In fact, solving those other problems before solving the real one will only make things worse. And after being burned firms will take two steps back after attempting that first step forward. God knows there are big firms out there that have built big teams and bought a lot of data only to have the firm at large claim it all useless. That happens because no one did the really hard work to rethink the core investment process from the ground up, including these things from the beginning. I say this to the detriment of my own ability to quickly sell more Estimize data to these discretionary firms which certainly could benefit from it.
Eventually, these guys are going to get fed up and leave their firms. What quant wants to deal with interpersonal, social, and process issues all day to have any of their work mean anything? I normally start these meetings asking them to explain how their investment process takes place, who makes decisions, and on what time frames. The senior management went on to explain that they had 17 analysts across four main sectors.
Four PMs shared the analysts. When I asked them how stock selection took place, they all kind of looked at each other like no one wanted to answer the question there was a PM sitting in the room.
At this point everyone around the table got it and they began asking me questions about the percentage of firms I had met with that were attempting the transformation at different levels. It was slightly awkward. To my surprise, the guys in that room no women? But they certainly understood how difficult it would be to make this shift from the perspective of institutional inertia.
It is not enough for executives to want something to happen in our industry, the PMs who truly wield the power must be on board as well, and this is a threat not only to their ego, but if done correctly, their position within the firm. Most in-house software built to support pieces of this process is super shitty. I would sincerely advise firms to bend away from attempting to build software meant for use by nonquantitative individuals. And if you believe that your edge will be in building this glorious piece of software that no other fund has, well, good luck with that.
All teams need to know what their north star is. What are the variables, time frames, market caps and sectors that you believe your team has an advantage in analyzing better than everyone else?
Almost all funds lie about turnover and how often they trade, or why they trade. Frankly, I find it interesting how poor most firms are at elucidating this set of constraints given that LPs like to bucket their investment in funds this way and look at the exposure to these variables. Any fund looking to market well should have a good grasp on this. The important aspect of seriously sitting down to lay out your focus here is that many of the next steps are dependent on your bent here. The same type of people who will successfully run a momentum book will fail miserably at picking value stocks.
Developing Differentiated Forward Looking Views. At its most basic level, fundamental investing is a very simple algorithm. Stock XYZ which is part of your universe currently trades at 5X trailing 12 month revenue. Revenue is the variable most causally related and correlated to the trailing one year performance of the stock.
You believe that if the company hits that mark the multiple will go from 5X trailing to 6X trailing for various reasons. Your alpha is the difference in the valuation the market expects and what you expect, you can either be right about the revenue, the multiple, or both.
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But there are a few huge issues around that. First off, they almost exclusively reside within disparate Excel models. Yes macros exist, but in my humble opinion anything being run via a macro in Excel should probably be a piece of software externally. These estimates should be collected centrally so that they can be processed centrally. There are a small handful of firms doing this, none well. Just think for a second how many crazy heuristics and outside variables are associated with this being how ideas bubble up to the person who puts things in the book.
The fights between analysts to get their ideas into the book, the back stabbing, the bro-ing out with your PM to become better friends. Who thought any of this was a good idea?! There is no reason we should be measuring analysts subjectively in dumb ways like how well they get along with the PM. Yes you need to be able to talk through how you got there and your confidence intervals, but man, that should be tertiary at best in terms of the totem poll of analyst skill sets. Give me Michael Burry over the hedge fund bro who can talk your ear off any day.
How do analysts develop differentiated views? As I said previously, firms are mostly doing this whole thing backward. But once they get this process in place, this is where a lot of the company level data fits in. It should inform the analyst as to what their beliefs of the forward earnings, revenue, EBITDA, same store sales, monthly active users, ARPU, or whatever other variable is important to that company. And then it should also inform their views on the multiple. It is really at the analyst level that a lot of the company specific information should be used.
But at the end of the day, it needs to be used to inform a structured estimate of future expectations and placed into the rest of the process for stock selection just like any other. They themselves made a recommendation to buy the stock to the PM.
The PM looked at it, and went the other way for whatever reason and shorted the stock. Analysts are required to make a full forward year of quarterly estimates for EPS, Revenue, and the 2—3 key performance indicators specific to their firm same store sales, bookings, iPhones, etc. Analysts will put an expected multiple on the aggregated full year of estimates they make to imply a price target.
Stock selection obviously is not just about structured forward looking estimates. There are a range of other variables that have to be taken into consideration, variables associated with risk factors that have to be accounted for, or catalysts that could affect the fundamentals or the multiple of the stock.
So we need to capture them. There are a few decent software tools for teams to capture this type of information and share it, and firms have done a much better job at this task than the structured estimate side. Factset acquired Code Red a little while back, Advent acquired Tamale from my friend John Fawcett who now runs the great Quantopian platform.
The problem with both products is the nature of the input being vastly unstructured.
Good luck getting anyone to systematically review the notes the analysts input. And if you think getting analysts to make structured estimates on regular time frames is difficult, try convincing them to put notes in there regularly. Analysts need to pick the 10 or so unstructured variables that they believe will affect the stock performance on a regular basis and give structured sentiment for them.
There are two uses for this. Our analysts are going to fill out these unstructured surveys at the same interval they make their estimates, or at any point in time between. But before we get to that, I want to talk a little bit about the other piece of the puzzle which we will later merge with the former. A factor model, for those not initiated, is basically a Z score of some variable across the stocks in your universe. Some factors can represent betas value, momentum, growth and some can be true alphas like the ones we provide to clients at Estimize post earnings drift, pre earnings consensus trend, historical surprise.
Alpha generating factor models are developed by quants using the scientific method. We start with a hypothesis, usually focus on a specific data set, for why variable A would be causally related and correlated to the outcome in the price of a set of stocks. For example, maybe we believe that the tone of the words the CEO uses on the earnings call is associated with six month stock performance. In either case, our goal is to basically rank the sentiment from the calls from best to worst.
Once we have our score we take the first half of the time series of our factor score data set and look at the correlation between scores and stock movement. Jim Cathcart, author of Relationship Selling, the eight competencies of top sales producers. I have been studying and teaching consultative sales and leadership for more than 20 years, and this book does a masterful job of weaving the two topics together in both an instructive and impactful way. I've read a lot of sales books, at least or more, and this will now be one of the top ones I recommend to all of my clients.
Top sellers turn failures into opportunities, obstacles into challenges, and possibilities into realities. Read it to make extraordinary sales happen. This book clearly outlines an authentic, natural and proven way to hit your number, delight your buyers and create long-term mutually-successful business relationships.
What a thrill to see them apply these proven leadership principles to professional selling!