TIP775: Why Your Valuation Metrics Might Be Lying to You w/ Kyle Grieve

**SPEAKER_1** (0:00)
You're listening to TIP.

**Kyle Grieve** (0:03)
Today's episode is a deep dive into some of the most compelling insights from Michael Mauboussin's work from writing The Consilient Observer. For those who might not be familiar, Mauboussin is a highly respected mind in investing, known for his rigorous and innovative approach to decision making, forecasting, capital allocation and many other essential investing concepts. Today, I'll be breaking down key concepts from several of his Consilient Observer articles, focusing on how investors can improve their judgment, reduce noise, improve their decision making, challenge commonly held investing myths and a lot more. Some of the biggest key takeaways include things such as how to use the BIN framework to combat forecasting errors. We'll explore how bias, information and noise distort our thinking, and a few simple tools to strengthen your decision making and just cut through the noise. We'll look at how we can utilize checklists and algorithms to improve systemic decision making. We'll examine why traditional valuation metrics like price to earnings, metrics and EV to EBITDA ratios are not as helpful as they once were. These evaluation metrics have been go-to tools for investors, but some key concepts need to be considered when comparing businesses. We'll look at why short-termism, dividends, money-losing businesses, and the rise of indexes might not exactly be what they seem. Mauboussin does a fantastic job explaining some of these market myths. We'll go over the rare nature of a business's success and how investors can position themselves accordingly. We'll look at some of the enduring traits of long-term winners to hopefully help you spot them in the future. I'll also examine how investors can avoid common pitfalls by focusing on capital efficiency, votes, and the right qualitative and quantitative data points rather than just following surface level metrics. If you're serious about improving your investing game and want to improve your thinking process by limiting biases, you won't want to miss this one. Now, let's jump right into this week's episode.

**SPEAKER_1** (1:54)
Since 2014 and through more than 180 million downloads, we've studied the financial markets and read the books that influence self-made billionaires the most. We keep you informed and prepared for the unexpected. Now for your host, Kyle Grieve.

**Kyle Grieve** (2:18)
Welcome to the Investors Podcast. I'm your host, Kyle Grieve. And today, I'm coming to you solo, discussing a series of fascinating investing concepts that were highlighted in a few of Michael Mauboussin's articles from The Consilient Observer. Now, I've been reading Michael J. Mauboussin articles for a few years now, and every time he drops a new one, I feel like my mind is just being blown. So today, we're gonna cover a few of my favorite ideas from a few handpicked articles that he's written over the years. Now, I also wanna mention that nearly all of his articles are co-authored by Dan Callahan. But for the sake of simplicity, I'll be referring only to Michael Mauboussin in this episode. I'll get links to each article in the show notes as well, if you wanna dive further into any of these ideas. So the first one I wanna cover is based on a simple acronym that he created that helps us think better by reducing the sources of forecasting errors. The acronym here is gonna be BIN, B-I-N. So B stands for bias, I for information and N for noise. As the article states, noise is the most important factor of these three. So I'm gonna go ahead and cover that one first. So he actually has a calculation for noise, which is interesting because I never even thought that that was possible. But what that calculation does is basically shows the difference between the number of a sample divided by the average. So just give you a quick example here. Let's say we have two accountants and they're both looking at someone's tax return. And let's say they both come up with a different number for how much that person owes in taxes. So let's say one of them comes up with $10,000 in taxes and the other one says they owe $14,000 in taxes. So noise would be $14,000 minus $10,000, which comes to $4,000. And we divide that by the average, which is $12,000. So in this example, the noise index produces a number around 33%.
So his first point on noise is that many, many of the judgments made by professionals are highly, highly variable. So he writes about an example where kind of piggybacking on this whole tax return thing, they actually reach out to 50 different accountants and ask them to calculate the taxes of a family of four with an income of about $132,000. Now the answers here varied quite heavily from $10,000 to $21,000. The noise index on this was over 20%, which is considered quite bad. So kind of the number that you're generally looking for that's normal is 10%. Now, how does this matter to investing?