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Logica Capital September 2021 LOGICAFUNDS.COM 424.652.9520

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Logica Capital – September 2021 Summary

Logica Absolute Return (LAR) - Upside/Downside Convexity - No Correlation

• Tactical/dynamic balanced Put/Call allocation –Straddle

Logica Tail Risk (LTR) - Max Downside Convexity – Strong Negative Correlation

• Tactical/dynamic downside tilted Put/Call allocation – Ratio Straddle

Summary: Equity markets broke trend in September with their first “significant” down month of

2021 (and only the 2nd down month overall for the year). VIX reached its highest level since May,

but quickly – and challengingly - retreated. Logica’s strategies suffered from poor

timing/sequencing in September – an outcome that is bound to happen once in a while, but

disappointing nonetheless.

1 Returns above are gross of fees. LAR Fund -2.47% (net), LTR Fund -0.03% (net) for September 2021.

2 Naïve Straddle Return: a 1.5 month out, S&P 500 at-the-money put and call bought on the final trading day of prior month and sold on the final

trading day of current month. This return on premium is divided by a factor of 6 to be comparable to Logica’s typical AUM-to-premium ratio.

3 Naïve Ratio Straddle Return: a 1.5 month out, S&P 500 at-the-money put and at-the-money call (divided by 2) bought on the final trading day of

prior month and sold on the final trading day of current month. This return on premium is divided by a factor of 6 to be comparable to Logica’s

typical AUM-to-premium ratio.

Sep. 2021 QTD YTD 2020

Logica Absolute Return1 -2.2% +0.9% +2.8% +14.9%

Naïve Straddle (1 put, 1 call)2 +5.7% +4.1% -12.1% +12.9%

CBOE Volatility Index (VIX) pts 6.7 +7.3 0.4 +9.0

CBOE Volatility Index (VIX) % chg +40.4% +46.2% +1.7% +65.1%

S&P 500 -4.8% +0.2% +14.7% +17.4%

Sep. 2021 QTD YTD 2020

Logica Tail Risk1 +0.1% +0.8% -1.5% +15.1%

Naïve Ratio Straddle (1 put, 0.5 call)3 +12.8% +2.8% -23.2% +0.5%

CBOE Volatility Index (VIX) pts 6.7 +7.3 0.4 +9.0

CBOE Volatility Index (VIX) % chg +40.4% +46.2% +1.7% +65.1%

S&P 500 -4.8% +0.2% +14.7% +17.4%


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Commentary & Portfolio Return Attribution

“Good timing is invisible. Bad timing sticks out by a mile”

-Tony Corinda

There were 3 main drivers of performance in September. As a starter, Single Stock Calls

disproportionately disappointed, with most of the loss attributed to our Growth/Tech and Health Care

positions. As a proxy/benchmark to illustrate the environment for these sectors in September, both the

NASDAQ 100 (QQQ) and SPDR Healthcare ETF (XLV) were down more than -5.5% on the month. Our

smaller decline was thanks to our more defensive single stock calls, which fared very well. Though not

so noticeable given the overall down month in LAR, they significantly buoyed the long side of the

portfolio (for reference, this is the module that we introduced in late 2020 - following our lengthy R&D

into the “rotation effect” to aid in the factor rotation and associated volatility we foresaw. This module

has performed materially better than our more “aggressive” Single Stock portfolio since its introduction

in October 2020).

Second, after hopes of a turn in trend, our Macro Overlay demonstrated significant weakness, led by

heavy declines in Long-Term Treasuries and Gold (iShares 20-year Bond ETF lost more than -2% on the

month; similarly, SPDR Gold Trust GLD ETF lost just over -3%). Our exposure to the US Dollar helped to

offset some of the losses in this bucket, but the lack of protection provided by Long-Term Treasuries

given an equity market decline was a focus of disappointment. Notably, we have maintained a reduced

exposure to this module for many months now, expecting some weakness in these assets across the

board, but are still somewhat surprised by the outsized weakness in the face of “risk off” moments. As

a consequence, we are further investigating the continued value of parts of this overlay (we introduced

our thinking on this in our June 2021 monthly letter in the section titled “So what’s the deal with

60/40?”).

