have delivered consistent and robust crash protection throughout your tenure as clients. We have employed no forecasting, no timing, no finger to the wind — — none none of which ultimately adds risk mitigation value, by definition. Effective risk mitigation requires consistency and robustness. It shouldn’t be a black box or or a mere statistic statistical al regularity. regularity. Our risk risk mitigation mitigation approach approach plays too too significant significant a role in a portfolio portfolio for that, more than the mere incremental “alpha” of a typical allocation. So much of what passes for risk mitigation strategies simply is not that; rather, they result in what Peter Lynch referred to as “diworsification.” They may moderately lower portfolio risk, risk, but more importantly they also lower lower its CAGR. When done right, and as we have seen first-hand and delivered these past ten years, effective risk mitigation does more than just lower risk. It transforms the entire portfolio, adding unique value that no other type of investment can. It is the tail that wags the dog.

Bernoulli and the Geometric Mean

Why do we use the CAGR, or geometric mean annual return, as our metric to evaluate the effectiveness of a risk mitigation strategy? A vexing conundrum known to most investors is losing, for instance, 50% one period and then making 100% the next; you’ve experienced an impressive arithmetic mean (or “ensemble average”) return of 25%, yet you just barely made made it back back to even even with a geometric geometric mean mean (or “time “time average”) average”) of 0%. As a single single wager, wager, a coin toss of either a +100% or -50% - 50% return looks smart, but as an ongoing and compounding parlay, it’s a big waste of time. Your long-run long-run performance (of just breaking even) would be highly “non -ergodic”. The arithmetic mean return of an investment just doesn’t convey all that much abo ut its risk mitigation value, and needn’t translate into a portfolio’s CAGR. As it turned out, over the past ten years the arithmetic mean annual return on invested capital in the standalone Universa tail hedge was quite high at 52.7%, while the arithmetic mean annual returns of the other five standalone strategies ranged from 1% to 6.3%. But this doesn’t really tell the story of the relative risk mitigation performance of all these strategies. As I have previously have previously written written and demonstrat demonstrated ed to you elsewhere elsewhere,, even if we hypothetically adjusted Universa’s standalone arithmetic mean annual return to exactly zero over the past ten years (by, say, hypothetically lowering our annual return in 2008 accordingly), the CAGR of that combined hypothetical Universa tail hedge and SPX

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risk-mitigated portfolio still would have outperformed the SPX alone by more than 0.6% — 0.6% — and and that would still be greater than any of the other alternative strategies (all with positive standalone arithmetic mean annual returns). (For the Universa allocation, this would equate to a similar-size allocation to a ten-year annuity yielding about 24% per year.) The extreme crash convexity of our return profile is far more important than our arithmetic mean return in adding risk mitigation value to your portfolio. The mathematical intuition behind how this works was first laid out in a 1738 paper by the great Swiss mathematician and physicist Daniel Bernoulli (the same Bernoulli who taught us how airplanes fly). Bernoulli put a stake in the ground for using the geometric mean to appraise the value and expected performance of an investment or wager, calling callin g the geometric mean expectation a “moral expectation.” (Unfortunately, he confusingly expressed this as a logarithmic utility function, merely an intuitive detail for him — him — but a detail detail that would become a profound distraction for economists for centuries to come.) Bernoulli explained how a logarithmic mapping of winnings and losses, expressed as a return on one’s total wealth, solved the famous St. Petersburg paradox (named after his Russian residence at the time, though formulated by his cousin Nicolaus — the the Swiss family Bernoulli were most prodigious indeed). His idea was that wagers should be evaluated by the probability-weighted logarithms of their outcomes, mathematically equivalent to the geometric mean of their probability-weighted outcomes. (Thus, whether we interpret his point as log utility maximization or geometric mean maximization doesn’t matter.) The paradox was no longer a paradox. When you maximize the expected geometric mean of a wager, you maximize your end-point wealth. (For a good, not-tootechnical read on this, see the 2005 book Fortune’s Formula my tattered copy has been Formula by William Poundstone — my sitting on my desk all these past ten years.)

