Information: quality or quantity?
The information we consume defines our thoughts. Our thoughts define our actions, and our actions define our achievements.
We are taught in school that the more information we have, the better. Attention is paid only to quantity, while quality is disregarded.
Financial markets require knowledge in many fields, which means we retrieve tons of information. The question is what information we use, where we get it, and how we interpret it.
In today's short article, I discuss the properties of information. Knowing them gives us a substantial analytical edge whenever we work in an environment full of uncertainty and constant change.
Information 101
Uncertainty and change describe the economy and financial markets precisely. As investors, we always make decisions about the unknown future using partial information. Knowing the properties of information transforms you from a passive consumer into a proactive analyst.
The Path of Information: from data to wisdom
Information has a path. It begins its journey as data, which, in isolation, has little meaning. But placed in context, it becomes information.
The next step is to transform this information into knowledge. This means synthesizing conclusions that can be applied to solve problems.
And the final stage is to turn this knowledge into wisdom. This happens when we experiment with our conclusions by decoding real-life puzzles. We apply them to solve practical problems, and the feedback we receive is wisdom.
The path of information: from data to hypothesis
Every human activity possessing the following characteristics requires a structured analytical process:
Processing a large quantity and diverse quality of information
Building hypotheses about the future with plausible accuracy
Actions taken based on derived hypotheses have severe material consequences.
Working in a constant environment of uncertainty
I just described the activities of analysts in intelligence agencies, macro investors, and military strategists. In other words, those professionals deal with an (in)finite number of (un)known variables in an environment of constant uncertainty.
What they have in common is that the analytical process follows specific steps:
Filtering raw data - reports, databases, charts, presentations
Sorting data by topic - macro, fundamentals, price action, narrative
Arranging the sorted data - at this stage, data becomes information.
Formulating conclusions for each subtopic - macro, fundamentals, price, narrative
Deriving a hypothesis is a synthesis of the conclusions of the individual topics.
The described steps are mandatory for any complex analysis aimed at delivering a plausible hypothesis. The mandatory steps are illustrated in the graph below:
Unknown Unknowns
The only certain thing is uncertainty, and this is true for financial markets and life. We have the terrible habit of ignoring because we do not understand it. Thus, we are blind to our blindness.
The infinite number of interconnections and reflexivity of the system makes it impossible to understand fully. So, if we base our decisions on rigid market assumptions, we will reach a deceptively strong conviction that leads to wrong choices.
We humans always strive to simplify problems and seek instant answers. This sometimes works but does not work for complex systems like the markets. The following equation guarantees us troubles:
Troubles = Unknown future + human desire for simple answers
We continuously operate in an environment of uncertainty, and therefore, we must use the following matrix to guide us in collecting and processing information:
KNOWN – KNOWN
KNOWN – UNKNOWN
UNKNOWN – KNOWN
UNKNOWN – UNKNOWN
In financial markets, we reside in all quadrants, with an emphasis on the last two. That being said, our thesis does not matter how well it is argued because it is always incomplete.
Structure of information: noise and signal
Before becoming information, raw data has two components – noise and signal. This leads us to the following maxim: garbage in, garbage out. If we put more noise into our decision-making system, we get more noise as an output because errors accumulate nonlinearly in complex systems.
Where we gather data for our research is the first step to filtering out the noise. Knowing where to look is just as important as what to look for.
Noise cannot be reduced to zero but can be minimized by carefully selecting sources. The closer we are to the original source, the lower the noise level.
The number of intermediaries between us and the source directly affects the quality. Each additional step adds some amount of data interpretation, which leads to distortion.
This discussion leads me to the next property of information.
First weed, then seed
The analytical process works with data, whose quality inevitably affects its result. The inability to filter the input data will significantly worsen the quality of the formulated hypotheses.
This dependency is not linear. Each incoming error tends to amplify at every subsequent stage of the process. At first glance, a small amount of noise can completely drown out the signal when it reaches the output.
Filtering incoming data is a mandatory part of the entire process. Successfully rejecting unnecessary data is a decisive factor in predetermining the quality of the final hypothesis.
Structure of information: spheres of knowledge
Human civilization has reached its current level of development by constantly experimenting with our surrounding reality. As a result, information accumulates, which is classified into different spheres of knowledge.
Every science is important, but some are essential. They are classified and arranged hierarchically—at the base are the sciences that describe our civilization and the world around us. They are the foundational principles upon which we build the models we use in practice.
Theory and practice cannot exist without each other. The former makes assumptions that we experiment with solving real-life problems. Ultimately, the feedback we receive helps us correct the initial assumptions. However, without theory, we are unable to analyze the feedback. So, we need both theory and practice, as we need two legs.
For a better illustration of the spheres of knowledge, see the following chart:
We start from the foundation and then move to more practical layers of knowledge—management, infrastructure, and business. Finally, we have current trends. As we approach the periphery, the current trend signal-to-noise ratio drastically dissipates.
The following metaphor is appropriate: if all knowledge is a tree, theoretical sciences are the tree’s trunk, and the practical fields of knowledge are its branches. Finally, the leaves, which change yearly - the current trends.
Information opportunity cost
Consuming information has an opportunity cost, and the two most expensive mistakes we can make are how we spend our time and the information we absorb.
Neglecting the quality of information costs us dearly—compound interest can be a formidable foe. If we consume low-quality information, we fall into information inflation.
In short, you cannot watch another episode of a TV show and read Seneca at the same time. You choose only one and ultimately suffer the consequences.
The Paradox of Abundance
In the digital age, we constantly generate and devour mind-boggling amounts of information, leading us to the paradox of abundance. The average quality of information is extremely low, but we have extremely valuable information waiting to be explored at the two ends of the distribution curve.
Most people consume low-quality information because it is much easier. But for a small conscious part of us, we live in the best era for knowledge seekers. Now, we can access highly specialized information in a matter of seconds.
Information diminishing returns
Following the "the more, the better" principle is dangerous when working with data and information. Analysts seek the minimum but sufficient amount of information to formulate a plausible hypothesis.
At some point, accumulating data with the conviction to improve the entire process leads to reverse results. The next new bit does not carry value. Instead, it can even lower the existing value of our thesis.
Accumulating more data does not lead to more clarity and understanding. Simply put, more information is not equal to higher-quality decisions. The number of data is a quantitative measure, and understanding is a qualitative one. An actionable analysis gives us a better understanding, not more facts.
Final Thoughts
How we spend our time and the information we consume, define who we are. Knowing and applying the principles described will make us proactive and conscious consumers. Once we adopt that mentality, the information will work for us, not vice versa.
Information is the raw material of our analytical process. To learn more about my framework, look at the following articles: