A Quantum Economics Perspective on Navigating Flash Crashes
BY PREETHA MUKHERJEE/ NOVEMBER 15, 2024
Like poker, the stock market is a high-risk business: a bad beat in poker wipes out your chips, and flash crashes in the stock market erase significant value of stocks and bonds within seconds, leaving investors reeling before making recovery. In light of this, the future of stable markets may well lie in the quantum realm, where risk, reward, and innovation collide.
icture the Indian stock market on a regular trading day. Excited about the potential economic boom and investment opportunities, retail investors are actively buying and selling stocks, hoping to capitalise on the country's growth. Suddenly, within seconds, prices start plummeting — stock values drop to unimaginable lows before swiftly bouncing back. This phenomenon is called a flash crash: a sudden, extreme drop in asset prices, often triggered by high-frequency trading algorithms.
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They highlight vulnerabilities in financial markets, where rapid trades can amplify volatility in no time. The exact causes of flash crashes are still unknown, as they occur swiftly. What is clear, however, is that they are not driven by changes in the fundamental value of individual stocks, such as company earnings or economic data. Instead, flash crashes affect entire stock indices, indicating that the phenomenon is more related to systemic market dynamics than the performance of any single company. One of the most likely causes of flash crashes are algorithms or computer programmes. Algorithms have become the most popular trading tool amongst traders (Corporate Finance Institute, 2023). These algorithms are a part of high-frequency trading (HFT) and are coded to react to market pressures such as shifts, that occur due to buying and selling operations. By reacting instantly to market shifts, HFT firms can exploit minor pricing differences, often benefiting from lower trading fees. When these algorithms start selling at a rapid succession it results in a snowball effect, where each sale triggers other algorithms to do the same, leading to a sudden drop in asset price. This happens within milliseconds, giving the market no time to react and stabilise.
Instead, flash crashes affect entire stock indices, indicating that the phenomenon is more related to systemic market dynamics than the performance of any single company.
The NSE Nifty 50 Index flash crash in the Indian stock market on October 5, 2012, is a significant example of how high-frequency trading algorithms can trigger flash crashes. The crash was caused by 59 erroneous trades by a brokerage firm called Emkay Global, which made an unintended order through an algorithm. This led to a circuit break, causing massive sell offs including a downturn of 15.5% in stocks trading – well beyond the 10% mark (Times of India, 2012). In the stock market, a circuit breaker is like a safeguard mechanism that temporarily halts trading when a market index or a company stock experiences a rapid and significant percentage decline. This pause is designed to prevent panic selling and excessive volatility, giving investors time to absorb information and make decisions. The Nifty crash exposed critical vulnerabilities within the Indian stock market as the NSE index showed a sudden fall by over 900 points within seconds before quickly rebounding to its initial levels. It wiped out INR 18 lakh crore from the market (Securities Exchange Board of India, 2012), revealing the failure of the circuit break mechanism. After the Nifty flash crash, regulatory authorities, including the Securities Exchange Board of India (SEBI), reviewed and strengthened market safeguards, implementing stricter controls on algorithmic and high-frequency trading to prevent similar incidents in the future.
The ambiguity of the algorithmic system also gives rise to ‘Spoofing’. Spoofing occurs when traders place large amounts of buy or sell orders without intending to execute them. This manipulates the market prices and creates false market perception, leading to genuine investors making trading decisions based on misleading signals. This triggers the high-frequency trading algorithms and contributes to the destabilising effects, thereby causing flash crashes.
One of the most significant flash crash cases involving the mechanism of spoofing was committed by Indian-origin trader Navinder Singh Sarao during the May 6, 2010 US Market crash. The Dow Jones Industrial Average, a stock market index that tracks the performance of 30 major publicly traded companies in the United States, fell by about 1000 points before shortly recovering. Sarao used spoofing and placed false orders which influenced market behaviour, contributing significantly to the flash crash, which wiped out USD 1 trillion in market value within minutes from the US market, as authorities later revealed (Wall Street Journal, 2010). His case became a significant turning point, leading to increased regulatory scrutiny and tighter controls to prevent similar market manipulation tactics in the future.
Pictured: Illustration by unknown via Pinterest
Role of Quantum Economics in the Stock Market
As markets become increasingly vulnerable to algorithmic mishaps and manipulative practices like spoofing, the need for innovative solutions has never been more urgent. This is where quantum economics steps in offering a fresh lens to view the complex, fast-paced nature of the modern financial market. While it is a nascent concept, it is shaping up to be the key to potentially mitigating flash crashes and bringing stability to an inherently unstable system.
Quantum economics approaches the economy like a complex, interconnected system, similar to how quantum physics examines the unpredictable interactions between particles. Instead of viewing money and economic activities in a simple and linear way, quantum economics explores their intricate and dynamic relationships, recognising that economic interactions can be complex and non-linear, much like the behaviour of particles in quantum physics.
A key figure in this field is Asghar Qadir, a Pakistani economist. Asghar Qadir contributed to quantum economics by using ideas from quantum mechanics to address uncertainty in economic behavior. Another economist, David Orrell, built on this by exploring how complex and unpredictable patterns in the economy could be understood through a mix of quantum and traditional economic ideas. Their work highlights that markets and economies are interconnected and uncertain, much like the quantum world, giving us a fresh way to understand economic behaviour.
A quantum economics model can be constructed using quantum concepts as follows: First, identifying key factors like stock prices, trading volumes, and market sentiment is important. Second, instead of treating these factors as fixed values, they are represented as probabilities, allowing each factor to exist in multiple states simultaneously. For instance a tech stock might have a 70% chance of staying around its current price, a 20% chance of rising slightly, and a 10% chance of dropping sharply. This approach helps investors prepare for a range of outcomes rather than just a single prediction, making it easier to gauge risk and make informed decisions.
Pictured: Illustration by unknown via pinterest
Next, quantum principles like Entanglement are used to show how these factors are interconnected—changes in one can rapidly affect others. Entanglement can be seen when a sudden drop in one sector, like tech stocks, triggers declines in other sectors due to interconnected trading strategies or investor reactions. This helps explain how sudden market shifts and correlations can lead to widespread instability. Equations are then built to describe these interactions and run simulations to see how sudden drops in prices might occur under different conditions. Finally, the model’s results are compared with actual flash crashes to refine it and improve predictions of when such crashes might occur (Schegel, A.A., 2000).
These principles can be perfectly applied to solve instances of flash crashes as they offer new perspectives on how sudden market movements change, and view economic concepts differently from traditional models. They acknowledge that people's choices and emotions are intertwined in complex ways, making it impossible to simply aggregate these factors for a clear picture. For instance, happiness and economic growth are treated as separate issues or loosely connected, rather than being directly linked as they are in traditional economic models.
Quantum economics can also be used to analyse how information is disseminated during high-frequency trading by leveraging quantum information theory including concepts such as superposition. Superposition means that a particle can exist in multiple states until observed. A classic example of this is Schrödinger's cat: a cat in a box exists in both "alive" and "dead" states until someone opens the box and checks. Similarly, in economics, decisions hold multiple potential outcomes until a choice is made, locking in one specific result. Application of this concept helps in understanding how traders process and react to information, particularly during flash crashes.
Furthermore, during a flash crash, market participants might be simultaneously considering different scenarios such as an economic downturn or a temporary glitch. These conflicting beliefs can coexist until new information is received or a decision is made, causing the market to react suddenly and dramatically. Quantum economics uses this idea to model how such simultaneous, overlapping beliefs can contribute to market instability. Trader's uncertainty and the presence of multiple possible outcomes can lead to unpredictable and rapid market movements, as seen in flash crashes. By understanding this, economists can better analyse and potentially mitigate the causes of such market disruptions.
Quantum economics uses this idea to model how such simultaneous, overlapping beliefs can contribute to market instability.
Challenges of Quantum Economics
Nearly all major economies have rolled out national missions and programmes to accelerate research and development in quantum computing. India formally entered the race when it announced its National Mission on Quantum Technology and Applications (NM-QTA) in the Union Budget 2020 (Observer Research Foundation, 2023).
Quantum economics, albeit a good solution to the problem of flash crashes, is not very easy to implement in a country like ours. The main challenge that India faces is financing the upgradation of quantum technology. According to the Technology Information Forecasting and Assessment Council, INR 3000 crore has been allocated towards building quantum computers. While this is a substantial investment, the heavy import duties that are levied on the components such as semiconductors make the opportunity cost high (Mishra, 2023).
There is also information asymmetry in the field of quantum economics. This stems from the lack of interdisciplinary research in India particularly in the areas of quantum economics and physics. Quantum economics is a highly specialised area that demands a deep understanding of both quantum theory and economic principles, and the lack of expertise has left only a few specialists capable of navigating and applying this complex field in India. Without appropriate academic programmes that address these intersections, the development of expertise will remain limited.
Additionally, regulatory frameworks in India are not yet adapted to accommodate the non-linear, probabilistic approaches of quantum economics, posing a challenge to its integration into traditional economic models. Concerns over data privacy and security in quantum systems further complicate adoption, requiring significant advancements in cybersecurity measures.
For India, this could be an opportunity to leap ahead, positioning itself at the forefront of a new economic era, provided the right investments and collaborations are made.
Maintaining stability in the stock market requires a complete reevaluation of market dynamics instead of just a technical fix. The traditional economic models remain ineffective in keeping up with the growing complexity and interconnectedness of the market. Quantum economics provides a solution to this, revolutionising the way the stock market is perceived by embracing uncertainty and interconnectedness at its core. Despite its immense potential, particularly in preventing sudden market failures, the journey to complete integration is arduous, challenging us to rethink our approach to finance. It requires blending science with economics in ways previously unimaginable. For India, this could be an opportunity to leap ahead, positioning itself at the forefront of a new economic era, provided the right investments and collaborations are made.
Keywords
Financial Regulations (SEBI), Economic Interconnectivity, Risk Management in Trading, Quantum Computing for Finance, Financial Market Innovation, Market Stability Solutions, Algorithmic Trading & HFT, Flash Crash Analysis, Stock Market Volatility, Quantum Economics, High Frequency Trading, Algorithmic Trading, Spoofing, Investment Strategies, Financial Markets, Finance Innovation, Nifty Flash Crash, Stock Market Insights, Risk Management
References
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