Our Business: Portfolio Risk Mitigation

I bumped into a friend tonight at dinner, who introduced me to one of his friends who also happens to be a very successful software entrepreneur.  Naturally, I mentioned that I, too am an entrepreneur with a financial software product for portfolio optimization.

We spoke about the technical side of the HAL0 software and he provided me with some fresh perspectives about the technical side of my build-out plan.  Then the conversation transitioned over to the business side.  He challenged me to explain the value proposition of my product without using technical language.  I started with “software to build better portfolios.”  OK, better how?  “Optimized for lower risk, and higher return.”  And?  “Supporting risk models beyond standard deviation.”  Nope — too technical.

I appreciated the grilling.  I kept saying “Thank you, bring it on,” and “yes, that criticism stung a bit, but this is useful.”

Trying to pin down the essence of the business, I found myself oscillating back and forth from “too general” to “too technical.”  Finally we pieced together a tagline:  Portfolio Risk Mitigation.  It is succinct and non-technical.  It seems likely that these three words capture the essence of the company and its flagship product.

Now for the business plan.

The Market

As of 2010, there were $17.5 trillion dollars in retirement investments, including $4.7 trillion in IRAs, and just over $3 trillion in 401(K)s according to Investment Company Institute. Annual advice fees on these assets typically range from 0.4% to 0.75% according to this Multnomah Group White Paper. And according to Zero Hedge, total US household financial assets totaled $51.9 trillion in Q2 2012.  Applying a very conservative 0.4% value for advice fees on these assets, this translates to over $207 billion in annual revenue for investment portfolio advice.  This is the primary market Sigma1 Financial seeks to tap into.

The Market Segment

I believe the best way to gain a share of the $200+ billion financial and portfolio advice market is by providing best-in-class portfolio-optimization software to companies that provide wealth management and investment advice.  These companies pay in-house analysts to find financial assets with superior returns.  They also pay portfolio managers (AKA wealth managers) to transform their proprietary research into investment portfolios tailored to the needs of their individual investors.  Sigma1 Financial software provides powerful portfolio optimization and analytics that helps portfolio managers mitigate risk, while maintaining or even boosting investment returns for their clients.

We are presently working will select beta partners in this market segment to learn about their specific wants, needs, and requirements. Our goal is to continuously adapt and improve our products within this market segment. We have the core technology; our current challenge is presenting it in a way that easily integrates with the business practices of our beta partners.

The Competition

The number one competing product in the field is MCSI’s Barra Optimizer.  To the best of my knowledge MSCI does not publish pricing information for this product.  I have heard, however, that large users of the software pay through the nose — aka millions of dollars per year.

There is overlap between the HAL0 portfolio-optimizer and the Barra Optimizer.  There are also unique attributes that distinguish each software product.  I will concede, that Barra currently offers better integration with a suite of financial products.  Conversely, HAL0 offers unique three-objective optimization and an optimization engine tuned for PMPT optimization.  Futher, it is my plan to initially position HAL0 pricing models as extremely competitive compared to Barra pricing.  Further, the first one or two major HAL0 customers will be in an unique position of being able to request and receive solutions to their specific requirements.

The Value Proposition

To beta partners (and soon, paying clients) the proposition is simple and compelling.  Provide Sigma1 with a set of assets and, optionally any or all of : min allocations, max allocations, expected return, and a sample portfolio.  If a sample portfolio is provided, I seek to provide three alternative portfolios that 1) provide superior expected return for the same risk, 2) provide lower risk at the same return, 3) provide both higher return and lower risk than the sample portfolio.

For beta partners (or potential clients), I seek to provide a model that is low-effort on their part.  For no cost,    Sigma1 provides (to select organizations) a range of optimized portfolio options.  These potential clients can then choose how to proceed from there.  Naturally, Sigma1’s goal is to impress the potential clients/partners with portfolio solutions that offer superior risk/reward characteristics.

The Bottom Line

Sigma1 offers a world-class product with low sunk costs.  This enables Sigma1 to offer an extremely competitive offering while maintaining high profit margins.  Both the client and Sigma1 stand to benefit.

Inverted Risk/Return Curves

Over 50 years of academic financial thinking is based on a kind of financial gravity:  the notion that for a relatively diverse investment portfolio, higher risk translates into higher return given a sufficiently long time horizon.  Stated simply: “Risk equals reward.”  Stated less tersely, “Return for an optimized portfolio is proportional to portfolio risk.”

