Total return of the Sigma1 Fund, based on after hours quote data, is 10.26%.  During the same period the CBOE S&P 500 Buy/Write Index (BXM) and S&P 500 returns were approximately 9.58% and 18.1%.  Since the Sigma1 Fund makes extensive use of S&p 500 call writes with expirations of 1-4 months, it is reasonable to expect that Fund returns will bear some similarity to BXM returns.

Long equity positions of less than 100% retarded returns relative to BXM in October and November.  Leveraged long equity positions in excess of 100% in January boosted BXM-relative performance.

Note:  All data are preliminary and unaudited.  For a (hypothetical) investor, returns would be lower after fees and expenses.

As of tonight, the Sigma1 private-equity fund is up 6.44% since its inception less than 4 months ago.  I’ve used this time to begin putting several of my investing ideas into practice.  I started out cautiously the first couple of weeks, making sure I knew enough about the new trading platform I was using.  I’ve learned that it is a superb trading platform, and well-worth the learning curving.

I’ve put together an investing strategy and portfolio that I believe is well-positioned to benefit from the current economic and investing climate.  If I force myself to pin down the current strategy with words, I’d call it a USD-focused, long-short, equity-dominated strategy with global exposure and significant currency-risk hedging.  The current Sigma1 investing strategy is also macro-focused and sector neutral.  As mentioned in a previous post, I’m also using options to re-shape the portfolio risk-profile, but only modestly.

One thing that is becoming clear to me is that I value the flexibility to change tactics and strategies as my analysis and judgment sees fit.  For example I have currently hedged the majority of Sigma1′s foreign currency exposure, but some day I may wish to seek foreign currency exposure rather than avoid it.   Similarly I may wish to step away from a sector-neutral stance and go long some sectors and short others.

I believe I will employ an evolving strategy with Sigma1 that seeks to avoid sharp discontinuities, but evolves with changes in market and economic conditions in the US and around the globe.

As I read “How I Became a Quant“  it seems that quants are more often born than made.  Of course it takes time to time to become a quant in the same way it takes time to become a doctor, lawyer, or engineer.  The difference is that one does not exactly just get a degree in quantitative financial analysis to become a quant.

Being a quant is very much like “having the knack” a la Dilbert.

Being a quant and becoming a successful quant are not one in the same.   So I must consider what might possibly give me an edge?   How can I possibly out-quant other quants?   The short answer is by providing “common sense descriptions and open-the-kimono transparency to investors”.   The long answer is to long to fit within the margins of Arithmetica or this blog post.   BTW, the other part of the long-short answer is by offering much lower fees than other hedge funds.

I’ve discovered in my electrical engineering career, that having the best data is not, by itself convincing.  Neither is having the best analysis, or proofs.   Sadly, having adequate data and analysis PLUS the right amount of polished persona, political acumen, and just a bit of luck tends to be the best alchemy for “winning arguments” in engineering.   Choices that alter the status quo are particularly difficult to influence with “mere data”.   Unfortunately for the would-be-quant, winning the initial argument is just the beginning.   If the choice proves to be  successful, getting a proper share of credit for ones part in the decision becomes its own challenge.

Finding a path to more direct to credit (or blame!) for one’s ideas appeals to me.   I am happy to put my own skin (money) in the game, provide detailed disclosure to my co-investors, and let the chips fall where they may.

With finance the proof is in the pudding.  Yes, luck, good or bad, is also a significant ingredient.  There are benchmarking tools (like Fama/French) to help separate luck vs. skill… but how many sophisticated investors bother to read — let alone grok — the Morningstar reports?   Shy meticulous self-motivated due diligence, sophisticated investors have these information proxies:  1) low fees, 2) track record, 3) fund transparency, 4) fund manager skin-in-the game.  In fact, without adequate fund transparency, rating agencies like Morningstar cannot even compute Fama/French 3-factor models.  Such folks are well-served with a proper mix of low-cost index ETFs and/or funds.

I ask myself, “For folks like these, what kind of ancillary fund would I seek?”   The answers I, as a quant, come up with is 1) low fees, 2) kick-ass transparency, 3) significant skin-in-the-game, 4) awesome algorithms and compute infrastructure.  I’m working hard on the first three.  I’m afraid #4 will only come with time and capt ital, lots of capital.  I’m hoping that 3 of 4, plus just a bit of luck perhaps, will produce that time and capital.  Until then,  3 out of 4 aint bad.

In today’s investment environment, if one wants to make any positive return on investment (and avoid negative inflation-adjusted returns) one must accept some risk.   CAPM presents a simplified model of risk as embodied by, sigma, or standard deviation.  Interestingly, the basic CAPM risk model does not encompass the concept of utility.

