The Future of Investing is Automation

A significant and growing portion of today’s individual investors have never placed a trade using a human stock broker.

Developing an Automation Mindset for Investing

In 2010, I bought the domain name Sigma1.com with the idea of creating an hedge fund that I would manage.  In order to measure and manage my investment strategies objectively, I began thinking about benchmarks and financial analysis software.  And as I ran scenarios through Excel and some light-weight analysis software I created, I began to realize that analysis, by itself was very limited.  I could only back-test one portfolio at a time, and I had to construct each portfolio’s asset weights manually.  It soon became obvious that I needed portfolio optimization software.

I learned that portfolio optimization software with the capabilities I wanted was extremely expensive. Further, I realized that even if, say, I negotiated a deal with MSCI where they provided Sigma1 Financial with their Barra Portfolio Manager for free, it would not differentiate a Sigma1 hedge fund from other hedge funds using the same software.

I was beginning to interact with several technology entrepreneurs and angel investors.  I quickly learned that legal costs and barriers to entry for a new hedge were intractable.  If Sigma1 attracted $10M in assets from accredited investors in 12 months, and charged 2 and 20, it would be a money loosing enterprise.  Cursory research revealed that critical mass for a profitable (for the hedge fund managers) hedge fund could be as high as $500M.  Luckily, I had learned about the concept of the “entrepreneurial pivot“.

The specific pivots Sigma1 used were a market segment pivot followed by a technology pivot. I realized that while the high cost of good portfolio optimization software is bad for a hedge fund startup, it was great for a financial software startup.  Suddenly, the Sigma1 Financial target market switched from accredited investors to financial professionals (investment managers, fund managers, proprietary traders, etc).  This was a key market segment pivot.

Just creating a cheaper portfolio optimizer seemed unlikely to provide sufficient incentive to displace entrenched portfolio optimizers. Sigma1 needed a technology pivot — finding a solution using a completely different technology.  Most prior portfolio optimizers use some variant of linear programming (LP) [or QP or NLP] to help find optimal portfolios. Moreover they also create an asset covariance matrix as a starting point for the optimization.

One stormy day, I realized that some algorithms I created to solve statistical electrical engineering problems in grad school could be adapted to optimize investment portfolios. The method I devised not only avoided LP, QP, or NLP methods; it also dispensed with the need for a covariance matrix.  Over then next several days I realized that by eliminating dependence on a covariance matrix, the algorithm I later named HALO, could use both traditional and alternate risk measures ranging from variance-based (eg. standard-deviation of return) to covariance-based ones (e.g. beta) to semivariance to max draw down.  By developing a vastly different technology, HALO could optimize for risks such as semivariance and Sortino ratios, or max drawdown, or even custom risk measures devised by the client.

Algorithms Everywhere

Long before Sigma1 began developing HALO, the financial industry has been increasingly reliant on digital systems and various financial algorithms. As digital communication networks and electronic stock exchanges gained trading volume, various forms of program trading began to flourish.  This includes the often maligned high-frequency trading variant of automated trading.

Concurrently, more and more trading volume has gone online.  A significant portion of today’s individual investors have never placed a trade using a human stock broker.

Automated Investment Advice, Analysis, and Trading

There are now numerous automated investment analysis tools, many of which come free with a brokerage account, while others are free or low-cost stand-alone online tools.  Examples of the former include the Fidelity’s nascent GPS (Guided Portfolio Summary) to more seasoned offerings such as Financial Engines.  Online portfolio analysis offering range from Morningstar’s Instant X-Ray, to sites like ETFreplay.

However these software offerings are just the beginning. A company call FutureAdvisor has partnered with Fidelity and TD Ameritrade to allow its automate portfolio software to make trades on its users behalf. Companies like Future Advisor have the potential to help small investors benefit from custom-tailored investment advice utilizing proven academic research (e.g. Fama French) at a very low cost — costs so low that they would not be profitable for human investment advisers to provide.

If successful (and I believe some automated investment companies will be), why should they stop at small-time investors, with less than $500,000 in investable assets?  Why not $1,000,000 or more?  Nothing should stop them!

I could easily imagine Mark Zuckerberg, Sergey Brin, or Larry Page utilizing an automated investment company’s software to manage a large part of their portfolios.  If we, as a society, are considering allowing automated systems to drive our cars for us, surely they can also manage our investment portfolios.