As I assimilated the CAPM doctrine in grad school, part of my brain rejected some CAPM concepts even as it embraced others.  I remember seeing a graph of asset diversification that showed that randomly selected portfolios exhibited better risk/reward profiles up to 30 assets, at which point further improvement was minuscule and only asymptotically approached an “optimal” risk/reward asymptote.  That resonated.

Conversely, strict CAPM thinking implied that a well-diversified portfolio of high-beta stocks will outperform a marketed-weighted portfolio of stocks over the long-term, albeit in a zero-alpha fashion.  That concept met with cognitive dissonance.

Now, dear reader, as a reward for staying with this post this far, I will reward you with some hard-won insights.  After much risk/reward curve fitting on compute-intensive analyses, I found that the best-fit expected-return metric for assets was proportional to the square root of beta.  In my analyses I defined an asset’s beta as 36-month, monthly returns relative to the benchmark index.  Mostly, for US assets, my benchmark “index” was VTI total-return data.

Little did I know, at the time, that a brilliant financial maverick had been doing the heavy academic lifting around similar financial ideas.  His name is Bob Haugen. I only learned of the work of this kindred spirit upon his passing.

My academic number crunching on data since 1980 suggested a positive, but decreasing incremental total return vs. increasing volatility (or for increasing beta).  Bob Haugen suggested a negative incremental total return for high-volatility assets above an inflection-point of volatility.

Mr. Haugen’s lifetime of  published research dwarfs my to-date analyses. There is some consolation in the fact that I followed the data to conclusions that had more in common with Mr. Haugen’s than with the Academic Consensus.

An objective analysis of the investment approach of three investing greats will show that they have more in common with Mr. Haugen than Mr. E.M. Hypothesis (aka Mr. Efficient Markets, [Hypothesis] , not to be confused with “Mr. Market”).  Those great investors are 1) Benjamin Graham, 2) Warren Buffet, 3) Peter Lynch.

CAPM suggests that, with either optimal “risk-free”or leveraged investments a capital asset line exists — tantamount to a linear risk-reward relationship. This line is set according to an unique tangent point to the efficient frontier curve of expected volatility to expected return.

My research at Sigma1 suggests a modified curve with a tangent point portfolio comprised, generally, of a greater proportion of low volatility assets than CAPM would indicate.  In other words, my back-testing at Sigma1 Financial suggests that a different mix, favoring lower-volatility assets is optimal.  The Sigma1 CAL (capital allocation line) is different and based on a different asset mix.  Nonetheless, the slope (first derivative) of the Sigma1 efficient frontier is always upward sloping.

Mr. Haugen’s research indicates that, in theory, the efficient frontier curve past a critical point begins sloping downward with as portfolio volatility increases. (Arguably the curve past the critical point ceases to be “efficient”, but from a parametric point it can be calculated for academic or theoretical purposes.)  An inverted risk/return curve can exist, just as an inverted Treasury yield curve can exist.

Academia routinely deletes the dominated bottom of the the parabola-like portion of the the complete “efficient frontier” curve (resembling a parabola of the form x = A + B*y^2) for allocation of two assets (commonly stocks (e.g. SPY) and bonds (e.g. AGG)).

Maybe a more thorough explanation is called for.   In the two-asset model the complete “parabola” is a parametric equation where x = Vol(t*A, (1-t)*B) and y = ER( t*A, (1-t)*B.  [Vol == Volatility or standard-deviation, ER = Expected Return)].   The bottom part of the “parabola” is excluded because it has no potential utility to any rational investor.  In the multi-weight model, x=minVol (W), y=maxER(W), and W is subject to the condition that the sum of weights in vector W = 1.  In the multi-weight, multi-asset model the underside is automatically excluded.  However there is no guarantee that there is no point where dy/dx is negative.  In fact, Bob Haugen’s research suggests that negative slopes (dy/dx) are possible, even likely, for many collections of assets.

Time prevents me from following this financial rabbit hole to its end.  However I will point out the increasing popularity and short-run success of low-volatility ETFs such as SPLV, USMV, and EEMV.  I am invested in them, and so far am pleased with their high returns AND lower volatilities.

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NOTE: The part about W is oversimplified for flow of reading.  The bulkier explanation is y is stepped from y = ER(W) for minVol(W) to max expected-return of all the assets (Wmax_ER_asset = 1, y = max_ER_asset_return), and each x = minVol(W) s.t. y = ER(W) and sum_of_weights(W) = 1.   Clear as mud, right?  That’s why I wrote it the other way first.

