It's Only a Point
Chance favors those who have a persistent curiosity about many things coupled with an energetic willingness to experiment and explore.
I have spent the last couple of months going down the Generative AI rabbit hole. It is the shiny new toy that has captivated everyone’s imagination. Everybody is talking about it, and some are even using it productively. New models are being released at an increasing cadence. Just this week, Anthropic announced Claude 3.5 Sonnet, which is cheaper and more capable than the previous top-of-the-line Claude 3 Opus. Just days before this, nVidia displaced Microsoft as the largest public company, with a staggering $3.3 trillion market capitalization. Meanwhile, companies left and right are racing to integrate AI into their workflows. The promise is huge, and as it inevitably happens, no amount of success can live up to the ever growing expectations. I won’t put money on it (yet), but all the signs that nVidia has peaked are there.
The two threads of finance and technology have been weaving patterns throughout my life. I have been analytical ever since I can remember. I would consider all sorts of hypotheticals ever since I was kid. Eventually, I learned to use computers and data to perform statistical analysis, which has since been rebranded as data science. This happened around the time I was majoring in Finance, and became really interested in how companies operate and eventually how they are valued. The organization of people and resources to manifest what we collectively desire, where that desire is transmitted by open market transactions, was infinitely fascinating to me. I was just discovering the power of imaginary constructs, such as "company", "market", "money". I still remember the moment I figured out how the fiat system works and the concept of credit as money.
Logically, this line of discovery led me to investing. After dabbling in trading signals, I soon figured out it was not my thing. Luckily, I discovered Benjamin Graham shortly after. He provided a much more statistically sound approach to investing. The concepts of investing for the long term, the market as a voting and weighing machine, intrinsic value and the margin of safety around it resonated with me and my way of thinking. I liked the solid statistic foundation set by Graham. Eventually, I started to appreciate the artistic touches Buffett, influenced by Munger, added to Graham's scientific approach.
This brings us to today. There is some disagreement over whether the Ben Graham approach to investing still works in this day of (almost) freely available information and analytics tools. There is just no comparison between this and the inefficiency of Buffett discovering obscure companies in Moody's Manuals, taking a train ride or spending hours in the library to get some obscure piece of financial information. Don't get me wrong. There will always be market inefficiencies. But they change. Today's inefficiencies are not the ones that were available to Graham or Buffett. When everyone willing, and there are plenty of willing and capable people in investing, has access to the same quantitative data, we are no longer exploiting information inefficiencies. We are looking for "variant perceptions" - different takes on the same data points that everyone is looking at. This depends on one's unique understanding of the topic/company at hand. The main question becomes, “What does the market know that just isn’t so?”
As in competitive sports, the level is higher today than in the past. The little edges that existed before have been eroded. The par for the course keeps going down.
Two recent talks by the CFA Institute - Unstructured Data and AI in Investments and The Automation Ahead - give a good overview of this shift. Structured financial data and the ratios based on it are just the starting point. Today, we are able to distill unstructured data and automate ever more tasks related to discovery, analysis, and writing. GPT models are another step in better encoding the combined public knowledge and using it to gain insight into specific topics. As Brian says, ultimately it is the curiosity to understand the world better that will give us to that edge. And you build this by doing case study after case study, valuing a company after company, internalizing the feedback, and improving your process incrementally.
In summary, we have new tools - internet, plentiful data, cutting-edge analytics tools, and now the power to interrogate whole knowledge bases in natural language - that can help us with the boring, low value-adding parts of our jobs. But as they become ubiquitous, they will not offer an edge. It will be still up to you to assess the relevant inputs, make the decisions, and live with the consequences.
Investing is a game of chance. You are doing good if you are right only slightly more often than you are wrong on a money-weighted basis. So focus on the point in play only.
“Perfection is impossible. In the 1526 singles matches I played in my career, I won almost 80% of those matches. Now, I have a question for you.
What percentage of points do you think I won in those matches? Only 54%.
In other words, even top-ranked tennis players win barely more than half of the points they play. When you lose every second point on average, you learn not to dwell on every shot.
You teach yourself to think, okay, I double-faulted ... it's only a point. Okay, I came to the net, then I got passed again; it's only a point. Even a great shot, an overhead backhand smash that ends up on ESPN's top 10 playlist. That, too, is just a point.
And here's why I'm telling you this. When you're playing a point, it has to be the most important thing in the world, and it is. But when it's behind you, It's behind you. This mindset is really crucial because it frees you to fully commit to the next point and the next point after that, with intensity, clarity, and focus.
You want to become a master at overcoming hard moments. That is, to me, the sign of a champion. The best in the world are not the best because they win every point. It's because they lose again and again and have learned how to deal with it. You accept it. Cry it out if you need to and force a smile.”
— Roger Federer