It turns out that it is relatively easy to shape, or re-shape, an asset’s risk-profile.  Assume that, on an amortized basis, option trades constitute a zero-sum game;  It is possible to reshape Gaussian risk within a Gaussian-risk band… for free.  Such risk-shaping techniques, applied to maximizing utility, are key principles of the Sigma1 Fund.  [One example is a bear put spread paired with an equal coupling of the underlying -- creating an utility-positive position play.  Another such utility-positive play is a covered call.]

Of course, the trading of options is a potentially (slightly) negative-sum game after trading expenses are accounted for.  The key take-away is that options (and synthetic options) are inexpensive tools via which to maximize utility for “almost free”.  Almost free can easily be 30-35 basis points per annum  (see Σ1 Fund Strategy).  With optimization and improved economy-of-scale this “almost free” component can be further improved.

In essence, Σ1 seeks to create  a classically-CAPM-optimized portfolio and then re-shape its risk profile in a utility-friendly manner.  Positive alpha is the penultimate goal, but reshaping sigma for enhanced utility is the ultimate goal.

Sigma1 Position Overview

I created the Σ1 Fund for several reasons.

1. A proprietary fund in which to invest some of my LLC’s assets.
2. A real-world investment vehicle in which to test, refine, and showcase  investing and trading strategies.
3. A hedge fund where my LLC can also make revenue from fees. (Fees as low as reasonably possible.)
4. A fund in which to directly invest some of my personal assets.
5. A fund were I can invest some of my retirement assets (e.g. 401K).

As of today I have only achieved goal #1.  I am making progress on goal #2.  If all goes well, the Σ1 Fund will be making progress towards goal #3 in the coming months.

I am happy to say that I trade modest quantities of stock (100-500 shares) for about the price of a candy bar.  Σ1′s stock and ETF trading costs are very low,  averaging about  3-4 basis points (0.03% to 0.04%) for a round trip on an typical equity position.  So far I often get the midprice or better on equity trades so the spread is also quite inexpensive.

Σ1 option trades are similarly inexpensive in absolute terms, but the spreads are wider and the dollar amount bought or sold is much lower.  Typical Σ1 option trade prices, currently, range from $300 to $1000 per trade.  The exchange fee the Σ1 Fund pays is thus as high as 35 basis points… not cheap.  Typical is probably closer to 20 basis points… still significant.  About half of  Σ1 Fund’s call writes expire out of the money, so I’d estimate the round-trip trading costs at about 30-35 BP.  In terms of the option price, this is a bit pricey. In terms of the overall portfolio it is more manageable.

It is not the direct trading costs for options, but the spreads that have my attention lately. It is common to see spreads of 2.5% on the ETF options that  Σ1 trades.  Assuming that the unweighted midprice (NBBO bid+ask, div 2) is the fair price, how close can I get to it on average (amortized over all fund option trades)?  I have not thoroughly analyzed the data yet, but I’d guesstimate I’m missing the option midprice by about 70 BP.  Add that to net option trading costs and we’re talking 100 BP… a whole 1%.

If my goal was to strictly follow the BXM, with monthly call writes, 100 BP could easily translate to a portfolio BP of 60. Unacceptable.  Therefore I’ve been writing longer calls (2-6 months).  This helps keep costs, seen and “unseen” down.  Nonetheless I’m strongly considering using BXM as a fund benchmark.

As fund assets grow, assuming they do, the relative direct option trading costs will decrease.  Perhaps with significantly larger trades 30-35 BP will, over time, decrease to 10 BP.  But the unseen spread-related ratio will not improve with increased trading volume or trade size. That is why I am researching trading strategies and algorithms.

Since I am not striving for high leverage (say >3X) I am relatively indifferent to whether a given option is technically covered or merely effectively covered.  Do I really care if my SPY call writes are covered with SPY or an equivalent amount of VOO?  Not really.  Do I really care if whether I write 1o Dec SPY calls at 117 versus 5 at 116 plus 5 at 118?  Not much.  Except if one gives me a better overall price… or the best chance at a better price.

If you’ve ever optimized digital logic with a K-map you  know how valuable “don’t cares” can be.  I’m trying to identify don’t cares for Σ1, and exploring ways to get higher prices for my option writes.  I’m also learning more about the nuances and details of exchanges and trading.  My goal is simple: buy low, sell high.

The PowerShares S&P 500 BuyWrite ETF (PBP) and iPath CBOE S&P 500 Buy/Write Index ETN (BWV) both seek to track the BXM Index.  Each advertises a 0.75% expense ratio/annual fee.  The BXM Index reports an “18 years [history], generating a return comparable to that of the S&P 500 with approximately two-thirds of the risk”.  Should the next 18 years be similar, the compound annual growth rate would be roughly 11.77%-0.75% (11.02%) vs an S&P 500 return (via SPY) of 11.67%-0.06 (11.60%) with .6688 relative deviation.  So, all things equal over 18 years, SPY would have a better return with a higher volatility.  With 3-month T-Bills providing returns of jack & squat (about 13 basis points), PBP and BWV would be expected to provide superior Treynor ratios.