 

The Financial Squeeze?

Obama’s idea of capping retirement accounts at approximately $3,000,000 is a shot across the bow to retirement investors.  Cyprus’s bank haircut is an attack on savers.  Is Western Capitalism under assault?

A 3.8% Obamacare surcharge on investment income (for “high” earners) — this includes dividends, capital gains, passive income, interest income, etc.  Not to mention the 0.9% tax hike on MAGI (modified, adjusted gross income) above $250,000 to married couples ($200,000 for individuals).

The questions I ask myself, are “Is this a long-term trend?”  Will the takers continue to out-vote the makers?

I will have to monitor the situation, both domestically and abroad.  I would prefer to stay in the USA, but I have to mentally prepare for a seismic shift in taxes, and property rights.

In the meantime, I anticipate early changes will be gradual.  It is also likely that the fiscal pendulum that is swinging left, will eventually begin to swing back to the right.

Portfolio Management Advice. What is it worth?

The best way to manage a personal investment portfolio starts with a complete picture of assets and liabilities.  The greater the net worth, the more potential worth good portfolio-management advice offers.

For a given net worth, investment advice is most useful to investors who exhibit any of the following:

  • disinterested in investing
  • lack knowledge about certain types of investments
  • lack knowledge about the tax-implication of investment choices
  • are undisciplined in their investment approach
  • overestimate returns, or underestimate risk or risk tolerance

Here are a few examples of experiences of the above-type investors.  Keep in mind that most exhibit more than one of the above traits.

Disinterested Investor

  • Has not changed their 401K from its default 3% contribution to 100% money-markets.
  • Has a broker who trades for them, yet has no idea how their investments have compared to the S&P 500 Index  
  • $100,000 in their bank account because they are too lazy/indifferent to invest it
  • Doesn’t rebalance

Ignorant Investor

  • Only owns cash and CD’s because they don’t understand stocks, ETFs, or mutual funds
  • Trades individual stocks but doesn’t know what a P/E ratio is 

Tax-Ignorant Investor

  • Holds tax-exempt muni-bond funds in a 401K or IRA
  • Holds annuities in a 401K or IRA (for no good reason)
  • Doesn’t know about qualified dividends
  • Doesn’t consider tax consequences of unrealized gains in ETFs and mutual funds

Undisciplined “Investor”

  • Chases trends like the tech bubble.  Extremely undiversified. Often losing big money 
  • Day traders. Eventually get burned.  Then out of the stock market all together.  Then finally back in only to buy into a market top.
  • Doesn’t even know how much they’ve made or lost

Irrational Investor

  • Expects 12%+ market returns. Surprised when markets fall.
  • Thinks they can tolerate a 40% correction, then sells in panic near the market bottom when such a correction occurs

The unfortunate truth is that the vast majority of investors I’ve encountered harbor at least one of the above investing flaws.  Many of these people make 6-figure salaries.  It appears that being a complete investor is a rather rare trait.  For these reasons good investment advice can be very valuable to the majority of people who are “incomplete investors”.  In many cases %1 of net investment assets or $495 for a two-hour consultation can be quite worthwhile.

2013: Financial Solutions

Sigma1 is rolling out a new tagline for 2013.  2012 was focused on portfolio-optimization software. For 2013, the Sigma1 theme will be “Financial Solutions Software.”

In on-going beta testing,  I have learned that investment professionals want more than answers to financial analysis problems; they want “solutions.”  And each beta test has a slightly different definition for “solution.”  The most common themes are 1) easy of use, 2) web-based interface.  Beyond that enhancement requests start to diverge.

Making software easy-to-use is not easy.  It is one thing to train users on a product, it is quite another to build software that most people find intuitive.  Nonetheless this is one of the Sigma1 software development challenges of 2013.  Sigma1 is looking for additional expertise in this area.

The flexibility of HALO software will allow the development of additional ready-to-use plugins.  On the drawing board are:

  • Capital-gains optimization and management
  • Capital-gains-savvy portfolio transition planning
  • Monte Carlo simulation
  • Total-Return confidence-interval visuals
  • Built-in alternative risk measures (max draw down, max rolling annual loss)
  • Asset covariance reporting
  • Time-series portfolio stress testing
  • Sampling tools (build a representative portfolio with a smaller set of securities)
  • Data back-fill for new passive ETFs

2013 represents an opportunity for Sigma1 Software to incorporate feedback from our beta partners and transform HAL0 software from an optimization-engine into a more complete software solution suite.  Thank you beta testers for your feedback!   Sigma1 looks forward a continuing relationship that we hope will amaze you and your clients.

