I discovered Benjamin Graham back in 2004.
I was on a business trip to Boston, attending an engineering conference on Electromagnetic Compatibility. In one of the evenings, after the conference had ended, I was strolling the streets of downtown Boston and decided to spend some time at the landmark three-story Barnes & Noble bookstore. Not long after, I laid my eyes on The Intelligent Investor, by Benjamin Graham, with commentary by Jason Zweig. Back then, I did not know who Graham was nor how he would change my life. Yet the title was nice. The Intelligent Investor. Hmm…I’m an investor (wannabe, back then in 2004), I consider myself intelligent, so what the heck, I’ll just buy it and find out what this guy has to say.
I started reading it on the very same day…and was immediately hooked.
Naturally, I purchased Security Analysis in the following week, when I was back at home in Israel. Kindle was yet to be invented back in the day, so I had the book shipped to me from the United States, at a higher cost than the price of the book.
And that is how I discovered Net-nets.
At first, it seemed such an easy way to make hoards of money. Almost too good to be true. All one has to do is simply search for the companies whose market price is lower than a fraction of their Net Current Asset Value, NCAV. Once you find them, invest in a handful of them, and forget about them for a year, or until they have appreciated by at least 50%.
Over the years, as my second-level thinking evolved, I became more skeptical. I figured, well, this might have been a good way to invest back in the ‘30s, ‘40s, ‘50s, maybe ‘60s. Back then, Ben Graham was the exception. Most investors were looking for dividend-paying blue chips, and the practice of security analysis was not as widespread as today. These days, computers can identify those net-nets in milliseconds. There couldn’t be any edge in those simplistic calculations. Hence the outperformance potential is probably gone. The market must be efficient to some extent, I thought. Not all the time, and not every market. But surely, the return potential that Graham described has faded or comes with a hidden cost.
Well, was I correct in assuming that?
Is there still value in net-nets?
Let’s find out.
In this article, I shall review the research on the topic, as well as describe the new original research that I had performed, especially for this newsletter edition. I will give you the necessary data to decide for yourself if net-nets fit your temperament, investment style, and investment goals. Let’s dive in.
What Are Net-nets, Anyway?
Let us start with a definition of Net-nets. Net-net Investing is a value investing technique developed by Benjamin Graham that seeks to invest in companies whose value is lower than their liquidation value. A net-net stock is a stock trading below of its estimated liquidation value.
An investor in net-net stocks examines the balance sheet to ascertain a conservative value of the value of its assets at liquidation, net of all liabilities. If a stock is selling at a market capitalization significantly lower (say 30% lower) than a conservative estimate of the value of its assets in liquidation, net of all liabilities, than a purchase is considered. In such cases, either liquidation or an improvement in the underlying business conditions may drive up the stock price. Nevertheless, profiting from such situations is not guaranteed, as a company’s losses may accrue faster than it would be able or willing to execute a liquidation or turnaround its operations.
A mechanical approximation of the liquidation value is NCAV or Net Current Asset Value.
NCAV = Current Assets minus – (all liabilities + preferred stocks).
NCAV is merely a quick approximation of the liquidation value. It assumes that in a liquidation, the company is worth at least its current assets, which includes its cash in the bank, its trade receivables, its inventory worth, minus all its liabilities.
The approximation is quick and easy to calculate, but inaccurate by design. Typically, companies undergoing a liquidation are unable to recover their full book value of trade receivables and inventory, and typically do have some fixed assets that they can sell and monetize. Every company situation is unique. A more accurate liquidation value requires a good understanding of the company’s assets and a careful analysis. NCAV is thus a rough approximation that saves considerable time and, on average, is not very far off. The use of NCAV as a rough approximation is probably the reason why Ben Graham insisted on a margin of safety, looking for stocks trading at no more than ⅔ of NCAV, and diversified broadly.
Graham and Dodd wrote in “Security Analysis (p. 564):
“When a common stock sells persistently below its liquidating value, then either the price is too low or the company should be liquidated”
“Stocks selling below liquidation value are in many cases too cheap and so offer an attractive medium for purchase. We have thus a profitable field here for the technique of security analysis. But in many cases also the fact that an issue sells below liquidating value is a signal that mistaken policies are being followed and that therefore the management should take corrective action—if not voluntarily, then under pressure from the stockholders. Let us consider these two lines of inquiry in order.”