Daniel Bernoulli (1700 – 1782), 1782), Patron Saint of Universa

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A plot of the logarithmic function (an example is shown in the picture, over Bernoulli’s left shoulder) shows how this all works. Consider that the geometric mean return is mathematically (the exponential of) the average of the logarithms (the y-axis) of the arithmetic returns (the x-axis). Because the logarithm is a concave function that curves downward, it increasingly penalizes negative arithmetic returns the more negative they are. Thus, the more negative they are (as we roll down the curve), the more they increasingly lower the geometric mean return (and the end- point point wealth). wealth). That’s not not esoteric quant finance; finance; that’s that’s precisely precisely the way way compounding compounding works in the real, physical world world (and regardless regardless of your your return distribution distribution assumptions assumptions). ). The destructive impact this curvature of the log function has on geometric mean returns and wealth is what I call the “volatility tax.” (It tax.” (It is why our smart coin tosses ended up being a waste of time.) What’s more, more, think of the geometric mean return as a superior measure of risk. This is because, unlike the more traditionally used standard deviation (or volatility), for instance, the geometric mean more accurately reveals the consequence of risk. Just as standard deviation is the square root of the average squared return deviation, the geometric mean is the exponential of the average log-return. The former over-weights larger deviations, positive or negative. The latter over-weights larger negative deviations; so, it measures the incidence of extreme loss more accurately, and it actually has real economic meaning. Bernoulli even went on to apply this geometric mean criterion to valuing insurance — insurance — something something near and dear to our hearts here at Universa. He demonstrated demonstrated how a shipper’s logarithmic losses relative to his wealth (which, again, penalize larger losses) are what drives his economic value in insuring against those losses. A large relative loss disproportionately lowers his geometric mean return as a shipper, because it leaves him with much less to reinvest and compound on his next shipment. So that log-loss shows the true (higher) value-added for him of insuring against it. Almost three hundred years ago, this Swiss mathematician understood the intuition of what really drives risk mitigation value better than most quants do today. (Leave it to the Swiss.) Bernoulli saw this disproportionate cost from compounding losses as “nature’s admonition to avoid the dice altogether,” which “follows from the concavity of curve” in the logarithmic function. He could just as easily have said “nature’s admonition to mitigate risk with extreme convexity.” He is Universa’s Patron Saint. Being exposed to this treacherous “concavity of curve” is a lot like being extremely “short gamma” far below the market (which as options traders know is another way of saying your incremental losses get bigger as the market moves down). This makes hedging that nonlinear cost via extreme “convexity of curve”—or extreme “long gamma” far below the market—seem like a natural fit. That’s good intuition behind why Universa’s specific type gamma” of extremely convex, insurance-like insurance- like payoff has been so effective at raising a portfolio’s geometric mean return— as well as why mere negatively-corr elated negatively-corr elated payoffs just can’t really move the risk mitigation needle. Mathematically, it is the rare big loss, not the frequent small losses, that matters most to long-run compounding. Over time, Bernoulli’s geometric mean maximization criterion eventually did put up a good fight for attention among risk practitioners, most notably through Williams in 1933, Kelly (whose “Kelly criterion” is simply a “geometric mean criterion”) in 1956, Latané in 1959, and Breiman in 1961 (who showed that a geometric mean maximizing strategy both minimized the time to reach a target level of wealth and maximized the level of wealth reached after a given amount of time —and time —and who wouldn’t wouldn’t sign up for that?). that?).