MTD QTD YTD

S&P Puts 2.49% 0.33% -6.62%

Fast Scalping 1.38% 0.49% -3.55%

Slow Scalping 1.10% -0.15% -3.07%

S&P Calls -2.24% 0.23% 6.32%

Fast Scalping -0.37% -0.23% 1.53%

Slow Scalping -1.87% 0.45% 4.78%

Single Stock Calls -2.14% 0.11% 3.35%

Macro Overlay -0.34% 0.23% -0.19%

Gold -0.30% -0.12% -0.45%

Long-Term Treasuries -0.39% -0.04% -0.41%

US Dollar 0.35% 0.39% 0.67%

Logica Absolute Return -2.2% +0.9% +2.9%

Attribution

MTD QTD YTD

S&P Puts 2.58% 0.47% -5.62%

Fast Scalping 1.46% 0.58% -2.91%

Slow Scalping 1.12% -0.11% -2.71%

S&P Calls -1.15% 0.06% 2.65%

Fast Scalping -0.19% -0.12% 0.71%

Slow Scalping -0.95% 0.18% 1.94%

Single Stock Calls -0.95% 0.10% 1.64%

Macro Overlay -0.34% 0.22% -0.20%

Gold -0.29% -0.09% -0.52%

Long-Term Treasuries -0.38% -0.06% -0.47%

US Dollar 0.33% 0.37% 0.80%

Logica Tail Risk 0.1% +0.9% -1.5%

Attribution


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Lastly, we were affected by poor timing on our scalping modules given the back-and-forth undulations

of volatility (spike-retreat-spike-retreat, etc..), or broadly, the indecisiveness of the market along its

general path. In a month like September, where implied volatility had a nice spike (but didn’t give it all

back intra-month), our expectation is for our strategies to be up nicely. But we are, at times, subject to

sequencing/timing issues associated with meaningful relief rallies during the fall, that while expected

to occur every so often, are frustrating when they do occur, and we are caught offside.

A timely baseball analogy might help here. Baseball statistics are notoriously noisy, and subject to small

sample size biases in the short-term. Every fan has experienced a game when team A seems to convert all

its opportunities, while team B leaves runners on base, inning after inning. Team A and B may have the

exact same long-term hitting expectancy, but in the short-term, things go poorly, and they just can’t get

the big hit with runners on base. The hits they do get are with no runners on base. Most SABRmetrics

aficionados chalk this up simply to bad sequencing which evens out over the long run. An example this

season is that of the LA Dodgers and San Francisco Giants. The Dodgers had a massive advantage in run

differential (runs scored minus runs given up – at 269-210) over the course of the regular season but have

a worse record than the Giants overall. Given run differential, the expected win/loss record for the Dodgers

is 109-53, and 103-59 for the Giants. But San Francisco has defied the odds and wound up with a better

actual, realized record through the end of the regular season. According to FiveThirtyEight, however, the

Dodgers ELO is still significantly better than the Giants, and the Dodgers are heavy favorites to win the 5-

game series.

This sequencing phenomenon is worth more focus. For perspective, since 2005 – the start of our back-

testing framework and through our live performance – there were 11 calendar months where the S&P

500 Index was down between 3% and 6% (ignoring the “current” month, Sep. 2021). LAR’s average

return over these was +3.3%, and LTR’s was +5.1%. However, LAR experienced 4 negative monthly

returns out of those 11. On average, of these negative months, LAR was -2.02%, which is tightly in line

with this past month’s performance. (Of note: the average positive month for LAR and LTR during these

periods has been +6.3% and +8.12%, respectively.)

“Through my research, I found that vulnerability is the glue that holds relationships

together. It's the magic sauce.”

-Brene Brown

Moving on to the bigger question: why does our process ever allow for a downside outcome during a

downside market? In recent letters, we’ve discussed that instead of shorting volatility as most other vol

managers do, we are unwilling to take any “short vol” risk, and so the risk we’re willing to take is that of


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delta risk. Recall our “shifted” distributions illustrating our “in the middle” of the distribution risk

profile:

Within the “risk” portion of our expected distribution, there are a couple things happening. The “delta

risk” we take is that of a mean-reversion bet, the point of which is to monetize volatility spikes, and

anticipate a bounce-back in the underlying. We do this so long as our model isn’t giving us a RED FLAG

that a bigger disaster is pending. When our model gives us this RED FLAG, we call it a “Phase Shift.” It’s

at this point that we halt our mean reversionary style and significantly increase our downside exposure

- eliminating our positive delta risk and trading differently for several days, ensuring a higher Put load

than usual. (As a reminder, even outside of Phase Shift, we always carry meaningful Put exposure on the

books. In the event of catastrophe, even with a positive delta risk in play, the Puts quickly overtake the

Calls. Please refer to our July 2021 monthly letter for a nice exposition on this idea).

