Principles of Portfolio Optimization Software

Explaining technical investment concepts in a non-technical way is critical to having a meaningful dialog with individual investors.  Most individual investors (also called “retail investors”, or “small investors”) do not have the time nor the desire to learn the jargon and concepts behind building a solid investment portfolio.  This is generally true for most individual investors regardless of the size of their investment portfolios.  Individual investors expect investment professionals (also called “institutional investors”) to help manage their portfolios and explain the major investment decisions behind the management of their individual portfolios.

In the same way that a good doctor helps her patient make informed medical decisions, a good investment adviser helps her clients make informed investment decisions.

I get routinely asked how the HALO Portfolio Optimizer works.  Every time I answer that question, I face two risks: 1) that I don’t provide enough information to convince the investment profession or their clients that HALO optimization provides significant value and risk-mitigation capability and 2) I risk sharing key intellectual property (IP) unique to the Sigma1 Financial HALO optimizer.

This post is my best effort to provide both investment advisers and their clients with enough information to evaluate and understand HALO optimization, while avoiding sharing key Sigma1 trade secrets and intelectual property.  I would very much appreciate feedback, both positive and negative, as to whether I have achieved these goals.

First Principle of Portfolio Optimization Software

Once when J.P. Morgan was asked what the market would do, he answered “It will fluctuate.”  While some might find this answer rather flippant, I find it extremely insightful.  It turns out that so-called modern portfolio theory (MPT) is based understanding (or quantifying) market fluctuations. MPT labels these fluctuations as “risk” and identifies “return” as the reward that a rational investor is willing to accept for a given amount of risk.  MPT assumes that a rational investor, or his/her investment adviser will diversify away most or all “diversifiable risk” by creating a suitable investment portfolio tailored to the investor’s current “risk tolerance.”

In other words, the primary job of the investment adviser (in a “fiduciary” role), is to maximize investment portfolio return for a client’s acceptable risk.  Said yet another way, the job is to maximize the risk/reward ratio for the client, without incurring excess risk.

Now for the first principle: past asset “risk” tends to indicate future asset “risk”.  In general an asset that has been previously more volatile will tend to remain more volatile, and and asset that has been less volatile will tend to remain less volatile.  Commonly, both academia and professional investors have equated volatility with risk.

Second Principle of Portfolio Optimization Software

The Second Principle is closely related to the first.  The idea is that the past portfolio volatility tends to indicate future portfolio volatility. This thesis is so prevalent that it is almost inherently assumed.  This is evidenced by search results that reaches beyond volatility and looks at the hysteresis of return-versus-volatility ratios, papers such at this.

Past Performance is Not Necessarily Indicative of Future Results.

Third Principle of Portfolio Optimization Software

The benefits of diversification are manifest in risk mitigation.  If two assets are imperfectly correlated, then their combined volatility (risk) will be less than the weighted averages of their individual volatilities.  An in-depth mathematical description two-asset portfolio volatilities can be found on William Sharpe’s web page.  Two-asset mean-variance optimization is relatively simple, and can be performed with relatively few floating-point operations on a computer.  This process creates the two-asset efficient frontier*.  As more assets are added to the mix, the computational demand to find the optimal efficient frontier grows geometrically, if you don’t immediately see why look at page 8 of this paper.

A much simpler explanation of the the third principle is as follows.  If asset A has annual standard deviation of 10%, and asset B an annual standard deviation of 20%, and A and B are not perfectly correlated, then the portfolio of one half invested in A and the other half invested in B will have a annual standard deviation of less than 15%.  (Non-perfectly correlated means a correlation of less than 1.0).  Some example correlations of assets can be found here.

In so-called plain English, the Third Principle of Portfolio Optimization can be stated: “For a given level of expected return, portfolio optimization software can reduce portfolio risk by utilizing the fact that different assets move somewhat independently from each other.”

Forth Principle of Portfolio Optimization Software

The Forth Principle of Portfolio Optimization establishes a relationship between risk and return.  The classic assumption of modern portfolio theory (MPT) is that so-called systematic risk is rewarded (over a long-enough time horizon) with increased returns.  Portfolio-optimization software seeks to reduce or eliminate unsystematic risk when creating an optimized set of portfolios.  The portfolio manager can thus select one of these optimized portfolios from the “best-in-breed” list created by the optimization software that is best suited to his/her client’s needs.

Fifth Principle of Portfolio Optimization Software

The 5th Principle is that the portfolio manager and his team adds value to the portfolio composition process by 1) selecting a robust mix of assets, 2) applying constraints to the weights of said assets and asset-groups, and 3) assigning expected returns to each asset.  The 5th Principle focuses on the assignment of expected returns.  This  process can be grouped under the category of investment analysis or investment research.  Investment firms pay good money for either in-house or contracted investment analysis of selected securities.

