A systematic approach
to deep value investing
Two core beliefs guide our investment philosophy:
That investors can profit by identifying undervalued stocks;
That it is possible to identify undervalued stocks without meticulously studying a company for hours, days, weeks, or months on end.
These beliefs reflect the essence of our quantamental approach, which combines a fundamentals-driven investment orientation with modern “quant” investing tools.
Hunting for value
A central premise of value investing is that investors can profit by identifying mispriced stocks [6]. If all stock market prices perfectly reflected stocks’ intrinsic values, it stands to reason that no such profit opportunities would exist [7].
To this end, academic and practioner studies have identified numerous measures now commonly used to assess whether a particular stock is a good “value”: market-to-book ratios, price-to-earnings ratios, et cetera, and, to date, there is a body of research supporting the use of these measures to predict stock returns [8]. Standing in isolation, however, these measures may not reflect a particular view about why exactly the market may systematically misprice stocks [9].
In contrast, our investment methodology reflects a view on two specific, but pervasive, mispricing mechanisms that we think cause stock prices to deviate from their intrinsic values: first, the market’s mis-estimation of the probability that a company will experience financial stress at some point in the future; and second, the market’s failure to properly account for the interrelations between investment, growth, and returns across industries. Stoney Point’s valuation methodology attempts to capitalize on both of these possible errors.
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While we think our method can improve the valuation of any particular stock, we think our approach is especially powerful in a portfolio context, where an investor can avail himself/herself of the benefits of diversification.
We make no bones about the fact that we don’t pour over earnings call transcripts or the footnotes in companies’ financial disclosures to conduct the sort of bottoms-up analysis that many financial analysts are inclined to perform. In our experience, these types of analyses require considerable time and resources to do well, and even when done well, time and resource (and cognitive) constraints make it impossible to perform such analyses on a very large number of stocks.
In general, we think these traditional approaches have a significant flaw: they provide no insight about all of the other stocks out there — the hundreds or even thousands of other stocks that weren’t considered, much less analyzed. For example, even if a detailed, bottoms-up analysis reveals that a particular stock is undervalued by an estimated 10 percent, the investor has no basis to conclude whether or not that 10 percent is a “good” return compared to other stocks at that point in time.
In contrast, we apply our valuation indicators to a large universe of stocks, ranking each stock in the universe according to how mispriced we estimate it to be currently, and selecting a sample of only the top-ranked stocks for our monthly picks. Our investment process envisions investing in these top-ranked stocks, and then re-ranking every stock in the universe monthly and re-balancing the portfolio accordingly.
Put simply, instead of following a handful of companies “closely” like traditional stock pickers, we aim to use our models to follows hundreds of companies intentionally loosely.
At any given point in time, some of our picks may be “down” quite a bit, while others may be “up” quite a bit. Our aim is for the “ups” to be up more than the “downs” are down, on average, relative to the risk assumed, resulting in market-beating performance.
Given the intuitive appeal of these “quantitative”, evidence-based, low-maintenance approaches to investing, the recent “breakneck growth” of the industry—including a near doubling of assets managed by quant hedge funds from 2010-2017 to nearly $1 trillion, as reported by the Financial Times—does not surprise us [10].