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Ironically, even Markowitz had become a proponent of the geometric mean criterion by 1959 (and very much so by 1976). But it was too late; his his 1952 meanmean-variance variance (“Modern Portfolio Theory”) framework was already on its way inexplicably to becoming the dominant framework used even today for portfolio construction. Markowitz’s is a distinctly one-period framework, in which time is irrelevant, as opposed to the multi-period, multiplicative, cumulative,, reality-based framework of Bernoulli. Like our non-ergodic coin toss, in a one-period world the cumulative arithmetic mean matters, and in a multi- period multi- period world it’s the geometric geometric mean. mean. In both the one-period one-period and the multi- period period world, volatility volatility is bad. In the former, it is because because volatility volatility means means “risk”; “risk”; in the latter, latter, it is because because volatility means higher losses that lower the geometric mean return and the end-point wealth. Nassim Nicholas Nicholas Taleb, Taleb, my longlong-time colleague and Universa’s Distinguished Scientific Advisor, in his recently published book book Skin in the Game, very insightfully took up the point of the non-ergodicity of the one-period ensemble (arithmetic) average versus the multi-period time (geometric) average, whereby the occurrence of -100% merely lowers the former, but brings ruin to the latter. As Nassim wrote, “more than two decades ago, practitionerss such as Mark Spitznagel practitioner Spitznagel and myself built built our entire entire business business careers around…the effect of the difference between ensemble and time.” That pretty much sums it up. Bernoulli’s call to map returns through the logarithmic the logarithmic function was a normative one, not a descriptive one. We experience the markets’ returns in arithmetic space; a return over any given time interval is arithmetic, by definition. If we had only one bet to make in our life, maximizing this arithmetic return might be appropriate. But from a long-term investment standpoint, when we have many multiplicative bets to make, or many bets whose results compound over time, we need to map present arithmetic returns into future geometric returns in order to maximize our end point wealth. To do so, we need to experience the markets’ returns through the lens of the logarithmic function. We need to be Bernoullian logarithmic risk-takers. (This distinction is the best instance I know of what I have called the “direct” versus the “roundabout” approach, the latter being the most productive for the long term.) While that may sound unnaturally Spock- like, it’s actually the most natural thing we could do as successful investors. Raising a portfolio’s long-term long-term geometric return is the very point of any effective risk mitigation strategy. strategy. All risk mitigation strategies aim to do it, but, as we saw over the past decade, most fall short. Effective risk mitigation happens to be a really hard thing to do.

The Next Decade

Our legacy to-date speaks for itself. I hope that this retrospective portfolio-level view of what we have delivered over the past ten years gives you a helpful, more holistic perspective on the benefit that Universa’s tail hedge has hedge has provided to your your portfolio. It should also also attest attest to the destructive destructiveness ness that the volatility volatility tax — or or Bernoulli’s treacherous “concavity of curve”— has has had on equity portfolios, and the benefit from effectively mitigating it. At Universa, we are engaged in what can only be seen as something of a new paradigm in portfolio construction and risk mitigation. We put Markowitz’s Modern Portfolio Theory on its head. While what we started ten years ago (and personally even ten years before that) has since grown into its own “tail hedging asset class,” such

6



nomenclature is more style than substance. Despite our growth and maturation over these ten years, our approach remains very much an unorthodox niche strategy and way of looking at investing, and I don’t see that changing anytime soon. We should expect the next decade to be at least as diverse as the last, with another great bust and boom and ever wilder swings in volatility. After all, the very root of all of that last time was the economic and market distortions of monetary interventionism, which is now certainly greater than ever before. But whatever turbulence lies ahead, thanks to effective risk mitigation, together we shall remain agnostic again. We shall, in the words of Bernoulli, “avoid the dice.” Make no mistake, effective risk mitigation will remain our absolute focus here at Universa, just as it was from the start, and we intend to continue providing all of our investors with a similar level of risk mitigation value-added in our second decade as we have in our first. I look forward to showing that to you again in 2028, when I write you our next decennial letter. What’s past is prologue…

Cordially,

Mark Spitznagel Chief Investment Officer Universa Investments L.P.