Applying the Portfolio Optimization Principles Together

Sigma1 Financial HALO Software applies these five principles together to help portfolio managers improve or fine-tune their proprietary-trading and/or client investment portfolios.  HALO Portfolio Optimization software utilizes the assets, constraints, and expected returns from the 5th Principal as a starting point.  It then uses the 4th Principal by optimizing away systematic risk from a set of portfolios by taking maximum advantage of varying degrees of non-correlation of the portfolio assets.  The 3rd Principle alludes to the computational difficulty of solving the multi-asset optimization problem.  Principles 1 and 2 form the bedrock of the concepts behind the use of historical correlation data to predict and estimate future correlations.

The Fine Print

Past asset volatility of most assets and most portfolios is historically well correlated with future volatility. However, not only are assets increasingly correlated, there is some evidence that asset correlations tend to increase during times of financial crisis. Even if assets are more correlated, there remains significant value in exploiting partial-discorrelation.
(*) The two-asset model can be represented as two parametric functions of a single variable, “t”, ER(t), and var(t).  t simply represents the investment proportion invested in asset 0 (aka asset A).  For three variables, expected return becomes ER(t0,t1) as does var(t0,t1).  And so on for increasing numbers of assets.  The computational effort required to compute ER(t0…tn) scales linearly with number of assets, but var(t0…tn) scales geometrically.
Optimizing efficiently within this complex space benefits from creative algorithms and heuristics.

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Performance

The basic heuristics and algorithms I envisioned a year and a half ago have stood up well to testing, both internal testing and using external beta tester and client data.  Lessons learned have largely been associated with learning what is important to institutional investors.  Some of those lessons:

1. To date, every client has avoided long-short portfolios.  All of my initial clients have asked for strictly long-only portfolio optimization. Based on their directions, I have temporarily incorporated a “zero-floor” for all securities, which modestly speeds up the optimization process.  (Note: this constraint can easily be reversed.)
2. The initial portfolio-optimization code ran open-loop; that is to say that any asset could be assigned a weighting between 0 and 100%.  Generally, extreme asset-weightings were mathematically avoided for the majority of the “optimization surface.”   However, all Sigma1 clients have requested individual asset minimum and maximum asset-weighting constraints.  While these constraints somewhat reduce the enormous search space, in practice, they tend to slow down the heuristic algorithms.  Much of my optimization effort has been focused on efficiently enabling individual asset range constraints.
3. The third major lesson was that some clients want layered asset-class constraints.  This capability has been incorporated into the base code.

The primary thesis behind HALO Portfolio Optimization is that compute technology and algorithms have sufficiently progressed to optimize portfolios beyond simple mean-variance optimization (MVO).  Moreover, creating a set of three(an efficient surface) of portfolios optimized for multiple objectives (three: risk1, risk2, and expected return) is performed, rather than a simpler 2-D optimization.

Much more run-time optimization is on the Sigma1 road map.  The primary speed up is via conversion of increasing conversion of key parts of the HALO Ruby code to C/C++.  In the meantime, upgrading to arguably the fastest processor on the planet, the Intel i7-4700K, has shown a 2.95X speed up over benchmarks running the i5-2647M CPU running Ruby code that is currently the HALO Portfolio Optimization run-time bottleneck.  The primary operations are (billions of) double-precision floating-point arithmetic computations.

The HALO portfolio-optimization algorithms/heuristics have already “fast enough” for every single institutional investor we have worked with to date.  That does not measurably dampen my personal desire to push optimization speed to its limit.  I intend to crush previous performance benchmarks, again and again.

Does a hard-working professional investment advisory team need optimization to faster than 30 minutes?  I’d argue “No.”  But do they want faster, of course “Yes!”  If the crunch time is reduced to 5 minutes — the same logic applies — they want faster.  I understand.

It could easily be argued that it would be better to apply my efforts to developing the web UI.  I am, in parallel, at my own pace.  Currently, however, my passion is speed.  Having achieved some speed, I crave more.  When I follow my passion, my productivity is dramatically improved.  Moreover, the skill set I am trying to master has intrinsic value beyond the field of portfolio optimization.  Fun and profit, in a start-up, is often more important than maximum profit (or maximum revenue).

The HAL0 algorithms and heuristics are intrinsically fast and scalable.  Since I am not planning on sharing their inner architecture (except for millions of dollars), the proof of their power is measured in raw performance.  If that effort results in temporary loss of revenue for enhanced future revenue, then so be it.

