Quantitative Deep Value (Net Nets)

Quantitative Deep Value is an investment process that seeks to identify those "Net Nets" (stocks trading at a discount to their net current asset value) that provide the greatest reward relative to the risk they present. To make a distinction between the "average" Net Net versus those that we are able to identify through the application of our extensive, evidenced-based process we dubbed our strategy "Quantitative Deep Value".

While we have documented every step of the Quantitative Deep Value process in detail for our investors, at this time, we present below only a limited summary, broken down as follows: the underlying philosophy, an introduction to the strategy, a schema detailing how our process is constructed and a disclosure of the majority of research utilized to build and validate that process.

Notwithstanding our application of the Quantitative Deep Value/Net Net investment strategy, in our Research section, we examine the evidence pertaining to Net Net investing with the objective of determining its reliability and robustness - confirmation bias is dangerous and we can think of no better way of warding it off than to forensically investigate the evidence underpinning the strategy itself.

The Quantitative Deep Value Investment Philosophy

“High performance; founded on fundamentals, extracted from behavioural bias."

Benjamin Graham, who made widely known the idea of purchasing stocks at a discount to their “intrinsic value” back in the 1930’s, is known today as the father of value investing. Since Graham’s time, academic research has shown that low price to fundamentals (i.e. “value”) stocks have historically outperformed the market. In the investing world, Graham’s most famous student, Warren Buffett, has inspired legions of investors to adopt the value philosophy. Despite widespread knowledge that value investing generates higher returns over the long-haul, value-based strategies have continued to beat the market. How is this possible? The answer relates to a fundamental truth: human beings behave irrationally. We follow an evolutionary mindset that focuses on surviving in the jungle, not optimizing our portfolio. While we will never eliminate our survival instincts, we can minimize their impact by employing quantitative tools.

“Quantitative,” is often considered to be an opaque mathematical black art, practised only by Ivory Tower academics and supercomputers. However, quantitative, or systematic, processes are merely tools that value investors can use to minimize the impact of their misleading “survival” instincts when investing. Quantitative tools serve two purposes: 1) protect us from our own behavioural errors, and 2) exploit the behavioural errors of others. These tools do not need to be complex, but they do need to be systematic. The research overwhelming demonstrates that simple, systematic processes outperform human “experts.”[i] The inability of human beings to robustly outperform simple systematic processes also holds true for investing, just as it holds true for most other fields.[ii]

Much of the analysis conducted by value investors—reading financial statements, interpreting past trends, and assessing relative valuations—can be done faster, more effectively, and across a wider swath of securities via a systematic process. Investors often argue that instinct and experience add value in the stock-selection process, but the evidence doesn’t support this interpretation. Rather, the evidence suggests that when investors respond to non-quantitative signals (e.g., the latest headlines, their expert friend’s opinion, etc.), they unconsciously introduce cognitive biases into their investment process. These biases, in turn, lead to predictable underperformance. The Quantitative Deep Value Investing Philosophy is best suited for value investors who can acknowledge their own fallibility. Granted, the approach is not infallible, and should always be questioned; however, it seeks to provide a systematic, evidence-based, value-focused investment strategy that is built to beat behavioural bias.[iii]

In accepting our fallibility, we select only stocks from our initial universe that meet our long proven, purely quantitative valuation metric. Thereafter, we utilize additional quantitative tools to both, avoid those firms that have a greater probability of permanently destroying capital and to seek out those firms that are of the highest quality. Only when quantitative tools are unavailable do we utilise qualitative analysis to improve the probability of a positive outcome.

Introduction to “Net Nets”

What resulted from our research is an evidence-based approach to systematic value investing. We spent several years researching the evidence to ensure a smooth transition from theory to practice. Our specific interpretation and implementation of the Quantitative Deep Value Investment Philosophy, which was created by Benjamin Graham (Buffett’s early mentor and friend) is colloquially known as “Net Net” investing. It is described as such because the market capitalization of the firms one invests in under the approach is “net” of the “net” current asset value. To be more specific, under the approach, one calculates the Net Current Asset Value (NCAV) of the prospective investment by taking only the current assets of the prospective investment (e.g. cash, receivables, inventory) and deducting all the liabilities (and preferred shares)—i.e., NCAV = Current Assets – Liabilities. Net Current Asset Value is meant to serve as a conservative approximation for liquidation value since it considers only assets that can be readily converted into cash (as opposed to long-term assets like buildings and equipment) but considers all liabilities.

When strictly applying the Net Net approach, one then considers the company for investment only if it is trading for less than 2/3 of the NCAV. In essence, with net-net investing, you seek to purchase dollars that are trading for 67 cents (or less).

It should be noted that companies trading at such an extreme valuation tend to be concentrated amongst the very smallest in any given market i.e. micro capitalization stocks. The ramification of the Net Net approach being largely confined to investments in “microcaps” is that the strategy does not possess “large scale” (i.e. the ability to deploy hundreds of millions of dollars into it). It is a strategy available to those managing relatively small sums.