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Appendix: A More Complete Risk Mitigation Scorecard

Portfolio CAGR (%) Strategy

1Y

5Y

10Y

10Y min

Universa Tail Hedge (3.33%) + SPX (96.67%)

15.7

12.2

12.3

0.6

iShares 20Y+ Treasury (25%) + SPX (75%)

12.8

12.0

9.7

-15.1

CBOE Eurekahedge Long Volatility (25%) + SPX (75%)

10.9

10.4

9.1

-13.8

iShares 7Y-10Y Treasury (25%) + SPX (75%)

12.5

11.4

8.7

-19.9

HFRI Relative Value (25%) + SPX (75%)

14.0

12.2

8.7

-27.3

Gold (25%) + SPX (75%)

14.2

10.6

8.5

-25.4

HFRI Equity Hedge (25%) + SPX (75%)

15.7

12.6

8.3

-29.1

HFRI Composite (25%) + SPX (75%)

14.6

12.2

8.2

-27.5

Japanese Yen (25%) + SPX (75%)

14.5

10.7

7.9

-19.3

HFRI Macro (25%) + SPX (75%)

13.1

11.4

7.9

-23.2

Barclay CTA Index (25%) + SPX (75%)

12.4

11.2

7.9

-21.5

Swiss Franc (25%) + SPX (75%)

14.4

11.0

7.8

-23.4

1-3M Treasury (25%) + SPX (75%)

13.1

11.2

7.7

-22.8

9.7

9.1

7.6

-33.7

13.1

10.0

6.8

-32.0

4.6

2.8

3.4

-21.8

Silver (25%) + SPX (75%) CRB Commodity Index (25%) + SPX (75%) Salient Risk Parity Index (100%)

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Important Disclosures and Other Information Confidentiality. This document was provided solely to the noted recipient. This document may not be copied, distributed or otherwise reproduced without express written permission of Universa Investments L.P. (“Universa”). General Information Regarding Hypothetical and Other Performance Charts. Universa prepared the charts in this presentation. They have not been reviewed or audited b y an independent accountant or other independent testing firm. More detailed information regarding the manner in which the charts were calculated is available on request. Universa only managed the stand-alone Universa tail hedge (or “BSPP”) component c omponent of the “Universa Tail Hedge + SPX” or “risk “risk - mitigated portfolio” hypothetical returns shown. Therefore, the performance results of the combined portfolio do not reflect Universa’s actual trading and may not refl ect the impact that material economic economi c and market factors may have had on Universa’s decision -making were it actually managing a combined strategy during those time periods. Any actual fund that Universa manages will invest in different economic conditions, during periods with different volatility and in different securities than those incorporated in the hypothetical and other performance charts shown. There is no representation that any fund that Universa actually manages will perform as the hypothetical or other performance charts indicate. indicate. An investor may lose all of its investment in a BSPP portfolio. Calculation of Performance of Various Risk Mitigated Portfolios. For the period from March 2008 through February 2018, the portfolio returns were b ased on hypothetical risk-mitigated portfolios pairing the S&P 500 Total Return Index and each risk mitigation strategy with the indicated weightings (rebalanced every calendar year end). Resulting annual performance performance figures were then tracked. All returns are based on official closing prices as of the end of February 2018 except the CBOE Eurekahedge Long Volatility, Barclay CTA and HFRI Hedge Fund Indices for which preliminary estimates estimates have been used.

