The Essence of Investing: Information, Surprise, and Quantification

I wanted to discuss a fascinating concept that lies at the heart of investing—information and its connection to surprise and quantification. By understanding these principles, we can better navigate the intricate patterns of the investment world. Allow me to elaborate on this matter.

In the realm of investing, our primary goal is to identify patterns that reduce uncertainty, enabling us to acquire valuable insights using relatively limited information. The key lies in incorporating causality through fundamental reasoning based on proven knowledge of patterns. This approach helps us distinguish genuine patterns from illusory ones, effectively disqualifying false positives.

Consider the transmission of information about the valuation of a company in the near future, approximately two years from now. How many “Yes” or “No” responses would it take to convey this information accurately? While certain patterns might allow us to achieve this with a handful of questions, most cases require a multitude of inquiries, making accurate predictions highly challenging.

A profound insight by Claude Shannon relates information to surprise. In the context of investing, new information becomes valuable when it surprises the market or investors. The more certainty we have about the content of a message, the fewer “Yes” or “No” questions we need, on average, to determine it.

To illustrate this further, let’s explore an analogy. Imagine two versions of an alphabet game. In the first version, I randomly select a letter from the English alphabet, and your task is to guess it. By utilizing the optimal guessing strategy, it would take an average of 4.7 questions to identify the letter correctly. An effective initial question would be, “Is the letter in the first half of the alphabet?”

Now, let’s shift to the second version of the game, where you aim to guess letters in actual English words. Here, you can tailor your approach by capitalizing on the fact that certain letters appear more frequently than others (“Is it a vowel?”), and knowing the value of one letter aids in guessing the next (q is almost always followed by u). Shannon calculated that the entropy of the English language is 2.62 bits per letter, significantly less than the 4.7 bits required if each letter appeared randomly. In other words, patterns reduce uncertainty, allowing us to convey a substantial amount of information using relatively little data.

One of the most valuable types of information in investing is changes in a company’s prospects. This encompasses factors such as new competition entering the market, shifts in consumer demand due to obsolescence or changing preferences, or disruptive technologies that could impact the company’s future performance.

Let’s consider an example involving coin flips. In the first scenario, we have a trick coin with heads on both sides, providing complete certainty that both flips will result in heads. Here, no additional information is required. In the second scenario, we flip a regular coin, resulting in heads on one side and tails on the other. By employing binary code (0 for heads, 1 for tails), we can communicate the outcome effectively. There are four possible messages: 00, 11, 01, and 10, each requiring two bits of information.

In the first scenario, complete certainty eliminates the need for any information. However, in the second scenario, where the chance of guessing the right answer is 1-in-4 (25% certainty), the message requires two bits of information to resolve the ambiguity. As a general principle, the less we know about the message’s content, the more information it takes to convey.

Shannon later developed the concept of Shannon Entropy, which represents the minimum number of bits required, on average, to communicate a message. It quantifies the number of “Yes” or “No” questions needed to ascertain the message’s content. Let’s consider a situation where two weather stations—one in San Diego and the other in St. Louis—wish to exchange seven-day forecasts. Due to San Diego’s consistently sunny weather, we have high confidence regarding the forecast’s content. In this case, a profitable first question could be, “Are all seven days of the forecast sunny?” If the answer is yes, we have determined the entire forecast with a single question. However, with St. Louis, where weather patterns are more uncertain, we would need to inquire about each day’s forecast individually.

In conclusion, the relationship between information, surprise, and investing is a captivating subject. By recognizing patterns, incorporating causality, and quantifying information, we can better navigate the investment landscape and make informed decisions. Investing often involves balancing uncertainty and information, where surprises hold great value.

Thank you for your time and attention. If you have any further questions or would like to discuss this topic in more detail, please don’t hesitate to reach out. I look forward to our continued engagement.

Sincerely,

Manny Singh,

Co-Founder & Portfolio Manager

Chakkal Capital Management, LLC | Website: ChakkalGroup.com

Series 65 Licensed | LinkedIn: https://www.linkedin.com/in/singhmanny/

Tel: 206.261.5956 | Email: manny@chakkalgroup.com