In “The Intelligent Investor”, Benjamin Graham commented on the strategy:

“It always seemed, and still seems, ridiculously simple to say that if one can acquire a

diversified group of common stocks at a price less than the applicable net current assets

alone — after deducting all prior claims, and counting as zero the fixed and other assets

— the results should be quite satisfactory.”[iv]

Invariably, companies that trade at these extreme valuations are unattractive businesses. However, a bad business doesn’t necessarily make a bad investment, and a good business doesn’t necessarily make a good investment. As the adage goes, “Price is what you pay, value is what you get.”

The Quantitative Deep Value (Net Net) Investment Process

In the end, we distilled our entire process into five core steps:

References (related to above text):

[i] Painting by Numbers: An Ode to Quant, https://alphaarchitect.com/wp-content/uploads/2013/01/Painting-by-the-Numbers.pdf

[ii] Grove, W., Zald, D., Lebow, B., and B. Nelson, 2000, “Clinical Versus Mechanical Prediction: A Meta-Analysis,” Psychological Assessment 12, p. 19-30.

[iii] The Quantitative Value Investing Philosophy, https://alphaarchitect.com/2014/10/07/the-quantitative-value-investing-philosophy/

[iv] The Intelligent Investor, A Book of Practical Counsel, Revised Edition, Benjamin Graham, Updated with New Commentary by Jason Zweig, Chapter 15

Additional references:

Investing Lessons from the Reigning Jeopardy Champ, https://awealthofcommonsense.com/2019/05/investing-lessons-from-the-reigning-jeopardy-champ/

What Has Worked In Investing, p. 3., https://www.tweedy.com/resources/library_docs/papers/WhatHasWorkedFundOct14Web.pdf

Barry Ritholtz’s Masters in Business: Aswath Damodaran Interview, https://soundcloud.com/bloombergview/barry-ritholtzs-masters-in-business-aswath-damodaran-interview

i3 Podcast Ep 22: Rich Pzena, https://marketfox.org/2019/04/03/i3-podcast-ep-22-rich-pzena/

Security Analysis, Principles and Technique, Benjamin Graham and David I. Dodd Associate Professor of Finance Columbia University, Sixth Edition p. 541.

An Analysis of “Benjamin Graham’s Net Current Asset Values: A Performance Update”, https://www.globalinvestinginsight.com/post/an-analysis-of-benjamin-graham-s-net-current-asset-values-a-performance-update

What Has Worked In Investing, Tweedy, Browne Company LLC, 2009, Table 5, pg 7, https://www.tweedy.com/resources/library_docs/papers/WhatHasWorkedFundOct14Web.pdf

Shareholder Yield: A Better Approach to Dividend Investing, Meb Faber, May 12, 2013

Leveraged Small Value Equities, Chingono, Rasmussen, 2015, https://papers.ssrn.com/sol3/Papers.cfm?abstract_id=2639647

Forecasting Debt Paydown Among Leveraged Equities: Analysis of US Stocks from 1964-2012, Chingono, Rasmussen, 2016, https://papers.ssrn.com/sol3/Papers.cfm?abstract_id=2776355

The Predictable Cost of Earnings Manipulation, Messod Daniel Beneish, Craig Nichols, 2007, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1006840

Ekkehart Boehmer, Zsuzsa R. Huszar, and Bradford D. Jordan, “The Good News in Short Interest,” May 15, 2009 , https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1405511

Oppenheimer, H. R. 1986. Ben Graham’s net current asset values: a performance update. Financial Analysts Journal 42, 40 – 47. https://www.valuewalk.com/wp-content/uploads/2015/03/Ben-Graham-Net-Current-Asset-Values-A-Performance-Update.pdf

Ben Graham's Net Nets: Seventy-Five Years Old and Outperforming, Carlisle, Mohanty, Oxman, 2010, https://www.valuewalk.com/wp-content/uploads/2014/07/benjamin-grahams-net-nets-seventy-five-years-old-and-outperforming-full-tables1.pdf

Daniel Giamouridis, Manolis Liodakis, and Andrew Moniz, “Some Insiders Are Indeed Smart Investors,” July 29, 2008. Available at http://ssrn.com/abstract=1160305

Lauren Cohen, Christopher Malloy, and Lukasz Pomorski, “Decoding Insider Information.” 2010, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1692517

Contrarian Investment, New Share Issues and Repurchases, Bali, Demirtas, Hovakimian, 2006, https://papers.ssrn.com/sol3/Papers.cfm?abstract_id=934782

Joseph D. Piotroski, “Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers.” https://papers.ssrn.com/sol3/papers.cfm?abstract_id=249455.

Value Investing Research: Simple Methods to Improve the Piotroski F-Score, Wesley Gray, PhD, https://alphaarchitect.com/2015/05/05/value-investing-research-simple-methods-to-improve-the-piotroski-f-score/

Elton, E. and Martin Gruber, 1977, Risk Reduction and Portfolio Size: An Analytical Solution, The Journal of Business 50, p 415-437.

V. DeMiguel, L. Garlappi, and R. Uppal, “Optimal Versus Naïve Diversification: How Inefficient is the 1/N Portfolio Strategy?” Review of Financial Studies 22, no. 5 (2009): 1915–1953.

Disclaimer

We are not registered investment advisors. This material has been provided for informational purposes only and should not be considered as investment advice or a recommendation of any particular security, strategy or investment product. Information contained herein has been obtained from sources believed to be reliable, but not guaranteed.