 

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Using HALO Portfolio Optimization Software

Setting up a basic HALO optimization requires a list of asset tickers, their min and max constraints, and expected returns.  Also at least one user specified category designation is required. Below is a short example:

SPY 7% 40% 9.11% Equities
PBP 0% 15% 8.53% Equities
USMV 5% 15% 9.05% Equities
VTI 5% 25% 9.35% Equities
IJH 5% 20% 9.55% Equities
VB 3% 15% 9.81% Equities
VEA 5% 12% 8.69% International
VEU 5% 20% 9.21% International
EEM 1% 11% 10.07% International
JNK 3% 15% 6.03% Bonds
BKLN 0% 8% 3.61% Bonds
AGG 1% 9% 2.32% Bonds

Generally, it is advisable to keep the sum of the individual asset minimums below 50%, and the sum of maximums above 200%. This provides the HALO optimizer the freedom to create a wide range of optimized portfolios with different risk/reward trade offs.

The above example is a very basic configuration. In order for asset managers to specify asset-class constraints, it is necessary to tell the optimizer that the “string” is a user-defined category.  Currently this is done with a leading gastritis (*):

*Equities       25% 85%
*International  10% 30%
*Bonds          15% 45%

The above config specifies that Equities must comprise a minimum of 25% of the investment portfolio and a maximum of 85%.  As with the individual asset constraints, it is advised to provide reasonably wide latitude to the optimization algorithms to produce a diverse set of optimized portfolios.

By default, the HALO Optimizer will produce a set of portfolios optimized to:

1) minimize:
a) semi-variance, σ_d (the default)
b) –OR– annualized standard deviation of total return, σ

2) maximize expected return, E(R)

The default time series used for computing σ and σ_d is end-of-month total-return deltas for the previous 36 months.  (This requires 37 months of total-return data for each security.)  The time period can be customized to use, say 60 months worth of data in the analysis.  HALO also supports using weekly closing data or even daily closing data — however I generally recommend using monthly data for a variety of reasons.  First, it speeds the computation.  Second, monthly data captures multi-day and multi-week trends, correlations, and specifically low-correlation asset optimization.  Third, monthly data is closer to the sampling period of a “typical” high-net-worth retail investor.  [That said, a case could be made for using quarterly data — which is also supported.]

Frequently HALO clients want to model newer securities that do not have 37 months of historical data.  For example, min-volatility ETFs such as SPLV, USMV, and EEMV are popular ETFs that are less than 3 years old. The HALO software suite has utilities that can statistically back fill the missing data.  The configuration of the statistical back-fill process is beyond the scope of this blog post, however it is an important and popular HALO Optimization Suite capability that so far has been used by all of Sigma1’s clients and beta testers.

Occasionally, Sigma1 clients and beta testers have had in-house funds that do not externally report their price or total return data.  For in-house funds, HALO can read client-supplied total-return data.  Naturally, HALO can include stocks, bonds, commodities, futures, and other assets with historical data into the portfolio optimization mix.

 

 

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.

 

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.

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.

 

 

Engineering Profit versus Theoretical Profit

Either there is a veil of silence covering the world of finance, or the obvious parallels between electrical engineering (EE) have been overlooked.   I suspect the former.

Almost every EE worth their salt has been exposed to the concepts of signals and signal processing in undergrad.  From signal-to-noise ratios (SNR) to filters (dB/decade) to digital signal processors (DSPs), EE’s are trained to be experts at receiving the signal in spite of the noise.  More technobabble (but its not!) are the Fourier and Laplace transforms we routinely use to analyze the propagation of signals through circuits.  Not to mention wave-guides, complex-conjugate reflections, amplitude- and frequency- modulation, etc.   Then there are the concepts of signal error detection, error correction, and information content.

My point is that financial firms made a mistake in hiring more physicists than electrical engineers.  At the end of the day (or the project) the work of the EE has to stand up to more than just academic scrutiny; it has to stand up to the real world — real products, real testing, real use.

EE’s with years of experience have been there and done that.  Mind you, most are not interested in finance.  However, a handful of us are deeply interested in finance and investing.

These thoughts occurred to me as I was listening to speakers I built 15 years ago.  They still sound spectacular (unglaublich gut, for you Germans).  They are now my second-tier speakers relegated to computer audio.  Naturally, I have an amp fed by Toslink 48K/s 20-bit per channel audio data. My point is that these speakers have audio imaging that is achieved by a smooth first-order crossover with tweaters/speakers chosen to support phase-accurate performance over a the frequencies that the human ear can best make use of audio imaging.