Let’s illustrate a simple sequence, which consists of Mean Reversion trade 1 (MR1), followed by another

Mean Reversion trade 2 (MR2), and finally resulting in a Phase Shift, as the underlying keeps going down

(moving right to left in our distribution):


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During these points, we are trading similarly to how one might play blackjack: one always doubles down

on 11 (MR1 and MR2), as the odds get more highly in favor of winning on the next play. Similarly, we did

exactly this, and lost each time. But then when our model goes into Phase Shift, as happened, we

suddenly reverse posture, preparing for a major asymmetric payoff in the other direction (as Volatility

continues expanding rather than reverting). It’s a bit like having the bases loaded with 2 outs in

baseball. Over time, the strategy that will generate the most runs will be to take a full swing and try to

drive the ball, either getting a home run or a double. However, this is a low hit rate proposition: doubles

and homers with the bases loaded don’t materialize that often. And when they don’t, fans are left

wondering why a hitter didn’t just “put the ball in play,” and why a manager didn’t simply play for at

least one run. Taking a big swing when the bases are loaded (when our model shouts at us that a

pending Phase Shift may be upon us) is a calculated risk that our strategy takes; this will achieve the

best long-term return and, as importantly, the most convexity.

(It wasn’t always this way, but just like in investing, baseball has come to understand that artificially

limiting run output via bunting, or only trying to hit singles, is a lower expected value than letting hitters

swing away in these “juicy” situations. The only exceptions are very specific in-game situations, where 1

run may win you the game. Since investing is a continuous “game” that doesn’t usually have a specific

endpoint, this is where the 2 “games” diverge, and the analogy becomes a bit less apt.)

But it was a false positive. Phase Shift can be a frustrating thing for us to implement when it doesn’t

follow through. In fact, it’s precisely counter to our general monetization policy: this part of the strategy

gears up for more volatility following a volatility spike/market downturn (and significantly delays

monetization). We know that Phase Shift has a low hit rate: most of the time, the market recovers, and

things go back to normal. But, when Phase Shift is correct, significantly outsized gains are well within


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reach. And this is what helps buoy an overall portfolio in the worst of times. Doing things in this

manner controls our risk and long term expected return but can hurt us more when we are wrong;

when the market does not follow through, but instead mean-reverts, as happened in September.

The crucial point, however, is that along this jagged path, we take calculated risks in the middle

of the distribution, and ones that are bounded by our Phase Shift parameter.

As you might be able to surmise by now, during September, both the mean-reversion trading and the

Phase Shift implementation got it wrong, so ended up whipsawing us at each turn. We are as frustrated

as anyone by saying that these sequences just happen sometimes; and even with our process taking the

right bets in being downside controlled for highly asymmetric payoff opportunities. The key is for us to

reflect on these and evaluate; to ensure these outcomes are within expectation, and to alter course if

we feel they are not. September, while somewhat disappointing, was in fact well within expectation.

So, we roll on.

All that said, we can see the normalcy of daily returns in our scatter plots:


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Of particular interest, September was an ideal month for a naïve implementation of both a straddle,

and a ratio straddle. This will typically be the case when implied volatility is positive for the month and

the final day is the absolute low point for the month:


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In closing, we continue to remain confident in our process, and just like a professional Blackjack player

doubling down each time they are dealt a sum of ‘11’ (given the high number of 10 value cards in the

average deck enabling the next card dealt to achieve ‘21’), if the market presented the same setup to us

once more, we would take the same bet, for those are the best odds for asymmetric upside at bounded

risk.


