It used to be that making money in stocks was relatively easier. Father of value investing, Benjamin Graham, was known for his predilection of “cigar butt” stocks. That is, stocks which the market has thrown to the curb, forgotten, but which still might have at least “a few puffs” left in them. Harvest Natural Resources (NYSE: HNR), a small-cap oil and gas exploration and production (E&P) outfit focused on the development of known hydrocarbon deposits worldwide, may prove to be a classic example of such a stock.
With a market cap of $74.4 MM, and an indicated net current asset value (NCAV) very near that, Harvest Natural Resources, as a company, does not appear to be dead money. On the contrary, following an imminent sale of its Gabon asset for $32 MM, the company’s cash and receivable balance, less total liabilities, will exceed its market capitalization by $25 MM, implying that the market assigns a negative value to its expected future cash hoard. Worst case, investors could expect to get nearly all of their money back even after a 30% tax on a special dividend. However, if management can figure out a tax-advantaged means by which to utilize that sum, investors could benefit. A tax-free return of capital, which might be allowed under US tax law, implies a 33% short-term, low-risk return.
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.[…]
In the world of investing and corporate finance, the Efficient Market Hypothesis (EMH) casts a long shadow. EMH states that a sufficiently liquid market reflects the “correct” price at all times. Since efficient markets factor in all known and relevant information at all times, it is therefore practically futile to attempt to predict the future direction of market prices. In other words, a blindfolded monkey throwing darts at the Wall Street Journal has about the same chance as beating the market averages as any professional investor. At one extreme, the founder of Vanguard Investments Jack Bogle revolutionized the mutual fund industry around cheap indexing, which he posited as the solution to efficient markets. At the other, Warren Buffet’s seminal essay, The Super-Investors of Graham and Doddes-ville, defends the notion that right-headed investors can carve out a significant edge [1. The Super-Investors of Graham and Doddes-ville]. In the middle, you have the greater majority of investors who will likely cede that both extremes contain some amount of the truth. Even 2013 Nobel Laureate Eugene Fama, of the University of Chicago Booth School of Business, who is credited with developing EMH, has stated that “[asset prices] are typically right and wrong about half the time” [2. The Super-Brainy Quote]. Being able to determine when they are right and when they are wrong is the holy grail to traders and investors alike. In order to investigate how correctly assets prices reflect all known information, we must develop an intuition and methodology for estimating the fair value of an asset. As we will discuss, just because a methodology is descriptive does not mean it is predictive (i.e., correlation does not imply causation). Continue reading →
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 →
On Monday, 23 September 2013, “Kingold Jewelry (NASDAQ:KGJI) was downgraded by analysts at Thomson Reuters/Verus from a buy rating to a hold rating”, according to Zolmax News. The shares closed at $1.73 on Thursday 26 September, up 25% on the day, and up 40.6% from Monday’s close of $1.23.
These kinds of anomalies are pretty rare; living proof of the fallacy of instantaneous market efficiency. But as a wise woman once said, contradictions do not truly exist; “Whenever you think you are facing a contradiction, check your premises. You will find that one of them is wrong” (AR).
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: