This article was co-authored by Dr. Roitman, Co-Founder & CTO of I Know First Ltd. With over 35 years of research in AI and machine learning. Dr. Roitman earned a Ph.D from the Weizmann Institute of Science.

This article was co-authored by David Shabotinsky, a Financial Analyst at I Know First, and enrolled at an undergraduate Finance program at the Interdisciplinary Center, Herzliya.

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Summary:

How Econophysics has shaped to become an important field of study for financial markets and policy makers

The uncertainty principle and how its used to predict the financial markets, such as bear and bull markets

What is phase transition in physics and how can it be applied to the financial market

Real applications of Econophysics in finance and policy making today

I Know First’s self-learning predictive algorithm and its application of phase transition

Real differentiated competitive advantages offered by I Know First’s algorithmic solutions

Background

Though many wouldn’t think of the financial market and physicists being much related, Econophysics, a relatively new field of study, is proving how the notions in physics are able to explain phenomenon in the financial market that baffles investors. This combination between physics and finance/economics, allows the business world to better tackle various risks found in finance.

An Oxford University professor J Doyne Farmer explains that, “Ultimately, our biggest interest is in providing a tool for policy makers for when they ask questions like: what restrictions should we place on markets? How much leverage should we let homebuyers have? Should we allow this type of loan? What would happen if there was a mass migration to that type of loan? Is that going to make the housing market more volatile or not? We think we can answer those questions,”

Uncertainty Principle in The Market

Here we will examine two main approaches/principles in explaining the financial markets, phase transition and the uncertainty principle. The former is more popular in explaining many market phenomenon, i.e. explaining the market crash of 1987. The Uncertainty Principle is a broader idea, originally developed by Werner Heisenberg in quantum physics. He essentially explained that the more precisely one can identify the momentum of a particle, the less likely one can identify the position of that same particle. The vice versa applies for the being able to identify the position of a particle. This idea can be applied to better understanding the financial market. The implication of Heisenberg’s principle is that one must choose between being able to determine precisely the movement of say a stock, or the timing of a move (but not which direction). For example, many investors may exclaim that the market will move in an upward direction in the foreseeable future. However, this person cannot tell you the exact timing of the movement. Hence, when bullish or bearish investors place their bets, often times they may experience a short-term loss, but in the long-term be correct in their prediction.

The same holds true for the opposite, in detecting a time for a major movement in the stock market. For example, an upcoming ECB or Federal Reserve meeting tends to have a real impact on the price fluctuations in the financial market. However, one cannot predict with certainty which direction the prices will move, as it will vary depending on the news.

What is Phase Transition?

In physics, ‘Phase Transition’ can refer to the physical transition from one state to a different state within the same type of object. For example, if you take water as an element, with different temperature and pressure levels one can achieve a few different phases. It can go from an ice cube to water to then a gaseous state. The Triple Point is the combination of temperature and pressure that permits the coexistence of the three phases in dynamic equilibrium. A phase transition, (or phase change) is the transformation of a thermodynamic system from one phase to another. At the critical point (in the phase transition) the system is undergoing its change.

(Source: armstrongeconomics.com)

Today, many experts explain market conditions through basic principles derived from phase transition. In the ‘market’ the price of an instrument is determined by supply and demand, in its most basic understanding, or buy and sell orders. For example, when demand rises for a security the price will generally rise as a result. In the market, there are essentially three phases that the market can transition through: wait, buy, and sell. These are determined by the demand/supply ratio of trading orders. At times a net demand of zero may exist, which is when the market enters the ‘wait’ phase. Therefore, when a positive net demand exists in the market, it is said to be apart of the ‘buy’ phase, which can be thought of as a gas state as there is a pressure on the price from below (having it rise). Additionally, when a negative net demand exists, this is considered part of the ‘sell’ phase in the market, or a liquid phase, as there is pressure on the price from above. Traders (investors) use analytical and critical skills of information available in the market, to determine orders to buy and/or sell securities. Hence, on a systematic level, the balance of these buy/sell orders determines the phase of the market at hand. At the critical point in the phase transition, the market essentially crashes as trade orders become mostly selling ones.

