Quantum computing helps financial services organisation improve portfolio optimisation

Quantum computing helps financial services organization

A joint team of quantum computing engineers and financial analysts at this industry leader in digital financial services, led by a forward-thinking CIO, knew they wanted to take advantage of quantum computing to solve unique challenges that could not be handled with classical computing alone.

This company sees value in preparing to be “at the ready” when quantum computing machines become more powerful. So, they devised a proof-of-concept (POC) project centered on financial index tracking, an essential application of portfolio optimisation used by financial firms for asset management strategies or for creating and managing new financial products such as exchange-traded funds (ETFs). A POC is a smart choice for a team looking to invest wisely in quantum computing as it allows the team to safely experiment and develop solutions in tests now that can be deployed as quantum machines become more powerful.

Financial indexes often consist of hundreds of thousands of assets and an important task to make it practical to manage such portfolios is replicating the financial index using a limited subset of assets, known as cardinality constraints. Such a constrained portfolio optimisation is extremely difficult to solve using classical computing, and standard algorithms tend to have difficulty finding appropriate solutions.

Client snapshot:

Profile

This industry leader in digital financial services wanted to use quantum computing to solve unique asset management challenges that classical computing could not handle

Client Situation

This client sees value in being prepared to act quickly when quantum computing becomes more powerful. They devised a proof-of-concept project centered in financial index tracking

Work Performed

Protiviti partnered with this client to develop a way around the classic cardinality-constraint issue using quantum annealing, a specialised type of quantum computing excellent at optimisation problems

Outcome/Benefits

This significant innovation in the financial services industry introduced an algorithm that reduced overhead costs while keeping customer fees low

A powerful partnership

Partnering with Protiviti, this client developed a way around the classic cardinality-constraint issue by using quantum annealing, a specialised type of quantum computing excellent at optimisation problems. The team worked to construct exact cardinality-constrained portfolios to allow for practical, real-world management of financial index tracking portfolios.

Quantum annealers focus on solving problems with a lowest-energy-state answer that may also be referred to as peak-and-valley problems of optimisation. Annealers can be efficient by ignoring the peaks (in this case, investments that exceed constraints) and focusing on just the best valleys. There is no correct answer, just a reasonably best answer, provided in an incredibly small amount of time.

Start early to be ready for what’s next

Making a practical business case for a complex emerging technology is daunting. This team recognised that being an industry leader in quantum technology would begin a journey, one that was important to start early. They also recognised they would have to be transparent in their communication to the business about what quantum would help them achieve.

They knew it would be impossible to wait until quantum was “ready” for their organisation and then simply flip a switch to begin leveraging quantum. So, they elected to invest the time early on to understand how to apply quantum to their organisation, and how to socialise the technology’s capabilities within the organisation. They spent a considerable amount of time developing visuals and messaging so that non-IT executives could better understand the true power and potential of this technology.

Leveraging previous hybrid classical-quantum approaches, the project team built investment portfolios for companies in both the Nasdaq 100 and the S&P 500 and used daily returns over the course of a year. This was accomplished using the latest Hybrid Solver system from D-Wave. The team then used the new algorithmic approach to build investment portfolios that can generate the same financial returns as traditional portfolios with significantly smaller groups of stocks.

Measurable results

This new algorithm can be used for managing ETF funds, reducing overhead costs for financial managers while helping keep fees low for customers. This approach will be recognised as a significant innovation in the financial services industry.

The POC produced a solution that replicated Nasdaq 100 returns with only 25 securities and S&P 500 returns with 50 securities. The same financial returns could be achieved with far fewer assets in the funds. The project research team also built an enhanced tracking portfolio, which showed that the quantum-built portfolios significantly outperformed the risk profile of the target index by up to 2x.

The more significant impacts of the project are the learnings gained from investing in the POC and then sharing its results with any IT teams currently exploring the potential of quantum computing for their organisations. In reviewing the project’s results, this client’s CIO said, “quantum unlocks two things: the volume of data we can use and the concurrency of models we can have. Executing different datasets together takes a lot of time using traditional models. Quantum brings the ability for me to do that at a much faster pace, giving me the opportunity to take scenario analysis to a completely different plane.” This CIO and his team are recognised evangelists for what’s ahead in quantum computing.

Impact by the numbers:

2X	4X	10X
The quantum-built portfolios significantly outperformed the client’s risk profile of the target index	The proof of concept reduced the number of stocks in the client’s NASDAQ 100 fund	The proof of concept reduced the number of stocks in the client’s S&P fund

This project demonstrates that quantum computing provides a path to deliver performance, accuracy and analysis for faster, better and less expensive capabilities.

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