Framework for Approaching the Market

Systematic Thinking

This is a brief, straight to the point write up on a framework for how I aim to think about the financial markets - a model.

Last year, I shared a primer on developing quantitative signals in trading. This research layers on it and takes it up one more level, you could read this then refer back to the article and try to apply some of the identified framework into it:

The financial market is a ecosystem with its participants, purpose, limits, constraints and of course multiple asset class. Before delving into an asset class or the market as a whole, it is important to understand both the microstructure and macrostructure of the asset. While this article doesn’t focus on that, we would examine a broad based thinking framework that can be applied in understanding the system.

The financial market is a complex system.

Complex Systems

Complex systems are often referred to as “wholes that are more than the sum of their parts,” wholes whose behavior cannot be understood without looking at the individual components and how they interact -Waterloo Institute for Complexity & Innovation

The financial market is a complex system, to play in it, I think it is paramount to understand the structure of the system, to quantify it, build models around it with the primary goal of making decisions on how the system will behave in reaction to changes in internal and external variables.

In order to approach a complex system we need to start with the basics which include:

• Identifying what the system is, more clearly its function or purpose

• Apply reductionism to identify all the parts that make up the system - Elements

• Understanding how the elements are interconnected

• Understanding the feedback loops and time delays that takes place within them

Essentially, what I try to do with macro data and price action is somewhat encapsulated within the basics listed above, but more importantly understanding how they are interconnected is what provides a relative edge through which the flow of information impacts the prices of assets. Additionally, understanding the information flow will aid us in making decision and taking actions within the system.

Overall, when working with the data across the elements the key task is to estimate what the goal of the system possibly is, because that is often the most crucial determinant of the system’s behavior. However, the financial system is dynamic, not static, such that how it behaved 5 years ago might not be the exact same way it would 10 years later, due to changes in the interconnection of the elements of the system. The changes should not be a problem if we adequately evaluate the flow of information and what it possibly implies for the system behavior.

Foundation of a System

Beyond identifying the basic components that make up the system, there are four concept that are of key importance in analyzing a system:

• Stock: are elements of the system that can be counted or measured at a given time, and it holds the memory of the history of changing flows over time.

• Flows: are the in-flow and out-flow into the stocks that causes the stock level to rise and fall over time.

• Time: aids in analyzing the behavior of the system over time as it gyrates towards it goal or limit. It is important because it help us contextualize what happened up to the the point and what might happen next

• Feedback Loops

Feedback Loops

Feedback loops emanates as a byproduct of the effect from the changes in stock level which also impact flows, which also impacts the stock level. Feedback loops could be:

• Balanced

• Reinforcing

• Goal Seeking Balanced Loop.

A balanced feedback loops works in such a manner that the stock variable are kept at certain levels or within a limit, such that if you push the stock variable too high(low), it will counteract and push it back lower(higher).

A reinforcing loop occurs when a system grows as a result of the growth in other variables within it, while Goal Seeking Balanced loop occurs when the stock variable competes with balancing its goals. E.g at the core price of assets move up/down to seek liquidity or balance inefficiencies, these are two goals, such that if liquidity has been sought, then the balance is to seek for an inefficiency and vice-versa.

Analyzing a System

The first step is to understand the system structure, the interlocking stocks, the flows and feedback loops, and quantifying the data across each elements of the system, then proceeding to ask some basic questions to aid in figuring the direction of the system:

• What are the driving factors

• What is driving the driving factors

• How are the driving factors likely to unfold

• How will the system react to the factors:

  • Growth

  • Stagnation

  • Decline

  • Oscillation

  • Randomness and Evolution

A case in point is the S&P500 what are the driving factors? Macro variables(Growth, Inflation, Liquidity and Policy), Equity specific variables (Earnings, Valuation, Corporate Actions…etc.) - this isn’t a comprehensive list but from these you can apply the basic questions above and then estimate how the system in this case, the S&P500 will react to changes in the driving factors and the probable pathway of the driving factors and how that reflects in the price of the asset.

Time is also a critical variable, such that there could be period where you lack clarity on the behavior of the system, and the simple answer is to calm down and wait for clarity.

Furthermore, in identifying the elements of a complex system, there are numerous variables/elements that you can put together to picture the structure of the system, while this is good it simply also implies that as you add more variable so does complexity increase, therefore what is really key is identifying the variables that are limiting to the system.

The limiting variables are such that without them, the system won’t function and these helps us construct our choice of boundaries to the components of the system. However, what is also more important is the ability to be flexible in other to identify when the limiting variable changes.

The goal is to be bounded by rationality when approaching the system, which includes having a comprehensive information flow, understanding of the incentives/disincentives, the goal of the system, triggering events, the stress and constraints that arises.

In summary, to approach a complex system:

• Identify the parameters of the system

• Identify the buffers in the system

• Map out the plumbing structure of the Stock and Flows variable, and their node of intersection

• Identify the delays in feedback process and the time it takes

• Identify what keeps the system balanced or constrained, the reinforcing factors in play

• Build a solid information structure of the system

• Identify the rules of the system, boundaries and degree of freedom

• Identify the goal of the system

• Self-organization of sub-systems to the larger system

Overall when putting this together to formulate a model of how you intend to approach the system, it is important to optimize the information flow and quantify them, while also paying attention to what is important even if everything is quantifiable.

Lastly, I think it is important to emphasize that the Map is not the Territory. While focusing on limiting factors and building models around what is key, it is important to not lose sight of the fact that while we are creating boundaries on the system, it is not the real system, we must be dynamic enough to know when/if we need to expand our model to include other components of the system to aid in our decision making, but at first, having a simple model is a good place to start then to build upon it.

Stay humble, keep learning.