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.
In this post I explain how less is more when it comes to using “big data.” The best data is concise, meaningful, and actionable. It is both an art and a science to turn large, complex data sets into meaningful, useful information. Just like the later paintings of Monet capture the impression of beauty more effectively than a mere photograph, “small data” can help make sense of “big data.”
There is beauty in simplicity, but capturing simplicity is not simple. A young child’s drawings are simple too, but they very unlikely to capture light and mood like Monet did.
Worry not. There will be finance and math, but I will save the math for last, in an attempt to retain the interest of non “mathy” readers.
The point of discussing impressionist painting is show that reduction — taking things away — can be a powerful tool. In fact, filtering out “noise” is both useful and difficult. A great artist can filter out the noise without losing the fidelity of the signal. In this case, the “signal” is emotion and color and light as as perceived by a master painter’s mind.
Applying Impressionism to Finance
Massive amounts of data are available to the financial professional. Two questions I have been asking at Sigma1 since the beginning are 1) How to use “Big Compute” to crunch that data into better portfolios? 2) How to represent that data to humans — both investment pros and lay folk whose money is being invested? After considerable thought, brainstorming, listening, and learning, I think we are beginning to construct a preliminary picture of how to do that — literally.
While not a beautiful as a Monet painting, the picture above is worth a thousand words (and likely many thousands of dollars over time) to me. The assets above constitute all of the current non-CASH building blocks of my personal retirement portfolio. While simple, the above image took considerable software development effort and literally millions of computations to generate [millions is very do-able with computers].
This simple-looking image conveys complex information in an easy-to-understand form. The four colors — red, green, blue, and purple — convey four asset types: fixed income, US stocks, international stocks, and convertible securities. The angle between any two asset lines conveys the relative correlation between the pair. In portfolio construction larger angles are better. Finally the length of the line represents the “effectiveness” with which each asset represents its “angular position” within the portfolio (in addition to other information).
With Powerful Data, First Comes Humility, Next Comes Insight
I have applied the same visualizations to other portfolios, and I see that, according to my software, many of the assets in professionally-managed portfolios exhibit superior “robustness” to my own. As someone who prides myself in having a kick-ass portfolio, this information is humbling, and took some time to absorb from an ego standpoint. But, having gotten over it, I now see potential.
I have seen portfolios that have a significantly wider angle than my current portfolio. What does this mean to me? It means I will begin looking for assets to augment my personal portfolio. Before I do that let me share some other insights. The plot combines covariance matrix data for the 16 assets in the portfolio, as well as semi-variance data for each asset. Without getting to “mathy” yet, the data visualization software reduces 136 pieces of data down to 32 (excluding color). The covariance matrix and semi-variance calculation itself are also a reducers in that they combines 5 years monthly total-return data — 976 data points down to 120 unique covariance numbers and 16 semi-deviation numbers. Taking 976 down to 32 results in a compression ratio of 30.5:1.
Finally, as it currently stands, the visualization software and resulting plot say nothing about expected return. The plot focuses solely on risk mitigation at the moment. Naturally, I intend to change that.
Time for the Math and Finance — Consider Yourself Warned
I mentioned a 30.5:2 (71:2) compression ratio. Just as music and other data, other information, including financial information can be compressed. However, only so much compression can be achieved in lossless manner. In audio compression researchers have learned which portions of music and other audio can be “lost” without the listener telling the difference. There is a field of psychoacoustics around doing just that — modeling what the human ear (and brain) can hear, and what gets “masked” by various physiological factors.
Even more important that preserving fidelity is extracting meaning. One way of achieving that is by removing “noise.” The visualization software performs significant computation to maintain as much angular fidelity as possible. As it optimizes angles, it keeps track of total error vis-a-vis the covariance matrix. It also keeps track of individual assets error (the reciprocal of fitness — fit versus lack of fit).
The real alchemy comes from the line-length computation. It combines semi-variance data with various fitness factors to determine each asset line length.
Just like Mercator projections for maps incur unavoidable error when converting from a 3-D globe to a 2-D map, the portfolio asset visualizations introduce error as well. If one thinks of just the correlation matrix and semi-variance data, each asset has a dimensionality of 8.5 (in the case of 16 assets). Reducing from 8.5-D to 2-D is a complex process, and there are an infinite number of ways to perform such an operation! The art and [data] science is to enhance the “signal” while stripping away the “noise.”
The ultimate goals of portfolio data visualization technology are:
1) Transform raw data into actionable insight
2) Preserve sufficient fidelity of relevant data such that the “map” can be used to reliably get to the desired “destination”
I believe that the first goal has been achieved. I know what actions to take… trying various other securities to find those that can build a “higher-angle”, and arguably more robust, more resilient investment portfolio.
However, the jury is still out on the degree [no pun intended] to which goal #2 has or has not been achieved. Does this simple 2-D map help portfolio builders reliably and consistently navigate the 8+ dimensional portfolio space?
What about 3-D Modelling and Visualization?
I started working with 2-D for one key reason — I can easily share 2-D images with readers and clients alike. I want feedback on what people like and dislike about the visuals. What is easy to understand, what is not? What is useful to them, and what isn’t? Ironing out those details in 2-D is step 1.