Variance, Semivariance Convergence

In running various assets through portfolio-optimization software, I noticed that for an undiversified set of assets there can be wide differences between portfolios with the highest Sharpe ratios versus portfolios with the Sortino ratios.  Further, if the efficient frontier of ten portfolios is constructed (based on mean-variance optimization) and sorted according to both Sharpe and Sortino ratios the ordering is very different.

If, however, the same analysis is performed on a globally-diversified set of assets the portfolios tend to converge.  The broad ribbon of of the 3-D efficient surface seen with undiversfied assets narrows until it begins to resemble a string arching smoothly through space.  The Sharpe/Sortino ordering becomes very similar with ranks seldom differing by more than 1 or 2 positions.  Portfolios E and F may rank 2 and 3 in the Sharpe ranking but rank      2 and 1 in the Sortino ranking, for example.

Variance/Semivariance divergence is wider for optimized portfolios of individual stocks.  When sector-based stock ETFs are used instead of individual stocks, the divergence narrows.  When bond- and broad-based index ETFs are optimized, the divergence narrows to the point that it could be considered by many to be insignificant.

This simple convergence observation has interesting ramifications.  First, a first-pass of faster variance optimization can be applied, followed by a slower semivariance-based refinement to more efficiently achieve a semivariance-optimized portfolio.  Second, semivariance distinctions can be very significant for non-ETF (stock-picking) and less-diversified portfolios.  Third, for globally-diversified, stock/bond, index-EFT-based portfolios, the differences between variance-optimized and semivariance-optimized portfolios are extremely subtle and minute.

 

 

Optimizing Capital Gains and Losses

Wish #1: End the Long/Short-Term Gain Distinction

Ideally there would be no differentiation between short-term and long-term gains. Whether an asset is held for a microsecond or decade, the gain or loss should receive the same tax treatment.   A gain is a gain, a loss is a loss.  But as long as the tax code retains the current short-term/long-term bifurcation, it is important to account for the impact of short and long term gains.

Wish #2: Account for Inflation on Capital Gains

If you realize a capital gain of 10%, but inflation during that time is also 10% have you really earned anything?  According the the current tax code: Yes.  In real terms: No.  In fact, after taxes, you have lost real purchasing power.

I propose that the tax code be changed such that investors can optionally use the GDP deflator to adjust their cost basis when investments are sold.   The IRS could publish official monthly GDP deflator (inflation) data going back 1914.   Investors would have the option (on a sale-by-sale basis) of adjusting their cost basis upward by the ratio of the sale-month deflator to the purchase-month deflator.

In the 10% example, if you bought 100 shares of XYZ corp 4 years ago at $80 and sold those shares at $88 your traditional cost basis would be $8000 and your sales process would be $8800, resulting in a taxable $800 gain.  In my system your basis would be adjusted for inflation, making it $8800 — resulting in $0 of gain, and $0 of tax.   You wouldn’t pay for simply keeping up with inflation.

If, during the same time period you bought 100 shares of ACME corp at $100 each and sold them for $100 you would experience a long-term capital loss, because you lost real purchasing power.  Your revised basis would be $11,000 and proceeds $10,000 — resulting in a $1000 capital loss.

A Role for Optimization — Economic Reasoning and Tax Policy Seldom Coincide

The above wishes are likely to remain unrealized for years or decades to come.  Until then, it is important to factor in the implications of short-term, long-term, and unrealized gains and losses. This complexity can be more easily handled with a multi-objective optimization algorithm.

Exploring Risk Models

As I continue to explore patterns in beta-client data, I clearly see one common difference.  For globally-diversified, and asset-diversified ETF- and mutual-fund-based portfolios 36-month, monthly modified-semivariance and variance based portfolios tend to converge to produce similar results.   This is in sharp contrast to stock-based portfolios, where variance (MVO) and semivariance (PMPT) portfolios display a significant trade-off between Sharpe and Sortino ratios.

My preliminary conclusion, based on poring through individual optimized-portfolios, is that variance and semivariance are closely correlated for portfolios based on sufficiently-diversified ETFs.  On the other hand, the difference between variance-optimized, semivariance-optimized, and hybrid (blend of variance- and covariance-optimized) portfolio is significantly different if individual stocks and bonds are analyzed.  [Sufficiently-diversified in this context does not mean diversified per se.  It only means relative diversification within a given ETF or set or ETFs/ETNs/Mutual Funds.)