Applications of Econophysics In The Financial Arena

This principle of phase transition is studied by econophysicists and other scientists today using back-tests of historical empirical evidence found in the market. Today, many of what we know about the financial markets can be attributed to this field of study. For example, they have been able to further elaborate on the causes on financial crashes, and relate them not only to financial and economic principles, but as well to dynamical principles. Additionally, in collaboration with economists, they have been able to develop more realistic models of how the market functions. For example, in the mid-90s, researchers at the Santa Fe Institute explained how fat-tailed dynamics can interact with the market and actually arise naturally. This type of model and others have greatly been able to influence financial policy for both the private and public sector. The European Commission has used these types of models to influence their decision making with regard to various financial regulations and laws, such as the idea of a financial transactions tax.

For decades many esteemed economists have discussed the risks and negative externalities on the economy of high leverage techniques used by wall street, i.e. Leverage Buyouts (LBOs). The idea being that as companies take on higher levels of debt, their default rate becomes higher as well. Hence, any finance students can explain that firms should seek to maintain an appropriate level of debt/equity ratio. This continues to be a highlighting critique on the financial industry in its excessive use of debts and financial engineering. Physicists have helped clarify market instability. In that, many thought that derivatives and various risky financial instruments, when share in uniformity, can help make individual firms and banks safer. However, Econophysics found that risk sharing can actually decrease stability in the market. Additionally, today, it has become increasingly harder to determine the risks taken on by financial institutions, as they operate differently than the basic management type company. Econophysicists were able to develop a network measure called ‘Debtrank’, to better measure risks undertaken. It is able to cut through the vast networks of the financial institutions and reveal the true nature of the risks. For example, in the 2008 financial crisis many had underestimated in the impact of Lehman Brothers becoming bankrupts, as most could not comprehend risks of many of their products, such as derivatives, and they were interconnected on a macro level.

(Source: IOP Science)

I Know First’s Self-Learning Predictive Algorithm

I Know First has a state of the art algorithm, that uses Artificial Intelligence and self-learning capabilities to predict asset price movements in the market today. The algorithm as well incorporates models and concepts derived from phase transition. The concept of phase transition occurs mainly in conditions of market instability, which is analogous to supersaturation in physics. When one paradigm has been exhausted, and the conditions are ripe for a change, then any small perturbation in the market input, such as the interest rate change, or social event (presidential elections) can bring about a large paradigm shift. In contrast, the stable market is characterized by a smooth input-output relationship. The I Know First algorithm is measuring the current state of the market, and is capable of detecting the potential shift in paradigms. In a stable market if one modifies the input to the algorithm by a small amount, the output should change by a correspondingly small amount. On the other hand, when the market is unstable, a discontinuity occurs, a small change in the input could result in a drastically different output. Hence, many analysts especially econophysicists explain the financial crash of 1987 using phase transition.

The I Know First algorithm takes many different inputs to create an output, and the relationship between input and output is highly non-linear. To test the present conditions for instability one would normally need to modify each of the inputs by a small amount. The huge number of different permutations of these inputs would take impossibly long time to probe. But we also know that not all inputs are equal. Some are more important than others by the orders of magnitude. Also, we know the inter-relationship between these important inputs, which allows us to cut the probing experiments to manageable numbers. Thus by modifying these in a controlled fashion we can measure the market stability.

I Know First’s Competitive Advantages

There is still another way, which is already built into the I Know First forecast. It consists of two values, the signal and the predictability. The latter is a measure of the forecast performance in the recent past. By monitoring the performance of each stock and of the aggregate of stocks over time one can judge the stability of the market. The signal is an indicator which represents the predicted movement direction/trend; not a percentage or specific target price. The signal strength indicates how much the current price deviates from what the system considers an equilibrium or “fair” price. The predictability is a separate indicator that is obtained by calculating the correlation between the prediction and the actual asset movement for each discrete time period. The algorithm then averages the results of all the prediction points, while giving more weight to recent performance. As the machine keeps learning, the predictability values generally increase, but then stabilize. By monitoring the predictability over time, one can detect the potential paradigm shift. This is a part of the competitive advantage offered by I Know First, allowing investors to more successfully differentiate themselves in the market.