I Robo: The Rise of the Robo Advisor

Think Ahead About Your Role in a Robo Advisory World

Financial innovation is here and it is here to stay.  Financial advisors, broker/dealers, hybrids, and even financial planners should be thinking about how to adapt to inevitable changes launched by disruptive investing technologies.

Robo Design — Chip designers have been using it for decades

I have an unique perspective on technological disruption.  For over ten years, my job was to develop software to make microchip designers more productive. Another way of describing my work was to replace microchip design tasks done by humans with software. In essence, my job was to put some chip designers out of work. My role was called (digital circuit) design automation, or DA.

In reality my work and the work of software design automation engineers like myself resulted in making designers faster and more productive — able to develop larger chips with roughly the same number of design engineers.

Robo Advisors: Infancy now, but growing very fast!

“The robos are coming, the robos are coming!” It’s true. Data though the end of 2014 shows that robo advisors managed $19 billion in assets with a 65% growth rate in just eight short months. This is essentially triple-digit growth, annual doubling.  $19 billion (likely $30 billion now), is just a drop in the bucket now… but with firms like Vanguard and Schwab already developing and rolling out robo advising option of their own these crazy growth rates are sustainable for a while.

With total US assets under management (AUM) exceeding $34 trillion, an estimated $30 billion for robo advisors represents less than 0.1% of managed assets.  If, however, robo advisors grow double their managed assets annually for the next five years that amounts to about 3% of total AUM management by robo advisors. If in the second five years the robo advisory annual grow rate slows to 50% that still mean that robo advisors will control in the neighborhood of 20% of managed assets by 2025.

“Robo-Shields” and Robo Friends

Deborah Fox was clever enough to coin and trademark the term “robo-shield.” The basic idea is for traditional (human) investment advisors to protect their business by offering robo-like services ranging from client access to their online data to tax harvesting. I call this the half-robo defense

Another route to explore is the “robo friends”, or “full robo-hybrid” approach. This is partnering with an internal or external robo advisor.  As an investment advisor, the robo advisor is subservient to you, and provides portfolio allocation and tax-loss harvesting, while you focus on the client relationship.  I believe that the “robo friends” model will win over the pure robo advising model — most people prefer to have someone to call when they have investment questions or concerns, and they like to have relationships with their human advisors. We shall see.

What matters most is staying abreast of the robo advisor revolution and having a plan for finding a place in the brave new world of robo advising.

 

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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.

 

Show Me 3D

I was having a dinner conversation and showing some Sigma1 Financial images from this site on an Android phone to a UI developer.  I kept looking for a 3-D perspective plot, and soon realized that I hadn’t posted one yet!  How easily remedied:

 

This plot shows the objective space and the trade offs between return, variance, and semivariance. It contains the same information present in other example plots, but presented from a different perspective.

3-D Frontier of Optimized Investment Portfolios

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.