Of course I am excited by 3-D. Most of the building blocks are in my head, and I can heavily leverage the 2-D algorithms. I am, however, holding off for now. I am waiting for feedback from readers and clients alike. I spend a lot of time immersed in the language of math, statistics, and finance. This can create a communication gap that is best mitigated through discussion with other people with other perspectives. I wish to focus on 2-D for a while to learn more about market needs.
That being said, it is hard to resist creating a 3-D portfolio asset visualizer. The geek in me is extremely curious about how much the error terms will reduce when given a third degree of freedom to work with.
The bottom line is: Please give me any feedback: positive, negative, technical, aesthetic, etc. This is just the start. I am extremely enthusiastic about where this journey will take me and my company.
Disclosure and Disclaimer
Securities mentioned in this post are holdings in my personal retirement accounts (e.g. 401K, IRA, Roth IRA) as of the day of initial publication of this post. The purpose of this post is to illustrate features of Sigma1 Financial software. This is NOT investment advice, and NOT a recommendation to buy, sell, or hold any securities. Please refer to the “Disclaimer” Tab of the main page of this site for further information.
The best models are not the models that fit past data the best, they are the models that predict new data the best. This seems obvious, but a surprising number of business and financial decisions are based on best-fit of past data, with no idea of how well they are expected to correctly model future data.
Instant Profit, or Too Good to be True?
For instance, a stock analyst reports to you that they have a secret recipe to make 70% annualized returns by simply trading KO (The Coca-Cola Company). The analyst’s model tells what FOK limit price, y, to buy KO stock at each market open. The stock is then always sold with a market order at the end of each trading day.
The analyst tells you that her model is based on three years of trading data for KO, PEP, the S&P 500 index, aluminum and corn spot prices. Specifically, the analyst’s model uses closing data for the two preceding days, thus the model has 10 inputs. Back testing of the model shows that it would have produced 70% annualized returns over the past three years, or a whooping 391% total return over that time period. Moreover, the analyst points out that over 756 trading days 217 trades would have been executed, resulting in profit a 73% of the time (that the stock is bought).
The analyst, Debra, says that the trading algorithm is already coded, and U.S. markets open in 20 minutes. Instant profit is only moments away with a simple “yes.” What do you do with this information?
Choices, Chances, Risks and Rewards
You know this analyst and she has made your firm’s clients and proprietary trading desks a lot of money. However you also know that, while she is thorough and meticulous; she is also bold and aggressive. You decide that caution is called for, and allocate a modest $500,000 to the KO trading experiment. If after three months, the KO experiment nets at least 7% profit, you’ll raise the risk pool to $2,000,000. If, after another three months, the KO-experiment generates at least 7% again; you’ll raise the risk pool to $10,000,000 as well as letting your firms best clients in on the action.
Three months pass, and the KO-experiment produces good results: 17 trades, 13 winners, and a 10.3% net profit. You OK raising the risk pool to $2,000,000. After only 2 months the KO-experiment has executed 13 trades, with 10 winners, and a 11.4% net profit. There is a buzz around the office about the “knock-out cola trade”, and brokers are itching to get in on it with client funds. You are considering giving the green light to the “Full Monty,” when Stan the Statistician walks into your office.
Stan’s title is “Risk Manager”, but people around the office call him Stan the Statistician, or Stan the Stats Man, or worse (e.g. “Who is the SS going to s*** on today?”) He’s actually a nice guy, but most folks consider him an interloper. And Stan seems to have clout with corporate, and he has been known to use it to shut down trades. You actually like Stan, but you already know why he is stopping by.
Stan begins probing about the KO-trade. He asks what you know. You respond that Debra told you that the model has an R-squared of 0.92 based on 756 days of back-tested data. “And now?” asks Stan. You answer, “a 76% success rate, and profits of around 21% in 5 months.” And then Stan asks, “What is the probability that that profit is essentially due to pure chance?”
You know that the S&P 500 historically has over 53% “up” days, call it 54% to be conservative. So stocks should follow suit. To get exactly23 wins on KO out of 30 tries is C(30, 23)*0.54^23*(0.46)^7 = 0.62%. To get at least 23 (23 or more wins) brings the percentage up to about 0.91%. So you say 1/0.091 or about one in 110.
Stan says, “Your math is right, but your conclusion is wrong. For one thing, KO is up 28% over the period, and has had 69% up days over that time.” You interject, “Okay, wait one second… so my math now says about 23%, or about a 1 in 4.3 chance.”
Stan smiles, “You are getting much closer to the heart of the matter. I’ve gone over Debra’s original analysis, and have made some adjustments. My revised analysis shows that there is a reasonable chance that her model captures some predictive insight that provides positive alpha.” Stan’s expression turns more neutral, “However, the confidence intervals against the simple null hypothesis are not as high as I’d like to see for a big risk allocation.”
Getting all Mathy? Feedback Requested!
Do you want to hear more from “Stan”? He is ready to talk about adjusted R-squared, block-wise cross-validation, and data over-fitting. And why Debra’s analysis, while correct, was also incomplete. Please let me know if you are interested in hearing more on this topic.
Please let me know if I have made any math errors yet (other than the overtly deliberate ones). I love to be corrected, because I want to make Sigma1 content as useful and accurate as possible.
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.
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
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.