These preliminary findings suggest that semivariance and variance based optimizations are highly correlated for certain asset classes (and expected returns) while differing for other asset classes (and expected returns).  Stock-pickers are more likely to see benefits from semivariance-based optimization than are those who select from relatively-diverse ETFs.

These preliminary findings are causing a shift in the approach taken by Sigma1.  Since, so far, Sigma1 beta partners are primarily interested in constructing portfolios based primarily or exclusively around ETFs, ETNs, and mutual funds, our company is focusing more on Sharpe ratios (because they are quicker to optimize for than Sortino ratios).

Because Sigma1 HAL0 portfolio-optimization is tuned to optimize for 3 objectives this presents an interesting question:  “Your investment company wishes to optimize portfolios based on 1) expected return, 2) minimal variance, and 3)  <RISK MEASURE 3>?”

Sigma1 is posing questions:  What is your third criterion?  What is your other risk measure?   Answer these questions, and Sigma1 HAL0 software will optimize your portfolio accordingly; showing the trade-offs between Sharpe ratios and your other chosen risk metric.

Sigma1’s 3-objective-optimization is causing a few financial-industry players to ask the question of established optimization engines, “Can you do that?”  Sigma1 Software can.  Can your current portfolio-optimization software do the same?
 

Beta Software, First Month

This marks the first month (30 days) of engagement with beta financial partners.  The goal is to test Sigma1 HAL0 portfolio-optimization software on real investment portfolios and get feedback from financial professionals.  The beta period is free.  Beta users provide tickers and expected-returns estimates via email, and Sigma1 provides portfolio results back with the best Sharpe, Sortino, or Sharpe/Sortino hybrid ratio results.

HAL0 portfolio-optimization software provides a set of optimized portfolios, often 40 to 100 “optimal” portfolios, optimized for expected return, return-variance and return-semivariance.   “Generic” portfolios containing a sufficiently-diverse set of ETFs produce similar-looking graphs.  A portfolio set containing SPY, VTI, BND, EFA, and BWX is sufficient to produce a prototypical graph.  The contour lines on the graph clearly show a tradeoff between semi-variance and variance.

 

Portfolio Optimization, Variance, Semi-Variance, and Total Return
Portfolio Optimization Graph

 

Once the set of optimized portfolios has been generated the user can select the “best” portfolio based on their selection criteria.

So far I have learned that many financial advisers and fund managers are aware of post-modern portfolio theory (PMPT) measures such as semivariance, but also a bit wary of them.  At the same time, some I have spoken with acknowledge that semivariance and parts of PMPT are the likely future of investing.  Portfolio managers want to be equipped for the day when one of their big investors asks, “What is the Sortino ratio of my portfolio? Can you reduce the semi-variance of my portfolio?”

I was surprised to hear that all of Sigma1 beta partners are interested exclusively in a web-based interface. This preliminary finding is encouraging because it aligns with a business model that protects Sigma1 IP from unsanctioned copying and reverse-engineering.

Another surprise has been the sizes of the asset sets supplied, ranging from 30 to 50 assets. Prior to software beta, I put significant effort into ensuring that HAL0 optimization could handle 500+ asset portfolios. My goal, which I achieved, was high-quality optimization of 500 assets in one hour and overnight deep-dive optimization (adding 8-10 basis points of additional expected-return for a given variance/semi-variance). On the portfolio assets provided to-date, deep-dive runtimes have all been under 5 minutes.

The best-testing phase has provided me with a prioritized list of software improvements. #1 is per-asset weighting limits. #2 is an easy-to-use web interface. #3 is focused optimization, such as the ability to set max variance.  There have also been company-specific requests that I will strive to implement as time permits.

Financial professionals (financial advisers, wealth managers, fund managers, proprietary trade managers, risk managers, etc.) seem inclined to want to optimize and analyze risk in both old ways (mean-return variance) and new (historic worst-year loss, VAR measures, tail risk, portfolio stress tests, semivariance, etc.).

Some Sigma1 beta partners have been hesitant to provide proprietary risk measure algorithms.  These partners prefer to use built-in Sigma1 optimizations, receive the resulting portfolios, and perform their own in-house analysis of risk.  The downside of this is that I cannot optimize directly to proprietary risk measures.  The upside is that I can further refine the HAL0 algos to solve more universal portfolio-optimization problems.  Even indirect feedback is helpful.

Portfolio and fund managers are generally happy with mean-return variance optimization, but are concerned that semivariance-return measures are reasonably likely to change the financial industry in the coming years.   Luckily the Sharpe ratio and Sortino ratio differ by only the denominator (σp versus σd) .  By normalizing the definitions of volatility (currently called modified-return variance and modified-return semivariance) HAL0 software optimizes simultaneously for both (modified) Sharpe and Sortino ratios, or any Sharpe/Sortino hybrid ratios in-between.  A variance-focused investor can use a 100% variance-optimized portfolio.  An investor wanting to dabble with semi-variance can explore portfolios with, say, a 70%/30% Sharpe/Sortino ratio.   And an investor, fairly bullish on semivariance minimization, could use a 20%/80% Sharpe/Sortino hybrid ratio.

I am very thankful to investment managers and other financial pros who are taking the time to explore the capabilities of HAL0 portfolio-optimization software.  I am hopeful that, over time, I can persuade some beta partners to become clients as HAL0 software evolves and improves.  In other cases I hope to provide Sigma1 partners with new ideas and perspectives on portfolio optimization and risk analysis.  Even in one short month, every partner has helped HAL0 software become better in a variety of ways.

Sigma1 is interested in taking on 1 or 2 additional investment professionals as beta partners.  If interested please submit a brief request for info on our contact page.

 

A Choice: Perfectly Wrong or Imperfectly Right?

In many situations good quick action beats slow brilliant action.   This is especially true when the “best” answer arrives too late.  The perfect pass is irrelevant after the QB is sacked, just as the perfect diagnosis is useless after the patient is dead.  Lets call this principle the temporal dominance threshold, or beat the buzzer.

Now imagine taking a multiple-choice test such as the SAT or GMAT.   Let’s say you got every question right, but somehow managed to skip question 7.   In the line for question #7 you put the answer to question #8, etc.   When you answer the last question, #50, you finally realize your mistake when you see one empty space left on the answer sheet… just as the proctor announces “Time’s up!”   Even thought you’ve answered every question right (except for question #7), you fail dramatically.   I’ll call this principle query displacement, or right answer/wrong question.

The first scenario is similar to the problems of high-frequency trading (HFT).  Good trades executed swiftly are much better than “great” trades executed (or not executed!) after the market has already moved.   The second scenario is somewhat analogous to the problems of asset allocation and portfolio theory.  For example, if a poor or “incomplete” set of assets is supplied to any portfolio optimizer, results will be mediocre at best.  Just one example of right answer (portfolio optimization), wrong question (how to turn lead into gold).

I propose that the degree of fluctuation, or variance (or mean-return variance) is another partially-wrong question.  Perhaps incomplete is a better term.  Either way, not quite the right question.

Particularly if your portfolio is leveraged, what matters is portfolio semivariance.  If you believe that “markets can remain irrational longer than you can remain solvent”, leverage is involved.  Leverage via margin, or leverage via derivatives matters not.  Leverage is leverage.  At market close, “basic” 4X leverage means complete liquidation at a underlying loss of only 25%.  Downside matters.

Supposing a long-only position with leverage, modified semivariance is of key importance.  Modified, in my notation, means using zero rather than μ.  For one reason, solvency does not care about μ, mean return over an interval greater than insolvency.

The question at hand is what is the best predictor of future semivariance — past variance or past semivariance?  These papers make the case for semivariance:  “Good Volatility, Bad Volatility: Signed Jumps and the Persistence of Volatility” and “Mean-Semivariance Optimization: A Heuristic Approach“.

At the extreme, semivariance is most important factor for solvency… far more important than basic variance.  In terms of client risk-tolerance, actual semi-variance is arguably more important than variance — especially when financial utility is factored in.

Now, finally, to the crux of the issue.   It is far better to predict future semivariance than to predict future variance.  If it turns out that past (modified) semivariance is more predictive of future semivariance than is past variance, then I’d favor a near-optimal optimization of expected return versus semivariance than an perfectly-optimal expected return versus variance asset allocation.

It turns out that respectively optimizing semivariance is computationally many orders of magnitude more difficult that optimizing for variance.  It also turns out that Sigma1’s HAL0 software provides a near-optimal solution to the right question: least semivariance for a given expected return.

At the end of the day, at market close, I favor near-perfect semivariance optimization over “perfect” variance optimization.  Period.  Can your software do that?  Sigma1 financial software, code-named HAL0, can.  And that is just the beginning of what it can do.  HALo answers the right questions, with near-perfect precision.  And more precisely each day.