[The Institute for Innovation Development interview series invites innovation experts, innovative business leaders and emerging fintech companies to talk to our readers about their latest innovation activities. The series seeks to learn from innovative business creators, uncover innovation best practices, and discover how to apply these insights into a financial services business model.
We recently sat down with co-founders Andrew Dassori and Mark Landis of Wavelength Capital Management—an innovative money management firm leading the efforts in connecting academia and artificial intelligence to the investment process.]
Hortz: Your firm has allocated substantial resources to apply artificial intelligence to investing. How exactly are you executing this and what are you learning?
Dassori: Marvin Minsky, the founder of the artificial intelligence lab at MIT, breaks the field down into three basic approaches: connectionist reasoning and rule-based and case-based artificial intelligence. We focus on rule-based artificial intelligence, as it is the most applicable and most relevant to what we do when investing in the markets. Rule-based systems provide a way to test logic by looking back to see if investment rules have been return predictive. This can be done alongside an ongoing investment process. Artificial intelligence is thus a tool for us to look at the past as well as the future. It allows us to be more predictive.
Our research involves identifying investment logic that is fundamental in nature and actionable through liquid financial instruments. We test this logic across different time frames and economic environments using extensive amounts of empirical data. If the investment logic is consistent, significant and robust, we are then able to implement it live in parallel to our existing decision engine for investments. It can be added to the portfolio to enhance investment processes already in place.
We have also learned that as smart as humans are, computers simply have more capacity to keep track of rules and can process them more efficiently with discipline. Computers are able to analyze data using higher level statistics than what would otherwise be possible. We are not advocating for computers to take over the investment process entirely—there are meaningful risks to this. We believe having humans oversee the design and implementation of these processes is critical, but as markets and the set of factors driving them become more complex, incorporating artificial intelligence will be increasingly important to managing investment strategies successfully.
Hortz: How many different kinds or levels of algorithms are there being used in investing today? How are algorithms being improved or innovated?
Dassori: Algorithms can be incredibly sophisticated or designed as simple systematic ways to tackle any basic problem. We use a robust set of algorithms that are designed to work together, much like a doctor might prescribe a combination of medicines to fully address an ailment. Different algorithms are used to assess forward-looking risks as a base for our investment strategy, and others to actively gauge the primary investment characteristics we believe to be predictive of excess returns: carry, momentum and value. We measure each of these characteristics across multiple time frames and individual inputs.
The different algorithms may work at different times or during different market cycles, but the combination of them, working over the long-term, is what makes our system robust. We combine their output efficiently to produce investment signals, and these signals determine buy and sell decisions within our investment process.
As to innovation or evolution of algorithms, know that the process described above improves each day through the ongoing reassessment and refinement of existing components of the overall system. In a rule-based system, you can build triggers that will remove an asset that is no longer responsive to changes in the economy and rebalance risk automatically to the assets that are responding. You don’t have to be sitting in front of a Bloomberg [terminal] waiting to spot disconnects from the market. We can automate that process as a risk management component to ensure that we address any issues in the portfolio when relationships change. This provides us more time to focus on building our systems by adding new pieces of investment logic and refining new algorithms that enhance the predictive return power of each of the signals.
Hortz: Your website mentions that your investment strategy is grounded in academic research and uses financial and economic concepts commonly examined by leading academics. How does that research help your investment process?
Dassori: Academia provides a rich pipeline for ideas and the key for us is translating theory to profitable investment strategies. At the root of our investment process are concepts that are regularly the subject of academic research—carry, momentum and value. New approaches and measures related to these continue to be developed, and this offers a great source of information as we build upon our process. To this end, we connect directly with professors at top universities to discuss their research and how it could be incorporated in our system. We benefit greatly from this active dialogue and debate as we adapt efficiently to new market dynamics.
Hortz: Your market observations indicate we have to “meet the challenge of a changing investment environment.” How would you characterize the nature of this change and the ways you are trying to address it?
Landis: The rate of change in markets has been dramatic and we expect further changes to market structures in the future. For example, there is a trend to increased liquidity in broader index level products as opposed to the underlying cash instruments they are based on. And with this, we believe in moving with liquidity, not away from it. Our number one screen is liquidity and signs of liquidity.
Also, the use of computers has taken previous inefficiencies out of many instruments. Previously, viable investment edges versus competition have become less and less sustainable, and many legendary investors—such as Bacon, Soros, and Robertson—have given back money as inefficiencies they took advantage of have waned.
We believe that the use of dynamic technology can identify new opportunities that garner excess returns for investors. Our models are designed to be dynamic and their effectiveness at predicting excess returns is constantly re-assessed.
Hortz: Besides being a liquid alternatives investment manager with a mutual fund and separate account offerings, your company overview mentions that you provide research and advisory services to investment companies, financial advisors, robo-advisors, etc. Can you walk us through how you work with these clients?
Landis: We have built an analytical engine that has a range of useful applications for investors. As different client types have different requirements and preferences, our goal is to make our process as useful as possible, and not just for us. We produce signals every day and maintain a research library on our website that is open to the public. We actively engage with other investment firms and encourage clients to use us as a resource for their own investment research and analysis.
As an example, within the basic robo-advisor world right now, the core of market allocations are based on Modern Portfolio Theory. While modern portfolio theory is interesting, it is close to 30 to 40 years old at this particular point and markets have changed … the world has changed …. the nature of information has changed. So, the utilization of just modern portfolio theory as a way to invest is a good basis, but with hedge fund and statistical analysis, you are going to have more complex ways, and different ways, of actually analyzing the markets and getting the returns and risks that you want to get. The way we look at the markets is the way the more sophisticated investors are looking at the markets, through utilization of economics, utilization of statistics, utilization of algorithms, to make sure that we are looking at all different factors. At the end of the day, the basis of what we are doing is not rocket science in any way shape or form, but it is a more sophisticated way of actually analyzing the markets, which is the next generation of how people are making money. That’s how you create both a beta play and an alpha play. This is the future of where some robo-advisors will be able to differentiate and be able to deliver more value to a larger group of customers.
Hortz: How exactly did you go about “engineering” an alternative investment process? Any advice for other RIAs considering or in the midst of engineering new investment methodologies?
Landis: We have spent more than 10 years improving our models. Andrew and I spent a tremendous amount of time analyzing what was the true edge of the world’s greatest investors. That was the root of our technology. Systematizing the logic and then testing is not easy to recreate. However, using machines to help in every phase of analysis is crucial, as the amount of work and data is something a human just can’t do on their own. So my suggestion is that no matter what one needs to be cutting edge on, computers can make your process is more efficient.
My biggest recommendation is just “don’t be afraid.” People are looking at old models vs new models. The markets have changed. Liquidity has changed. The basics have changed. Information and the amount of information have changed. Know that the utilization of technology is a great equalizer, where the human mind cannot process all these sort of things easily. Don’t be afraid of technology, because, at the end of the day, people need advice and you can triangulate a lot more info quicker than you were able to before with technology to service that advice. So don’t be afraid—help guide clients through all this information with technology to guide you both.”
Hortz: Thank you gentlemen!
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