The 75-basis-point expense ratios are tolerable, but still a bit high for essentially-passive funds.  I have a bias towards EFTs over ETNs (because ETFs have a much lower counter-party risk.) and may consider buying some PBP shares for the Sigma1 Fund… if NAVs sufficiently exceed prices. Currently Sigma1′s annualized annualized trading expenses to emulate BXM have ranged from 10 to 55 basis points so the possibility to short PBP and/or BWV also exists.

For the last several weeks, behind-the-scenes work on the Σ1 Fund has been quietly progressing.  Now some of that work is finally being unveiled.  The most visible change is an upgrade to the more prestigious sigma1.com top-level domain (TLD) name.  This involved acquiring the TLD in a private auction, numerous technical tasks assisted by Endress|Analytics, and a compute infrastructure upgrade with the help of a Linux system expert.

On the business front, I’ve been learning more about SEC regulations.  What I have learned so far about Accredited Investors is encouraging.  More broadly, I’ve found this SEC Small Business Q&A page helpful.  So far I’ve only scratched the surface what I need to know before the Σ1 Fund can begin raising outside capital investment.

On the investing front, I’ve been expanding and refining Σ1′s holdings.  Σ1 currently holds 4 long equity ETF  positions, 1 bond ETF long position, 2 equity-option short call positions, and 1 equity-option long put position.  I’m beginning formulate a plan/algorithm for maximizing Σ1′s tax efficiency in the equity+equity-options space.  Unfortunately I have not yet determined a good method for reconciling Σ1′s fixed-income investment objectives with tax-efficiency, so tax-efficiency for this portion of the portfolio will be sub-optimal.

I’m beginning to consider arbitrage strategies in both the fixed-income and ETF/ETN investment spaces.  I’m considering long/short positions to capitalize on possible bond-fund misspricing.  I’m also considering long/short positions to take advantage of certain ETN tracking and pricing inefficiencies.  Σ1′s new Linux compute infrastructure should provide a software-development platform for creating custom software to model, test, and refine some of these arbitrage ideas.  In general these ideas involve slow arbitrage meaning I expect them to play out over weeks, months, or even years.  While it is possible these concepts might also apply to fast arbitrage (aka high-frequency trading), I plan to focus on slow arbitrage for the foreseeable future.

At its heart, the Σ1 Fund takes a quantitative investing approach.  I am not primarily a stock picker, though over the years I’ve had success with my occasional picks.  I am primarily a quant believing in some market mispricing, particularly equity option miss-pricing and well as bond miss-pricing.  However, I believe that markets are generally efficient.  In particular true arbitrage opportunities are infrequent and fleeting.

The Σ1 Fund will be in many ways a hedge fund. First, it will systematically hedge many of its stock positions.  Second, it will apply margin and leverage.

Initially I wish to apply an investing approach and principles I’ve been developing over the past 5 years. I plan to refine, tune, and rigorously test this investing approach.  Over time, I want to code this approach so that more and more aspects are algorithmically defined.    Testing and “proving” both the broad conceptual framework as well as testing specific algorithmic implementations will require months, perhaps a year, of work.

I ask myself what value-adds will distinguish the Σ1 Fund from the numerous fund out there.  Here are a few key distinguishing points:

1. Innovative, industry-leading transparency for a hedge fund.
2. Far lower overall expenses than typical for a hedge fund.
3. Far lower investment minimums than a typical hedge fund.

If the fund performs well, one important Balhiser LLC asset, will be the Σ1 Fund strategies and trading algorithms. One challenge I foresee is a dissonance between  transparency and secrecy of the underlying trading algorithms and code.  Disclosure of the specific algorithms will not be part of the contract.  However, slightly-delayed disclosure of the fund’s holdings, under NDA, will be available to members of the proprietary trading group as well as their CPA.  This will be a delicate balance, requiring the fund to place a measure of trust into the hands of its clients.  When in doubt I hope to err on the side of greater transparency.

While there is much more on my mind, I will wrap things up for today’s blog.  I wrap up with a few other principles:

• No “window dressing”.  This a cheap and transparency-reducing trick.
• Benchmarking.  The Σ1 Fund is a hybrid fund and will be hard to categorize.  Nonetheless I have tentatively chosen to select two benchmark indexes:

DJ Wilshire 5000
MSCI U.S. Broad Market Index

** Disclaimer:  This contains forward-looking statements that reflect the author’s current thoughts and assumptions.  As such all comments, etc are subject to significant potential change and revision.