Quantum computing is the latest and greatest technology showing promise for various fields, from science to investing and more. Experienced investor Daniel Calugar says that many investment professionals believe it could be a game-changer for financial markets, and there are many reasons to believe they are right.
It's very possible that quantum computing will help revolutionize certain aspects of computing and completely change everything we know about computing in its current state.
In financial markets, quantum computing is most often associated with algorithmic trading and portfolio management. Investors have the potential to utilize the immense processing speed and power it possesses to help them solve complex math problems while also conducting real-time analysis of huge data sets.
Below, Dan Calugar will explore the potential of quantum computing in algorithmic trading and how it could indeed be a game-changer for the industry as a whole. He'll cover all the basics of quantum computing, the advantages it provides compared to classical computing, and how it might be applied to various trading strategies.
What is Quantum Computing?
Although they share the same name as traditional computers, quantum computers aren't just the subsequent development in the long line of conventional computers. Instead, they are new machines altogether.
There are some problems that even the most advanced computers just can't handle, and these are the problems quantum computing seeks to solve.
Quantum computing is an emerging technology that draws its inspiration directly from the laws of quantum mechanics and has the ability to run circles around traditional computers.
This technology integrates physics, mathematics, and computer science concepts to help solve extremely complex problems faster than traditional computers. The general umbrella term includes both the research of new hardware and the development of applications that can run on it.
Traditional computers use what are known as "bits" to carry out all operations. This familiar technology is a string of "0s" and "1s" that tell the computer what to do.
Quantum computers, meanwhile, use something called qubits, which allow them to execute quantum algorithms on a multi-dimensional basis simultaneously.
It does this by utilizing quantum mechanical effects, including entanglement and superposition. Daniel Calugar provides a generic explanation of each below.
Entanglement
Entanglement is when two different systems are linked so closely together that obtaining information about one of them gives a person instant information about the other. The processors used in quantum computing allow the users to draw particular conclusions about one individual particle by measuring another particle.
A simple example would be that if an individual qubit spins to the left, then the other qubit always spins to the right -- and vice versa. Therefore, the principle of entanglement provides quantum computers with the ability to solve even the most complex problems much faster than classical computers.
At its core, entanglement is a qubit's ability to correlate its own state with that of other qubits.
Superposition
The principle of superposition comes directly from traditional physics. When you add together at least two quantum states, the result can be another quantum state. Each quantum state can also be represented as a total sum of at least two other distinct quantum states.
In simpler terms, the fact that superposition can be applied to qubits is what allows quantum computers to have parallelism. This gives them the ability to process an unimaginable amount of operations all at the same time.
The Advantages of Quantum Computing
While much of the previous sections were technical in nature, it's fairly obvious that comparing quantum computers to typical computers isn't really an apples-to-apples exercise. Indeed, as Dan Calugar points out, quantum computing has the ability to completely revolutionize entire industries, while classical computers have a finite limit to what they can do.
There are many generic use cases for quantum computing that any industry can take advantage of. Below are some of the main ones.
Simulations
A big part of predictive analyses nowadays is back-testing theories before putting them into practice. This form of modeling is used in just about every industry, from aeronautics to consumer products to financial markets.
The power of simulations is that they allow companies and individuals to test their ideas in the simulation before they invest the time, money, and effort in what could prove to be a disaster in the real world.
In terms of quantum computing, simulations can be done on an extremely large scale to help predict the success or failure of very complex concepts. They can also be used in approximation methods, which not even the best supercomputers of today can handle with such extreme accuracy.
This aspect of quantum computing is game-changing in scientific fields where chemical simulations can be completed to ensure the safe creation of drugs and various products that might go to market.
It could also prove invaluable in financial markets, allowing investors to test out the efficacy of the complex mathematical formulas they've come up with as part of their algorithmic trading strategies.
Optimizations
While simulations handle macro functions, optimizations are used to fine-tune the micro details of the larger processes. Quantum computing can help to improve R&D, production, and supply chains by optimizing every task that needs to be completed along the way.
Quantum optimization is already being used in loan portfolios to allow lenders to improve what they can offer customers by lowering their interest rates and freeing up their own capital to use.
The more optimized a process is, the better it will perform -- no matter what you're talking about. This is another area where standard computers don't hold any weight compared to quantum computers.
Machine Learning
One of the biggest trends in computing today is machine learning or ML. This process includes having computers analyze vast amounts of data so that, over time, they can make more informed decisions and more accurate predictions.
Computers can use data and information provided to them to learn about a particular topic or area so that they can then predict what will happen next. In financial markets, this provides a huge opportunity for investors to execute trades on major prospects that they may not have identified through their own manual analysis.
Every investor seeks to predict where the markets will move in the future. Machine learning is becoming an ever-important tool to help them do that, and quantum computing is the key to taking this to the next level.
Using Quantum Computing in Financial Markets
Quantum computing has many use cases in financial markets. Because it is exponentially faster and more efficient than classical computers, the possibilities are truly endless.
At the moment, quantum computing isn't accessible to everyone. However, as developments continue in the next few years, it's likely to become more mainstream, more readily available to the market, and more affordable to obtain and use.
Daniel Calugar says that when this happens, use cases are likely to increase further for algorithmic trading, which will result in the development of even more ideas and strategies.
It is presently recognized that quantum computing can produce results that are 99% error-free, but researchers are working to close that accuracy gap even further. How amazing would it be for investors to have a computing tool that always made the right predictions?