The Future Roll of the Human Financial Adviser

There will always be some percentage of investors who want a personal relationship with a financial adviser. Human investment advisers can excel at explaining investment concepts and putting investors at ease during market corrections.  In some ways human investment advisers even function as personal financial counselors, listening to their clients emotional financial stories.  And, of course, there are some people who want to be able to pick up the phone and yell at a real person for letting them suffer market losses.  Finally, there are people with Luddite tenancies who want as little to do with technology as possible.  For all these reasons human investment advisers will have a place in the future world of finance.

Investment Automation will Accelerate

There are some clear trends in the investing world.  Index investing will continue to grow, as will total ETF assets under management (AUM). Alternative investments from rental property to master limited partnerships (MLPs) to private equity are also likely to become part of the portfolios of more sophisticated and affluent investors.

With the exception of high-frequency trading, which has probably saturated arbitrage and front-running opportunities, I expect algorithmic (algo) management to increase as an overall percentage of US and global AUM. Some algorithmic trading and investing will be of the “hardwired” variety where the algo directly connects to the exchanges and makes trades, while the rest of the algo umbrella will comprise trading and investing decisions made by financial software and entered manually by humans with minimal revision.  There will also be hybrid methods where investment decisions are a synthesis of “automated” and “manual” processes.  I expect the scope of these “flavors” of automated investing to not only increase, but to accelerate in the near term.

It is important to note, however, that for the foreseeable future, the ultimate arbiters of algorithmic investing and portfolio optimization will be human.  The software architects and developers will exercise significant influence on the methodology behind the fund and portfolio optimization software.  Furthermore, the users of the software will have supreme control over what parameters go into the optimization process such as including or excluding or bounding certain assets and asset classes (amongst many other factors under their direct control).

That being said, the future of investing will be increasingly the domain of financial engineers, software developers and testers, and people with skills in financial mathematics, statistics, algorithms, data structures, GUIs, web interfaces and usability. Additionally, the financial software automation revolution will have profound impacts on legal professionals and marketers in the financial domain, as well as more modest impacts on accountants and IT professionals.

Some financial professionals will take the initiative and find a place on the leading edge of the financial automation revolution. It is likely to be a wild but lucrative ride. Others will seek the short-term comfort of tradition. They may be able to retain many of their current clients through sheer charisma and inertia, but may find it increasingly difficult the appeal to younger affluent clients steeped in a culture of technology.

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HALO Portfolio Software Availability

We’ve received several requests from individuals about obtaining access to HAL0 Portfolio-Optimization software. We’re very appreciative of the interest!

In the long-term Sigma1 Financial might consider a SaaS (software as a service) model for individuals. Currently, however, Sigma1 Financial is only pursuing select relationships with financial professionals.

If you are an financial professional interested in HALO software, please use this Contact Form to contact us.  Also if you are a web software developer or business manager with experience in sales to financial companies who wishes to learn more about opportunities with Sigma1 Financial, please contact us as well.

Regardless of your financial background, we welcome comments and questions from our readers.  We use a lot of math in this blog and naturally make a few mistakes on occasion.  We love to get feedback on any mathematical typos, oversights, or just plain errors; and we strive to correct them as quickly as possible.

Pursuing Alpha with Antivariance

A simple and marginally-effective strategy to reduce portfolio variance is by constructing an asset correlation matrix, selecting assets with low (preferably negative) correlations, and building a portfolio of low-correlation assets.  This basic strategy involves creating a set of assets whose cross-correlations (covariances) are minimized.

One reason this basic  strategy is only somewhat effective is that a correlation matrix (or covariance matrix) only provides a partial picture of the chosen investment landscape.  Some fundamental limitations include non-normal distributions, skewness, and kurtosis to name a few.  To most readers these are fancy words with varying degrees of meaning.

Personally, I often find the mathematics of the work I do seductive like a Siren’s song.  I endeavor to strike a balance between exploring tangential mathematical constructs, and keeping most of my math applied. One mental antidote to the Siren’s song of pure mathematics is to think more conceptually than mathematically by asking questions like:

What are the goals of portfolio optimization?  What elements of the investing landscape allow these goals to be achieved?

I then attempt to answer these questions with explanations that a person with a college degree but without a mathematically background beyond algebra could understand.  This approach lets me define the concept first, and develop the math later.  In essence I can temporarily free my mind of the slow, system 2 thinking generally required for math.

