Quantum Computing Solution for Your Financial Institution
In the time it takes you to read this sentence, investment banks, hedge funds and other investors around the world will have traded $80 million in stocks. That adds up to more than $200 billion per day or nearly $70 trillion total last year, according to the World Bank. Some single trades involve numbers that are scary to imagine. The financial institutions dedicate substantial resources to predict the value of their various assets to mitigate risks in the future. It’s not what we can call gambling because then they would not make so much profit. The predictions that are made with respect to these assets are based on the historical trends and those trends can thus be trusted with respect to the fluctuations in the market such as a recession or a major change in interest rates. If an analyst finds that a portfolio has a high likelihood of plummeting, they can pad it with less risky investments. The dark side is that these calculations are resource-intensive and the institutions which do those already have an in-house supercomputer or have sufficient resources from a dedicated cloud provider.
Several quantum algorithms for financial institutions are currently being tested and implemented. Although a real-time application of these apps is still years away to have a quantum advantage over everybody else, it is good to start with to identify the various high-value problems that can actually be solved using quantum computing. Several types of challenges face financial services firms that quantum computing may address. These challenges include the classification and selection of assets, customers, and vendors by default risk; and the detection of fraud, money laundering or other criminal activities by finding complex variable relations. A series of complex and often concurrent multi-disciplinary tasks is required to resolve such challenges. Quantum computers, with greater speed and accuracy, might provide new capabilities in these areas.
Quantum computing can be particularly helpful in solving optimization problems like portfolio optimization, management, and diversification. It may also extend to complex risk measures such as Conditional Value at Risk
JP Morgan Chase(JPMC) and Barclays are among the first few banks to associate themselves with the IBM Q network and experiment with quantum computing to accelerate risk mitigation and improve performance modeling.
JPMC is focusing to solve problems with relevance to its business like portfolio optimization, option pricing, and financial health classification. It has been performing a series of experiments in collaboration with IBM to explore quantum advantage, its limitations and potential for future real-world applications. Many algorithms have been developed and tested on real quantum hardware and also compared to the current classical computer implementation of the same.
Barclays already has a working group of quantum computing experts who come from the statistical modeling field and they code quantum apps that are tested on a quantum computer hosted on a cloud service akin to what IBM does. The team is testing the apps for optimization problems — such as determining the correct sequencing and prioritization of activities — with a final outcome of settling thousands of trades every business day efficiently and accurately. To just get an idea of the scale of the optimization problem they are trying to optimize consider that selecting the optimal order of execution for 5,000 trades has more than 4.2 x (10^16,325 )possibilities.
Monte Carlo Simulations are very well used to develop forecasting models that are further used in predicting the outcomes for various algorithms in financial institutions. The simulation provides a visual representation of many or all potential outcomes, assisting users to assess the relative risks of a decision. It models the future as a series of forks the road. A company might go under; it might not. President Trump might start a trade war; he might not. There may be a recession; there might not. An analyst estimates the likelihood of such scenarios, then generates the millions of alternate futures at random. To predict the value of a financial asset, they use a weighted average of these millions of outcomes. While a classical Monte Carlo simulation may require millions of classical samples its quantum counterpart only requires a fraction of those samples which is also called the “quadratic speedup” for such simulations.
JPMC along with IBM developed a method of option pricing and tested on real quantum hardware, an algorithm that gives them a quadratic speedup over the classical Monte Carlo methods which will be discussed over in a follow up article.
Originally published at: http://quantumhermit.com/leveraging-quantum-for-financial-engineering/