My second point is that a lot of engineering went into these speakers.   This engineering goes beyond electrical.   Speakers are fundamentally in the grey region between mechanical and electrical engineering.  However the mechanical parameters can be “mapped” into the “domain” of electrical engineering concepts.  This positions EEs to pick the best designs and combine them in most advantageous designs  on a maximum value- per-dollar basis.

This post is targeting a different audience than most.  Apologies.  An EE with a CS (computer science) background is an even better choice..

The analysis of financial data as concurrent, superimposed discrete waveforms is natural to EEs as air is to mammals and water is is to fish.  Audio is, perhaps, the simplest application.   Just Google “Nyquist-Shannon” if you want to know of which I speak.

I’m not for hire — I only do contract work.  I’m just telling hiring managers to both broaden and restrict their search criteria.  A well-qualified EE with financial expertise and a passion for finance is likely to be a a better candidate than a Ph.D. in Physics.  Don’t hire Sheldon Cooper until you evaluate Howard Wolowitz (not an EE, but you get my point, I hope).

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?
 

Not your typical portfolio-optimization post

I am presently in Placencia Belize.  I love to learn about how the economy and culture of a country and how  regions within a country contribute to its place in the world.

My high-school education in history went deeper than most.  I studied AP history and my score exempted me from university history requirements.  The approach to history was, in part, a story of wars, victories, and defeats.  Differentials in leadership, technology, and manpower were used to explain, ex post, how wars were won and lost.

I  have learned that these are only part of the story.  The parallel, kindred theme is economic.  Superior economies provide great advantage.  This advantage is reflected in both income and the interest rate paid on bonds.  If a side loses the confidence of the bond market, the outcome of the conflict becomes largely predetermined.

I have a working theory about how people and governments respond to events.  At the core  of my working theory is positive feedback.  Despite the name “positive” feedback is not necessarily positive in the emotional sense.  “Positive” feedback simply means self-reinforcing.  Virtuous cycles and vicious cycles are both examples of self-reinforcing positive feedback.

Strangely, mindset is crucial.  There are two mindsets that favor positive outcomes: 1) economic self-confidence, 2)  patient self-development and willingness to defer economic self-gratification (spending).  The United States seems to be losing both of these factors.  The key word is “self”, as in individual.

US growth, anemic  as it is, is based largely on government leverage.  As long as this leverage can be financed at historically-low rates, this largely superficial growth can occur… until real, organic growth resumes, or the debt bubble bursts or begins deflating (signaled by erosion of the value of the USD).

Simply put, I have become bearish on the USD and am looking for safer harbors for my “non-risk” capital.  Alternatives include CAD, AUD, and NOK (kr) government bonds.  TIPs provide a degree of protection, if you believe CPI-U stats accurately reflect US inflation.  CASH in USD is suspect.

Back to Belize.  Belizean currency is pegged to the USD on 2:1 basis.  Belize’s unemployment rate is approximately 14.4%, and the trend is upward.  With the shear beauty and wonder of the country, reasonable English-proficiency, and a tiny 360,000 population, tourism should translate more effectively to income/capita, GDP/capita, and overall economic growth.

I am studying dissonant economic stagnation first hand.  Beauty surrounded by poverty.  Economic stagnation.  Hope concentrated outward on government, on divine intervention, on anything but self.

I study with objectivity laced with sadness.   These people could be much more, but they will likely not be any time soon.

This is a lesson for the US.  The US government’s fiscal situation is unsustainable.  The general outlook is looking outward for hope rather than inward (to oneself) for strength and betterment.   Until this attitude changes I see anemic US growth as the future.  This is likely to manifest in US inflation and USD devaluation.  Federal Reserve policy seems to concur.

My macro outlook can be reflected in the numbers I put into expected-return data for global market ETFs.  Sigma1 HAL0 software uses these projections and variance and semivariance optimization (or other metrics)  to help me revise my personal portfolio.

Feel free to disagree with my outlook.  Sigma1 HAL0 optimization will incorporate your projections, and build an optimized portfolio based upon them.  HAL0  portfolio optimization is algorithmic and objective.  Individual security expected-returns are typically based on user inputs.  HAL0 software optimizes within the provided framework.  User predictions matter, as does human discretion in using the HAL0 results.

Because my projections for expected-return may vary widely from yours, your company’s, and your analysts, the resulting portfolios will vary widely.

The bottom line is that Sigma1 portfolio-optimization software uses your hard-won security analysis and projections to build an accordingly-optimized investment portfolio. If you believe in your analysis, so will Sigma1.  Keep that in mind.  Your analysis matters.