The stand-alone stand-alone Universa tail hedge (or “BSPP”) component of the hypothetical returns on invested inve sted capital were calculated based on monthly administrator-provided actual return data (which is net of all fees and expenses) for a series of standard, representative investors through time -- whose fund financial statements statements for each year through 2017 have also been audited. The returns include your specific specific performance from the date you started. started. Universa then expressed expressed these returns as annual returns returns on a standardized standardized 10% (of “BSPP “BSPP notional”) capital investment at the start of each year (to standardize across different historical preferences of capital fun ding among different accounts). To account for the time needed to fully implement or wind down a BSPP portfolio, monthly administrator-provided return data has always included an incremental 3-month lag for investor-directed notional sizing increases (applying the average of an y intra-month increase to the entire month), and any variations as approp riate, as well as for investor-directed notional reductions (applying the full reduction after 3 months on month-end, unless the notional reduction was full and and Universa accelerated accelerated it as appropriate). Lastly, the BSPP returns returns from March 2008 through August 2008 were generated in a separately-managed account for which there are no administrator statements or audits. Therefore, the calculation conservatively conservatively assumes a 100% loss on invested capital over that entire time period. Actual Performance Results for Individual BSPP Funds Differ. The actual BSPP performance results incorporated in the returns shown differ from the actual performance results for other BSPP clients during those periods. The differences in performance are due to the differences in trading for each BSPP fund for most periods during that time. It can take several months for Universa to fully implement the BSPP strategy for new BSPP funds (especially those with significant notional amounts), and thus the performance during the periods before full implementati implementation on of the strategy strategy does not reflect a BSPP strategy’s performance when fully invested. In addition, any client can at any time (and during 2008 and 2009 some clients did) request one or more of an adjustment to a notional amount, purchase or sale of individual positions in a BSPP portfolio, liquidation of an entire portfolio, or withdrawal of excess margin, and some clients have restricted lists that limit the securities in which Universa can invest on their behalf. As well, actual BSPP performance results may differ in instances where expenses attributed to administrative, audit, legal, or other costs varied based on the size of the investment of the client. These decisions by individual clients lead to significant differences in performance among client accounts and thus it is difficult to select any BSPP fund during those periods that accurately reflects the performance of the BSPP strategy (without the effect of individual client decisionmaking). Universa believes, however, however, that the performance shown from September 2008 through the present is a fair representation representation of an actual BSPP client’s performance during the period shown. Monthly performance information for investors or client accounts accounts is generally available on request from Universa. CFTC-Required Disclosure re Hypothetical Performance. Universa only managed the BSPP component o f the “Universa Tail Hedge + SPX” or “risk -mitigated portfolio” hypothetical returns shown. Therefore, the performance results of the combined portfolio are based on simulated or h ypothetical performance results that have certain inherent limitations. Unlike the results in an actual performance record, these results do not represent actual trading. Also, because these trades have not actually been executed, these results may have underor over-compensated for the impact, if any, of certain market factors, such as lack of liquidity. Simulated or hypothetical trading programs in general are also subject to the fact that they are designed with the benefit of hindsight. No representation is being made that any account or fund will or is likely to achieve profits or losses similar to those being shown. Comparisons to Other Risk Mitigation Strategies and SPX. Universa compares the hypothetical returns of a portfolio combining the SPX with the BSPP to the hypothetical returns of the SPX paired with other risk mitigation strategies solely for illustrative purposes; the investments in the BSPP strategy are are entirely different from the investments in those other strategies. strategies. In addition, Universa’s Universa’s BSPP clients are likely to compare the performance of a stand-alone investment in publicly-traded equities (for which the SPX is a proxy) with a paired investment in the SPX and the BSPP, so Universa includes the performance of the SPX as well in this presentation. The SPX

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is an unmanaged, capitalization-weighted capitalization-weighted index of the common stocks of 500 large U.S. companies designed to measure the performance of the broad U.S. economy. In contrast, the BSPP strategy invests in options, futures (including options thereon) and oth er instruments as well as short sales, and includes a component designed to profit during months in which the SPX experiences significant declines. The SPX’s performance reflects reflects the reinvestment of interest, interest, dividends and other other earnings. No Duty to Update. Neither Un iversa no r any of its affiliates assumes any duty to update or correct any information in this document for subsequent changes of any kind.

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Universa Decennial Letter 2018

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