Monetizing knowledge

Do you know XTX or Citadel? If your answer is no, and you are in the financial market, something is wrong. Their traders can earn half a million dollars annually. Many of their traders know nothing about the market; however, they look for people with high cognitive abilities. In other words, they are not seeking vital diversity, gender diversity, or any other criterion beyond outstanding cognitive skills. They do not hire randomly, they do not leave the cognitive level of their traders to chance, and they ensure that they are exceptional (at least potentially exceptional).

Their traders have exceptional analytical skills.

These types of companies aim to exploit intelligence and generate profits. For those of us familiar with the financial world, it is no surprise that trading is the most plausible place to do this.

We can set leverage aside, at least on our part, because it is clear that trading is where it is best used, but it is not the only place in the financial market where it exists. Think of banks, which have very high leverage ratios. For example, in Chile the leverage ratio is 3%, meaning that out of 100 assets, capital must be able to cover 3%.

The reason may be another, which we will attempt to explain.

In financial markets, when you trade, you give up an asset that matters less to you than the one you are acquiring. But to reach this nature of financial markets, we first passed through barter (swap for financiers) before it ended with the birth of money. And with the birth of money itself, which allows us to order our preferences and make them highly legible to others.

Then, to reach financial markets, we also overcame specialization, which demands trading with many distant and unknown counterparts. The way to overcome specialization is through homogenization, which allows us to dispense with reputational networks via standardization, since there are widely known, specific, verifiable, and easily measurable variables.

In some way, we managed to overcome the “how to pay and what to receive” problem. Legally, this is solved with price (money), on one hand, and with the obligation to receive at least something of average quality (in commerce law, the buyer has the right to receive at least regular quality if the contract stipulates the seller must deliver the purchased item), on the other hand; and financially, this is solved when things that are not money (a good, whether specific or generic) become similar to money (fungibility).

This makes explicit that we do not seek goods and services as preferences themselves, but rather we seek our preferences through goods and services. The brokerage firm does not want to buy your share; it wants to buy your share in order to later sell it, collecting commissions in the meantime, along with the spread between buying and selling.

Obviously, the market is founded on this, since there will never be a person who can perfectly hold the inverse preferences of the counterparty. It is difficult to find someone who wants to sell a specific share, at a specific price, at a specific moment. The market does that work for you.

The financial asset, like money, seeks to satisfy common preferences of a group of investors. These preferences, moreover, have an abstraction (through standardization) that allows them to scale. Greater volume and participation in the market means greater probability that any specific preference will find someone trading on the other side. When there is scale and market depth, we can satisfy our preferences. Interestingly, new preferences emerge from these scales. Agents begin to have preferences about these different scales and exclude certain markets once they reach a certain scale. This comes with heightened sensitivity to contingencies.

Here we arrive at reinforcement. Scales always need abstractions (which allow standardization) to properly manage preferences, understanding that physical goods are extremely difficult to trade without friction. Abstraction allows further scaling, facilitating trade among strangers. In other words, the larger the market, the greater the scale, and with it abstraction, which allows us to trade more efficiently with anonymous parties. The financial market represents the final point of this reasoning, since the absence of transaction costs is precisely what a regulated financial market provides. The market then scales according to the size of underlying preferences.

Now comes our favorite part at MIV. It is crucial to recognize that this abstraction inevitably creates risk. When we simplify the world, we also create risks. The good news is that these risks can be quantified. When we buy assets, companies, or businesses, we are buying problems—or rather, probabilities of risks materializing negatively. But in financial markets we have the advantage of leverage. Risk, in fact, enables leverage.

Risk can be evaluated statistically, without case-by-case analysis (we believe this is one factor that often makes law collide with finance: casuistry vs. statistics). We can evaluate risks systematically and decide to invest instantly.

An example is the following. Nvidia represents a standardized affirmation of the world (as some procedural law professor might say), through public information, price history, and risk calculation as a company. In contrast, the stall selling Chilean sopaipillas on Pío Nono street in Santiago is a unique set of risks tied to its location, management, customer base, and operations. The difference in this example is that the Pío Nono stall cannot be evaluated by an algorithm, unlike Nvidia.

With Nvidia we can quickly approach risk, even applying leverage to manage it. In the case of the sopaipilla stall, it is not possible to calculate risk quickly, since it is not standardized, abstracted, or scaled.

In financial markets, at the highest level of abstraction, everything is ultimately reflected in price. In other businesses, outside finance, such as the sopaipilla stall, value is created through other channels, such as customer service, and then you attempt to capture part of that value as profit.

In the real economy you build valuable things, with profit as the measure of success. In contrast, the financial market eliminates all of that, leaving only the precision of price. In finance, the objective and the means merge into maximizing profit. There are no intermediate stages, such as maximizing customer service or product quality to seek profit; instead, value is sought directly to extract profit.

That is why it is unfounded to say that financial markets do not create value, because hitting the right price (through fundamental analysis, trading, or quantitative methods) is creating value.

Hitting the right price is a cognitive exercise, without separate implementation phases, because the market is designed to execute our insights instantly.

That is why those with superior cognitive abilities achieve great profits in financial markets. If value is reduced to better thinking or ideas, compensation quickly reflects that relationship.

In part, the above serves to justify MIV Finance. Our purpose is to monetize thought, but now through research. We believe the best ideas and thoughts can improve others, and we are the bridge.

Disclosure: Analysts hold both long and short positions in Nvidia.