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.
Balhiser Financial 