The intrinsic value approximation

Companies have monetary value. Sometimes we call this value market capitalization, other times we call it intrinsic value.

But is there a difference between market capitalization and intrinsic value?

If market capitalization is what you get by taking the stock price and multiplying this with number of shares outstanding, then market capitalization is the value that the market currently puts on a company.

Intrinsic value on the other hand is the value you get by using fundamental analysis and apply the discounted present value formula we looked at previously. We argued however that there’s a lot of uncertainty when people do their fundamental analysis because there are many unknowns.

But certainly there exists a true intrinsic value. It’s just that no one really truly know what this value is, because a company is a complicated asset. It’s a hidden variable.

Imagine the above bubble chart show the true, but hidden, intrinsic value of a selection of public stock market companies. These are some of the biggest companies in the world. Every day, every hour, every minute, their true hidden intrinsic value changes, because they are active entities interacting with the world. And the world interacts with them.

The purpose of a stock market is to facilitate the exchange of securities between buyers and sellers. These exchanges provide real-time trading information, facilitating price discovery.

It is this price discovery process we believe iterates towards the true hidden intrinsic value, and we believe this is a process that contains many forms of behavioral bias.

Imagine the below chart show the true, hidden, intrinsic value of a company. The white line, illustrating the true intrinsic value, represents one of the bubbles above. Due to various forms of interactions with its environment the true value will jump up and down. For a real and active company this would jump up and down very frequently, but we’re slowing the process down to help illustrate how we view the markets reaction to this.

Two types of reactions are shown, illustrated with red and blue. These are meant to show the current market capitalization, as observed in the stock market.

The blue line show a subdued reaction, where supply and demand dynamics gradually iterate towards the true hidden intrinsic value. This is an iterative and not an instant process because of the gradual reaction to new information. It does not overreact because it shows a case where investors are remaining rational towards the target. There is sufficient information, understanding and not enough hype for investors to overreact. But, as said, it is an iterative process because humans need time to absorb, distill, and execute on such information.

The red line show a more excited reaction, where elements like mania, delayed feedback, crowding, etc, causes the market to overreact (in either direction). While it is still iterating towards the true value, it will overshoot the target. In extreme cases we can observe this as stock market bubbles, which always eventually regain sanity to then finally approach the true intrinsic value. We assume this can happen if there’s a lack of understanding, unrealistic expectations around future growth, or extreme pessimism on the lower end. It can also happen simply due to positive feedback loops, where self-interactions causes this.

We believe both of these types of reactions exist. Which type of reaction that currently dominate depend on the company, sector or general market in question, the overall mood or state of mind and many other factors. A particular company’s market capitalization will show both of these forms of behaviors at different periods, shifting between them as things progress.

At AgoraOpus we work on identifying these states of the market, and use this to build tools that help you spot interesting investment opportunities, a topic we’ll explore more in the next blog post.

This blog post was written by Christian, the main portfolio curator here at AgoraOpus. With a background from FinTech, he holds a MSc in Quantitative Finance and a BSc in Computer Science and Industrial Automation.

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