With news like JPMorgan losing $9 billion dollars in a quarter due to trading losses, it’s no wonder that risk management software is seen as increasingly important.  I appears that the highest level executives have no clue how to assess the risks that their traders are taking on.  No clue, that is, until they are side-swiped by massive losses.

To begin to fathom the risk exposure from proprietary-trading (and hedging) it is necessary to have near-real-time data for the complete portfolio of securities, derivatives, and other financial positions and obligations.  This is a herculean, but achievable task for more vanilla securities positions such as long and short positions in stocks, bonds, ETNs, options and futures.  All of these assets have standardized tickers, trading rules, and essentially zero counterparty risk.  Further these financial assets have thorough, easily-accessible, real-time data for price, volume, bid and ask.  Even thinly traded assets like many option contracts have sufficient data to at least estimate their current liquidation value with tolerable uncertainty (say +/- 10%).

OTC trades, contracts, and obligations pose a much greater challenge for risk managers.  Lets think about credit-default swaps on Greek bonds.  Believe it or not there is uncertainty over the definition of “default”.  If European banks agreed to take a 50% haircut on Greek debt, does that constitute a default.  Most accounts I have read say no.  So even if a savvy European bank hedged its Greek bond exposure with CDS contracts, they lose.  Their hedge really wasn’t.

Sigma1 doesn’t (currently) attempt to assess risk for exotic OTC contracts and obligations.  What Sigma1 HAL0 software does do is better model standardized financial asset portfolios.  A tag line for HAL0 software could be “Risk: Better Modelling, Sounder Sleep”.

My goal is to continuously improve risk management and risk optimization in the following ways:

  1. Risk models that are more robust and intuitive.
  2. Enhanced risk visualization.  Taking the abstract and making it visible
  3. Optimizing downside risk (minimizing downside risk) with sophisticated heuristic algorithms.

I prefer the term “optimize” (in most contexts) to “minimize” or “maximize” because it is clear what optimize means.  Naturally portfolio optimization means finding the efficient frontier of minimized risk returns (or return-maximized risks).  Either way optimization usually involves concurrent minimization and maximization of various objective functions.

HAL0 portfolio optimization is best suited for optimizing the following types of funds and portfolios  1) individual investment portfolios, 2) endowment portfolios, 3) pension funds, 4) insurance company portfolios, 5) traditional (non-investment bank) bank portfolios, 6) company investment portfolios (including bond obligations).

While the core HAL0 optimization algorithm is designed to optimize more than 3 objective functions, I have been increasingly focused on optimizing for 3 concurrent objectives.   In the most common usage model, I envision one expected return function, one risk function, a third objective function.   The third objective function can be another risk model, diversification metric, investment-style metric or any other quantitative measure.

For example, HAL0 can optimize from a pool of 500 investments to create a 3D efficient frontier surface.  The z axis is, by convention, always the expect return.  The x axis is generally the primary risk measure, such as 3-year monthly semivariance.  The y axis, depth, can be another risk measure such as worst 5-year quarterly return.

Looking at this surface gives perspective on the tradeoffs between the various return and risk metrics.  It is particularly elucidating to plot a point representing one’s current investment pool or portfolio.  If it is on the surface, it is optimal (or near optimal).  However, if it is under the surface it is sub-optimal.  Either way, looking north, south, east, or west show the nearby alternatives — trading of various risks and rewards.

So the nascent marketer in me asks:  Can your financial optimization software optimize and display in 3 dimensions?  Can it optimize non-standard functions (such as worst-case quarterly return over 5 years)?  Is your current portfolio optimization software written from the ground up to be specifically optimized for financial optimization challenges?

HAL0 is.   It is the financial software that I would buy (and will personally use) to optimize my financial portfolio.  It is so compelling that it is the first project that is causing me to seriously consider quitting my day job with excellent benefits, vacation, and a six-figure salary for.   To borrow a baseball analogy software development and finance are in my wheelhouse.  I am considering giving up the comfort and security of a solid job in electrical engineering to pursue my dream and my truest talents.  Many in my industry would “kill” for my current position.  To me it feels largely intellectually unchallenging. In contrast, developing and enhancing HAL0 has taken every spare ounce of my creativity, knowledge, and passion.  In essence, HAL0 is a labor of love.

I passionately want to redefine financial risk.  I also want to modestly redefine financial return.  I see the current financial model and flawed in major and minor (yet significant) ways and hope to reinvent it.   It’s about leveraging the best of the past (Markowitz’s core ideas including semivariance) and the best of the now (fast, networked, parallel compute technology).  To accomplish this requires great software, the beta version of which, called HAL0, is residing on my Linux server.

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