While the true potential of quantum computing may not be realized for a few years, there are still plenty of current use cases for it in financial markets. Below are a few examples that show just how game-changing it could be for the industry.
Portfolio Optimization
Maintaining an optimized portfolio is not only essential to stay the course in terms of an investment strategy, but also to mitigate risks caused by market fluctuation.
One of the main benefits of algorithmic trading is also one of its potential downfalls: Because trades are executed so precisely and so quickly -- at such a high frequency -- it's easier for portfolios to become unbalanced before you know it.
Quantum computing can help investors always find the best combination of various assets to hold simultaneously. It allows algorithmic trading to go on at full speed while simultaneously balancing the portfolio, ensuring that nothing gets "out of whack." Quantum computing is much more efficient in optimization than classical computing could ever hope to be.
Risk Management
There's significant risk management built into portfolio balancing and optimization. That's one of the main goals of the exercise.
But, there are also a lot of other risk management strategies that can be put into place thanks to the power of quantum computing. These computers can simulate various scenarios so that potential risks can be identified and then mitigated.
This is very useful in high-frequency trading (HFT) but also in all other executions of algorithmic trading. This expands on the simulation use case described above. The ability to run multiple, large-scale simulations allows investors to identify potential risks that they may not have ever anticipated but that the quantum computers can extract from the data.
Identifying Trends and Patterns
All investing research is based on identifying trends and patterns, according to Daniel Calugar, but this can be done on a much larger scale with quantum computing.
Today's most powerful supercomputers have the ability to analyze many different types of data to help investors make more informed trading decisions. However, quantum computing takes this to an entirely new level.
For instance, quantum computing can analyze standard data such as quarterly earnings reports, historical data such as past seasonal performance, and alternative data such as satellite imagery all at once. Then it can instantaneously cross-reference and compare that data to deduce what's a real trend and what might be a mirage.
Here's a prime example ...
Conventional computers are using alternative data, such as satellite imagery, to analyze vehicle traffic at big box store parking lots to try to predict an upcoming increase or decrease in sales. Suppose the parking lot shows an increased number of cars coming and going during a particular window of time, for instance. In that case, this could indicate that the company is going to experience an increase in revenue.
The power of this for investors is that it is a real-time look at when the sales are increasing. So, rather than waiting until the next quarterly earnings call to see what happened -- and make an investment decision based on past performance -- traders can make those decisions on the fly and get in at lower stock prices before they soar once the earnings call has happened.
This satellite imagery alone may not tell the whole story, though; this is where quantum computing can take alternative data to the next level.
Once this information is identified -- that there is more vehicle traffic than usual at the big box store -- the technology can cross-reference that information with other data sources.
For example, it can integrate historical data to analyze whether the increase in traffic may be due to seasonality. For instance, if this boost in traffic is happening in December, it may simply be due to the holiday shopping season.
While this also would result in a boost in sales, of course, it may not produce a profitable investment. This is because the expected increase in sales may already be priced into the stock's value.
In other words, even exorbitant amounts of data may not tell the full story of what's actually happening if not analyzed to its full extent. And while even the most powerful classical computers may not be able to handle these complex data sets and cross-referencing tasks, quantum computing easily can.
Quickly Point Out Profitable Trades
Those involved in high-frequency trading are especially excited about the prospects of quantum computing thanks to the exponential boost in speed and accuracy that it provides for this type of algorithmic trading.
Quantum computing can quickly point out profitable trade opportunities for high-frequency traders so they can capitalize on even small discrepancies and inequities in the market.
Back-Test New Strategies
Back-testing is an essential part of any algorithmic trading strategy. While the algorithms that power this type of trading are ultra-powerful, it's still possible that they may not work.
Daniel Calugar says that back-testing allows investors to test their theories without risking any of their money. So, if they have new ideas about the importance of certain data points, they can use back-testing to determine whether such insights would be effective in the real world.
Classical computers can handle all of this, but they do have their limitations. For example, they might be able to back-test a particular theory against current market conditions, but they may not be able to anticipate changes in the market.
This is where investors can use quantum computing to not only back-test against current and past market conditions but also to simulate potential market fluctuations that haven't happened yet.
As we've discussed already, this is a key component of quantum computing- the ability to simulate what might happen in the future, even if it hasn't happened in the past.
And as it relates to back-testing specifically, this ability helps investors ensure that their algorithms and coding are robust enough to withstand most potential fluctuations in the market.
Regulators Can Identify Spoof Data
Quantum computing can also help regulators of financial markets, according to Dan Calugar. Ten years ago, the Securities and Exchange Commission launched MIDAS -- the Marketing Information Data Analytics System -- so they could identify fraudulent practices such as spoofing.
It had become a large issue at the time, as people executed certain trades to cause a false increase in the supply-and-demand chain. This activity can cause many negative effects on the market at large.
When these spoofers spring into action, they leave behind a trace of what they did. It may be hard to uncover using typical computers, but quantum computers could allow regulators to discover spoofing activity and enable them to distinguish between potential spoofs and real data.
In this way, quantum computing will assist not only capital market institutions but regulators as well to ensure fair play for everyone in the financial markets.
About Daniel Calugar
Daniel Calugar is a versatile and experienced investor with a background in computer science, business, and law. While working as a pension lawyer, he developed a passion for investing and leveraged his technical capabilities to write computer programs that helped him identify more profitable investment strategies. When Dan Calugar is not working, he enjoys working out, being with friends and family, and volunteering with Angel Flight.
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