The evolution and development of modern finance were closely linked to the growth of computers, communications and financial mathematics. Two main changes happened in the 1970s with the beginning of derivative trading and after the crisis of 2007 with the massive introduction of fintech.
Derivative pricing started with equations and formulas in 1974, followed by a wealth of mathematical methods to compute the prices of derivatives. Still, even the 1980s derivative pricing required supercomputers, giving big firms a significant competitive advantage- before the 2007 crisis, the trading volume was close to 1 trillion dollars a day. The prevailing opinion was that derivatives had enabled us to complete financial markets so that any stream of cash flows could be engineered.
The belief was shattered by the 2007 financial crisis, which showed that hedging could be perfect only as long as counterparties stay solvent. The world’s finance became painfully understood that there is risk in derivatives and that free markets are not mechanical. To save them, central banks invested trillions of dollars, euros and yens in liquidity through quantitative easing (QE). The United States invested some 4.5 trillion dollars in cash, roughly one-third of the total monetary mass.
Understanding clients and mitigating problems
After the crisis, the financial world turned its attention to understanding clients and to mitigate the problems created by market manipulations made possible by automated trading. Fintech uses computer-based techniques to model client behavior, to automate dealing with clients and to plan and execute trades. At the same time, several “flash crashes”- sudden but short-lived large drops in market value- has heightened the attention of major players to the risk of crowding of algorithms.
A major new change is now insight through the possible implementation of quantum computers. Instead of binary bits- the classic elementary unity of information- quantum computing use qubits (quantum bits), obtained by the superposition of binary states, this would allow them to process a much larger amount of information thousands of times faster than classical computers.
It was generally believed that quantum computing was far in the future, but Google has recently announced to have actually reached this goal. First, the Financial Times reported that Google had posted a paper on NASA’s website announcing that its quantum computer called Sycamore has been able to perform in three minutes a computation that would take 10,000 years to perform on traditional supercomputers.
Why is it so important to reach quantum supremacy? Modern economies are shaped by complex computations. Supercomputers are used to design products such as cars and planes, invent new drugs, create electronic circuits, model economies, organize large-scale logistics and study the climate. Unfortunately, computations also allow us to build lethal weapons and, increasingly, to monitor and attempt to control the behavior of populations.
In the last 70 years, computing power has increased by a mind-boggling multiple. In the 1960s, even powerful computers were able to perform only a few MFLOPS (millions of floating-point operations per second) while today the most powerful computer is able to perform almost 100 PetFLOPS (10 raised to 17th power).
Even with such power, there are important computational tasks are not solvable or only partially solvable today. The study of combustion and turbulence, the study of molecules from basic physical principles (quantum-mechanical simulation), engineering nuclear fusion, and even logistic problems are some of the grand challenges of computation as defined by federal High-Performance Computing and Communications (HPCC) program. Solving these problems would give a firm or even a nation an important competitive edge.
What would be the importance of quantum supremacy for finance and economics? First, quantum computing would make current cryptographic techniques unsafe. Methods and algorithms will have to be changes, Post-quantum cryptography, or quantum-resistant cryptography, is a flourishing sector of study both in academia and with firms involves in cryptography. Some firms already offer products for post-quantum cryptography, which will be big business.
Intuition, not brute force
But probably the significant changes would be in artificial intelligence (AI) and machine learning. The fact is that we do not know how human intuition and problem-solving works. Ultimately, computers solve problems with a brute-force approach by looking at different alternatives and choosing the best. The search space of quantum computers could be thousands of times larger than the search space considered by current computers. It would become more “creative” through the ability to explore an immense range of possible design solutions. In the fields of finance and economics, quantum computing could lead to analyzing an ample space of heterogeneous data to make financial predictions and understanding the economic phenomenon.
Amid such hope, caution is necessary: financial and economic data are truly complex, and analysis will not necessarily lead to more accurate predictions given the complexity of data. The complexity and non-stationarity of data might defy analysis. In other words, it is questionable if the use of quantum computing will reduce uncertainty.
The global effect of quantum computing on economic and social life will depend on the use that will be made of this tool- and that stems from human decisions rather than being forced by knowledge itself.
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