Recently, I came up with the concept of antivariance.  I’m sure others have had similar ideas and a cursory web search reveals that as profession poker player’s nickname.  I will layout my concept of antivariance as it relates to porfolio theory in particular and the broader concept in general.

By convention, one of the key objective of modern portfolio theory is the reduction of portfolio return variance.  The mathematical concept is the idea that by combining assets with correlations of less than 1.0, the return variance is less than the weighted sums of each asset’s individual variance.

Antivariance assumes that there are underlying patterns explain why two or more assets should be somewhat less correlated (independent), but at times negatively correlated.  Consider the affects of major hurricanes like Andrew or Katrina.  Their effects were negative for insurance companies with large exposures, but were arguably positive for companies that manufactured and supplied building materials used in the subsequent rebuilds.  I mention Andrew because there was much more and more rapid rebuilding following Andrew than Katrina.  The disparate groups of stocks of (regional) insurance versus construction companies can be considered to exhibit paired antivariance to devastating weather events.

Nicholas Nassim Taleb coined the the term antifragile, because terms such as robust simply don’t convey the exact mental connections.  I am beginning to use the term antivariance because it conveys concepts not well captured by terms like “negatively correlated”, “less correlated”, “semi-independent”, etc.   In many respects antifragile systems should exhibit antivariance characteristics, and vice versa.

The concept of antivariance can be extended to related concepts such as anticovariance and anticorrelation.

 

 

Software Development Choices for Portfolio Optimization

The first phase of developing the HALO (Heuristic Algorithm Optimizer) Portfolio Optimizer was testing mathematical and heuristic concepts.  The second phase was teaming up with beta partners in the financial industry to exchange optimization work for feedback on the optimizer features and results.

For the first phase, my primary tool for software development was the Ruby language.  Because Ruby is a “high-level” extensible language I was able to quickly prototype and test many diverse and complex concepts.  This software development process is sometimes referred to as software prototyping.

For the second, beta phase of software development I kept most of the software in Ruby, but began re-implementing selected portions of the code in C/C++. The goal was to keep the high-change-rate code in Ruby, while coding the more stable portions in C/C++ for run-time improvement.  While a good idea in theory, it turned out that my ability to foresee beta-partner changes was mixed at best.  While many changes hit the the Ruby code, and were easily implemented, a significant fraction hit deep into the C/C++ code, requiring significant development and debugging effort.  In some cases, the C/C++ effort was so high, I switched back portions of the code to Ruby for rapid development and ease of debugging.

Now that the limited-beta period is nearly complete, software development has entered a third phase: run-time-performance optimization.  This process involves converting the vast majority of Ruby code to C.  Notice, I specifically say C, not C/C++.   In phase 2, I was surprised at the vast increase in executable code size with C++ (and STL and Boost).  As an experiment I pruned test sections of code down to pure C and saw the binary (and in-memory) machine code size decrease by 10X and more.

By carefully coding in pure C, smaller binaries were produced, allowing more of the key code to reside in the L1 and L2 caches.  Moreover, because C allows very precise control over memory allocation, reallocation, and de-allocation, I was able to more-or-less ensure than key data resided primarily in the L1 and/or L2 caches as well.  When both data and instructions live close to the CPU in cache memory, performance skyrockets.

HALO code is very modular, meaning that it is carefully partitioned into independent functional pieces.  It is very difficult, and not worth the effort, to convert part of a module from Ruby to C — it is more of an all-or-nothing process.  So when I finished converting another entire module to C today, I was eager to see the result.  I was blown away.  The speed-up was 188X.  That’s right, almost 200 times faster.

A purely C implementation has its advantages.  C is extremely close to the hardware without being tied directly to any particular hardware implementation.   This enables C code (with the help of a good compiler) to benefit from specific hardware advantages on any particular platform.  Pure C code, if written carefully, is also very portable — meaning it can be ported to a variety of different OS and hardware platforms with relative ease.

