Summary: As quants, we’re all aware that every model has a shelf-life… a similar pattern applies to the world of data. Rare, unique and proprietary data eventually diffuses and becomes commonplace, easily available, edgeless data. The best analysts constantly reinvent their models, to avoid their inevitable obsolescence. Today, they’re venturing into the world of alternative data as a new source of alpha.[…]
After recently having completed testing on a general method of discounted cash flow (DCF) analysis for estimating a broad basket of stocks’ intrinsic values, I became more concerned with “quality”. While DCFs remain the foundation of any sound business valuation, I discovered they are highly sensitive to the assumptions and data used. Slightly changing a minute detail can drastically influence the result causing an attractive investment to all of sudden seem not so attractive and vice versa. While relative valuation methods were a natural alternative (Wall Street’s preferred choice, in fact) to circumvent the sensitivity issues, I was inclined to believe that an ability to define robust ‘quality factors’ would complement the ideological purity of the discounted cash flow approach much better. The purpose of this discussion is to demonstrate that a good company can indeed also be a good investment. Continue reading →
In this post, I present a case that alpha can be gleaned from publicly available SEC Form 13-F data. Traditionally, pundits looked at commonalities in institutional top-holdings by dollar amount. Research suggests that these aggregated top holdings among many institutions can be indicative of their “best ideas” (1). That may be all good and well, but common sense indicates that the best leads should come from a good institution’s top holdings per unit of capacity. For my institution, I use RenTec because:
a.) they are quantitative and therefore it may be easier to find commonalities in their holdings; and,
b.) they have consistently delivered exceptional returns.
I believe that their “best ideas” should be those positions in which the position size is largest relative to capacity because a moderately-sized holding for a small float stock is much more indicative of expected risk-reward than a relatively much larger position in a relatively much larger float stock. Additionally, focusing on a single institution (rather than many) allows us to ask the all-important “why” by determining if there are any commonalities in their top holdings. Understanding the “why” might us allow us to move beyond “piggybacking” off of quarterly 13-F data, and understand what drives the decisions of the best in the industry. I argue that if we can deconstruct some of the decision-making criteria, we can use this for finding our own unique source of alpha.
I used to think of myself as contrarian, but after completing my most recent research project, “Grading the Gurus” (which I presented at the last MBIIM) , I now realize that I only appear to be a contrarian. I’ll spare you the discourse on how I came to that for now, but I’ll make sure to include it in my closing remarks.
For those who were unable to join us last Sunday, I thought I would summarize the presentation and the following discussion. Without further ado…
INTRODUCTION TO ‘THE PROBLEM’
Like my previous post says, “I have often wondered if it makes any sense to pay attention to investing gurus.” And there certainly are a lot of them. Most of which seem to promise you that they’ve found the “secret” to easy money, whether that be a method of valuing companies or assessing the market’s future direction. However, evidence suggests otherwise as it has been proven that 85% of mutual funds have underperformed “dumb” index funds over the last 40 years. This means that all those fancy folks that went to fancy schools and wear fancy neckties are not as smart as “passive” investors. Therein lies the problem…
Sunday, August 4, 2013
10:00 AM to Cafe Lumiere
365 Calle Principal, Monterey, CA
Grading the Gurus I have often wondered if it makes any sense to pay attention to investing gurus. I’m talking about the greats; the legends; the Warren Buffets; The Benjamin Grahams; those with real followings, real track records, and, most importantly, real philosophies. In brief, I want to ask questions and query data in such a way that will help people place the “usual suspects” into one of three groups: