“Quantum computers enable us to use the laws of physics to solve intractable mathematical problems,” said Marcos López de Prado, Senior Managing Director at Guggenheim Partners and a Research Fellow at Lawrence Berkeley National Laboratory's Computational Research Division. “This is the beginning of a new era, and it will change the job of the mathematician and computer scientist in the years to come."
Marcos Lopez de Prado argues that some of the most popular optimization techniques used in Finance are in fact detrimental. Take mean-variance optimization (MVO), the most commonly used portfolio construction technique, with its multiple upgrades and variations throughout the past 60 years. A great majority of academic papers apply MVO when the authors are faced with the dilemma of building a diversified portfolio. One would expect that such venerable technique would be among the best performing portfolio construction methods, right? Think again.
A number of studies have demonstrated that MVO portfolios underperform the so called “naïve portfolio”, that is the portfolio that splits assets equally among holdings (see here for example). And yet MVO is taught in every business school as one of the key results in Finance. Shouldn’t students be warned that MVO is detrimental, relative to a naïve allocation? How can a Nobel prize-winning theory lose to the most rudimentary scheme.
This is not a unique example. There are plenty of revered financial techniques that fail to perform as advertised. Cointegration models are known to lack robustness, in the sense that small changes on a few observations will lead to entirely different forecasts. This is particularly problematic in a discipline like Finance, where the signal-to-noise ratio is low and measurements are far from precise. Still, unstable econometric methods are routinely used by economists to forecast macro variables and by the Federal Reserve to inform their life-changing decisions.
In fairness, these methods were designed and vetted for academic consumption only. They are toy models, to be used for in-sample philosophical disquisitions, not in out-of-sample industrial applications.
Real-world applications require a degree of complexity and robustness that simple models cannot satisfy.
How Quantum Computing May Save Finance
There are numerous instances in which machine learning methods deliver better results than classical calculus or linear algebra applications. But machine learning often deals with NP-complete or NP-hard problems, which demand overwhelming computational power
Building Diversified Portfolios that Outperform Out-of-Sample by Marcos Lopez de Prado Guggenheim Partners, LLC; Lawrence Berkeley National Laboratory; Harvard University - RCC
The promise of financial quantum computing (QC) is that soon we will not need to dumb-down models, or rely on heuristics. We will develop models cognizant of reality’s complexity, and solve them in their NP-complete grandeur. Think about it: If HRP can improve your out-of-sample Sharpe ratio by 31% over MVO’s, what will the improvement be once you replace the DC+Heuristics tandem with QC+Completeness? Perhaps 50%? That means boosting your Sharpe ratio from 1.50 to 2.25, quite worth the management fee.
Experts in finance, mathematics, computer science and physics have agreed to participate as editors and content contributors of the community, including:
- Dr. Horst Simon, Deputy Director of Lawrence Berkeley National Laboratory (Berkeley Lab)
- Dr. David Bailey, Senior Scientist (retired) at Lawrence Berkeley National Laboratory
- Dr. Jonathan Borwein, Laureate Professor in the School of Mathematical and Physical Sciences at the University of Newcastle
- Dr. Peter Carr, Executive Director of the Courant Math Finance Program at NYU
- Dr. Kesheng (John) Wu, Berkeley Lab Group Leader
- Dr. David Leinweber, Co-founder of Berkeley Lab's Center for Innovative Financial Technology
- Dr. Blu Putnam, Chief Economist for CME Group
- Dr. Michael Sotiropoulos, Managing Director, Global Equities at Deutsche Bank Securities Inc.
Through 1QBit, members will be able to access an interactive notebook and a set of tutorials that will allow them to explore applications of quantum software. “Sharing the tools we’ve built with the community will create a better understanding of how quantum computing can be applied to finance, and in turn will inspire the development of additional tools that enable these new applications,” said Landon Downs, President and Co-founder of 1QBit.
“Quantum computers have the potential to provide a different approach to solving very difficult finance problems, but it will take the collective intelligence of many experts to do so," said Vern Brownell, CEO of D-Wave and former CTO of Goldman Sachs. “We co-founded Quantum for Quants as a way to bring together such experts and to provide resources and connections that can’t be found anywhere else. In addition, when we find problems being discussed and vetted in the forums that we believe are a good fit for D-Wave's quantum technology, we can identify them as candidates for transition from the simulators to a D-Wave system."
Navy SPAWAR also using Dwave
SPAWAR, a division of the US Navy, will pay Canadian company D-Wave to learn how to use its quantum computing infrastructure, according to a federal filing posted online in May.
Quantum computers employ quantum bits, or qubits, each of which can be zero or one or both, unlike the regular bits in classical computers. The superposition of qubits lets machines perform great numbers of computations at once, making a quantum computer highly desirable for certain types of processes. Google recently found that quantum annealing with D-Wave hardware is 100 times faster than simulated annealing on a classic computer chip.
SOURCES - Dwave Systems, quantumforquants, FBO.gov, Journal of Portfolio Management