A pure C implementation has disadvantages.  Some include susceptibility to pointer errors, buffer-overflow errors, and memory leaks as a few examples.  Many of these drawbacks can be mitigated by software regression testing, particularly to a “golden” reference spec coded in a different software language.  In the case of HALO Portfolio-Optimization Software, the golden reference spec is the Ruby implementation.  Furthermore unit testing can be combined with regression testing to provide even better software test coverage and “bug” isolation.  The latest 188X speedup was tested against a Ruby unit test regression suite and proven to be identical (within five or more significant digits of precision) to the Ruby implementation.  Since the Ruby and C implementations were coded months apart, in different software languages, it is very unlikely that the same software “bug” was independently implemented in each.  Thus the C helps validate the “golden” Ruby spec, and vice versa.

I have written before about how faster software is greener software.  At the time HALO was primarily a Ruby implementation, and I expected about a 10X speed up for converting from Ruby to C/C++.  Now I am increasingly confident that an overall 100X speedup for an all C implementation is quite achievable.  For the SaaS (software as a service) implementation, I plan to continue to use Ruby (and possibly some PHP and/or Python) for the web-interface code.  However, I am hopeful I can create a pure C implementation of the entire number-crunch software stack.  The current plan is to use the right tool for the right job:  C for pure speed, Ruby for prototyping and as a golden regression reference, and Ruby/PHP/Python/etc for their web-integration capabilities.

 

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.

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.

 

 

The Technology Behind the Tech

I started developing HALO portfolio-optimization software just shy of 1.5 years ago.  I owned a Linux workstation with a simple RAID-1 file system running Debian Linux, and I was on a 8-week sabbatical from work.  I started development using Ruby 1.8.7.  Later I switched to Ruby 1.9.1, then 1.9.3.  I have chosen, for now, to remain “frozen” on the 1.9.3 Ruby implementation.

I chose Ruby as my primary prototyping language because it allows rapid development, intuitive and highly-readable code, and easy of debugging.  Moreover, the Ruby HALO code is highly portable: The code developed Linux ran identically on Window with little to no modification.  After the initial development push that produced 3-D optimization graphs, I began developing on Windows under Cygwin.  I did this for three reasons.  1)  I wanted to run and test on Windows, 2) I had a Windows laptop and wanted to be able to develop anywhere: at the park, at the coffee shop, on an airplane, etc.  3)  I wanted to bring and demo my software to potential investors and potential clients.  I periodically sync the code between my Windows laptop and my Linux desktop.

Eventually, I began to wonder how much run time performance I was giving up by using Ruby instead of C/C++.  I profiled the Ruby code and began re-implemented the most compute-time-intensive parts of the code in C/C++.  Finding C++ a bit lacking by itself, I began using C++11 with Boost.  I am currently using Boost 1.53.0.

I was expecting perhaps a 10X speed up, but was happily surprised that I saw speed ups of between 20X and 50X when converting from Ruby to C++.  The C/C++ implementation is not a line-by-line parroting of the Ruby code, but the algorithms remained the same.  Coding in C/C++ caused me to focus more on memory allocation and, yes, garbage collection, so I attribute part of the massive speed up to that.

(I also tried JRuby — compiling the Ruby code to Java bytecode and running on the JVM.  I did see a speedup, but only about 15%.)

To date I have converted less than half of the Ruby to C++.  Currently the complete HAL0 Suite is 70% Ruby, 26% C++, and 4% other (Perl, Shell, etc).  By converting the most critical algorithms to C++, overall run time has improved approximately 3.5X.  Less than 10% of the end-to-end run time is consumed by the C++ portion.  That leads me to believe that an additional 10X performance increase is likely by the time I convert to a 100% C++ implementation.  [All of my run-time benchmark data are based on runs on an Intel(R) Core(TM) i5-2467M CPU @ 1.60GHz.]

My current C++ compiler is gcc 4.5.3.  All the C++ benchmarks above were performed with no compiler optimization (-O0).  Simply switching to “-O2” optimization resulted in a 15.6% performance boost.  I plan on experimenting with other flags starting with some suggestions from this GCC Intel x86 performance webpage. I will admit I have a bias toward Intel hardware.  That being said, it likely be a long time before I choose to plunk down $699 x 2 of Sigma1 Financial’s capital to buy even the basic Intel C++ compilers for Windows and Linux.

I ordered an Intel Core i7-4770K (desktop) system for Sigma1 Financial within a day of the chip’s official release.  It is scheduled to arrive next week.  Naturally, I am excited to see how much performance gain results from jumping two CPU generations and over 2X in core frequency.

 

 

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.

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.