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Business Update

We are happy to share that the Logica team continues to grow. Over recent months, we have added two

new team members, including Alexandra Imbro, who will be assisting our client relations team, and

Jam Zovein, who will be heading up our Product Development & Strategy. Alex comes to us from a long

stint at Aegis Capital, and Jam comes to us with long term experience in similar institutional roles at

Nuveen, Wilshire Associates, and quant hedge fund Algert Global. Welcome aboard to our new team

members!

Logica Strategy Details

Note: We have comprehensive statistics and metrics available for our strategies, but only include a

select few to highlight what we believe is our most valuable contribution to any larger portfolio.

If you would like to learn more about our strategies, please reach out to:

[email protected]

424-652-9500

Follow Wayne on Twitter @WayneHimelsein

mailto:[email protected]

https://twitter.com/WayneHimelsein

https://twitter.com/WayneHimelsein


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*HFRX Indices have been scaled up to 15% annualized volatility to be comparable to LAR and S&P 500.

Logica Absolute Return 1.76 (0.14) 0.12

S&P 500 Index 1.21 1.00 (0.37)

HFRX Macro Index 0.08 0.34 (0.17)

HFRX Equity Mkt. Neutral Index (0.34) 0.32 (1.33)

Correlation

(S&P 500)Sortino Skew

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec YTD

2021 0.5% -1.7% -0.1% 1.2% 0.5% 1.5% 2.1% 1.1% -2.2% 2.82%

2020 4.7% 6.3% 8.6% 0.6% -1.4% 1.1% 0.0% -1.3% -1.1% -1.2% -2.1% 0.4% 14.93%

2019 1.9% 0.3% 7.2% -1.2% -3.8% 9.2% 5.4% 3.0% -8.5% -6.0% 0.3% 0.1% 6.53%

2018 7.3% 10.8% 3.6% 0.7% 7.2% -2.3% -2.8% 6.5% -0.2% -5.4% 1.8% 6.6% 37.98%

2017 1.2% 5.3% -1.2% -1.4% 3.2% -5.6% -2.0% 3.2% 0.7% 8.9% 2.4% -1.1% 13.63%

2016 6.0% 1.9% -0.9% -4.3% 3.9% 6.0% 1.7% -2.1% 2.0% -5.1% 1.8% 0.9% 11.58%

2015 8.8% -3.6% 5.2% -8.4% 4.4% -3.4% 2.3% 8.4% 1.6% 1.2% -3.4% -5.0% 6.62%

Logica Absolute Return - Monthly Returns

Logica Absolute Return

2015-2019 stats & grid, reconstitution of live sub-strategies

2005 to present growth of $1000 chart, simulation

Jan 2020-Present Live


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*EHTR Index has been scaled up to 17% annualized volatility to be comparable to LTR.

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec YTD

2021 0.5% -1.9% -1.4% -0.2% 0.2% 0.4% 1.0% -0.3% 0.1% -1.55%

2020 3.1% 10.2% 13.2% -0.7% -2.6% 0.4% -1.4% -2.7% 0.7% -0.9% -3.1% -0.6% 15.12%

2019 -1.1% -1.8% 5.4% -2.5% 0.8% 4.2% 4.2% 2.7% -7.1% -6.6% -1.3% -1.9% -5.78%

2018 2.2% 11.9% 5.8% 0.6% 3.1% -2.7% -3.3% 3.1% -0.7% 0.8% 1.7% 13.1% 39.93%

2017 0.4% 2.9% -0.6% -1.6% 0.7% -4.6% -2.6% 1.8% -1.2% 4.5% 0.0% -1.2% -1.78%

2016 10.4% 3.5% -4.6% -2.5% 1.7% 4.3% -0.2% -1.8% 1.0% -3.0% -3.1% -0.6% 4.33%

2015 11.2% -6.9% 3.4% -6.6% 1.2% -3.6% 0.2% 12.9% 3.3% -1.3% -3.4% -5.0% 3.29%

Logica Tail Risk - Monthly Returns

Logica Tail Risk

2015-2019 stats & grid, reconstitution of live sub-strategies

2005 to present growth of $1000 chart, simulation

Jan 2020-Present Live

Logica Tail Risk 1.08 (0.56) 1.15

S&P 500 1.21 1.00 (0.37)

EH Tail Risk Index (1.10) (0.54) 6.29

SortinoCorrelation

(S&P 500)Skew

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