When a stock becomes so cheap that it is selling below its approximation of liquidating value, there can be many favorable outcomes, but only one undesirable outcome. On the favorable side, the company can liquidate, in which process it will realize at least the amount of its market capitalization, and probably more. In practice, only a small percentage of net-nets end up liquidating. There are other potential favorable outcomes. The company may appear on activists’ radar, which may take a controlling stake in it and pursue a turnaround or M&A to right the ship. Even if not taken over by an activist, the board of directors may replace the management team with a more competent one, pursue the sale of the company, or take other strategic actions. The guiding principle is that when a stock reaches such cheap levels, in most cases (but not all), huge forces come into play and apply pressure on improving the operations or the financial standing of a company. If those measures bear fruit, the stock price will appreciate considerably.
However, there is always a chance that the measures taken and improvement attempts fail, and the company will continue bleeding cash while dissipating its value.
How Can An Investor Profit From The Net-Net Phenomenon?
Graham and Dodd’s answer was – Diversification and Discrimination.
On discrimination, Graham and Dodd write:
“There is scarcely any doubt that common stocks selling well below liquidating value represent on the whole a class of undervalued securities. They have declined in price more severely than the actual conditions justify. This must mean that on the whole, these stocks afford profitable opportunities for purchase. Nevertheless, the securities analyst should exercise as much discrimination as possible in the choice of issues falling within this category. He will lean toward those for which he sees a fairly imminent prospect of some one of the favorable developments listed above. Or else he will be partial to such as reveal other attractive statistical features There is scarcely any doubt that common stocks selling well below liquidating value represent on the whole a class of undervalued securities. They have declined in price more severely than the actual conditions justify. This must mean that on the whole, these stocks afford profitable opportunities for purchase. Nevertheless, the securities analyst should exercise as much discrimination as possible in the choice of issues falling within this category. He will lean toward those for which he sees a fairly imminent prospect of some one of the favorable developments listed above. Or else
he will be partial to such as reveal other attractive statistical features besides their liquid-asset position, e.g., satisfactory current earnings and dividends or a high average earning power in the past. The analyst will avoid issues that have been losing their current assets at a rapid rate and show no definite signs of ceasing to do so.”
Applying such discrimination through the definition of mechanical rules, such as the ones used in quantitative investing strategies is possible, but due to the low number of net-net stocks available for trading, such an attempt is not practical.
Investing in net-net stocks is thus a semi-quantitative semi-manual endeavor. A quantitative screen can be used to identify potential securities. Then, an investor should manually select the stocks to include in his or her portfolio. These selection criteria may either be mechanical or discretionary, depending on the investor’s temperament and experience.
Later in this article, I present the performance of U.S.-traded net-net stocks during the last 20 years odd.
But before that, I would like to briefly review the top research on the subject of net-nets.
The Top Research On Net-Nets
One of the most famous papers on net-nets is that of Oppenheimer, H. R. (1986) titled Ben Graham’s Net Current Asset Values: A Performance Update. Oppenheimer tested the performance of net-nets for the period of 1970-1982. He recorded NCAV and market cap on November 30th and had built portfolios starting on the last day of December of each year. He recorded the 12-month and 30-months performance of those portfolios. Unfortunately, Oppenheimer’s study has limited practical value. Most net-nets are so illiquid that no investor, even a retail investor with the tiniest of portfolios, can invest in. In contrast, our study brought herein will take a more practical approach and examine the performance of investable stocks (from a liquidity perspective). Moreover, his choice of investing in stocks on December 31st, based on 1-month old prices, may be consistent from an academic perspective, but that’s not how people invest in reality.
Oppenheimer summarizes his results as follows:
“During the 1970-83 period, the mean monthly return of NAV portfolios was 2.45 percent. By contrast, the mean monthly returns for the NYSE-AMEX and small firm indexes were 0.96 and 1.75 percent, respectively. To put these results in a form more meaningful to an investor, $10,000 invested in the NAV portfolio on December 31, 1970, would have grown to $254,973 (with monthly compounding) by December 31, 1983. The comparable figures for the NYSE-AMEX and small-firm indexes are $37,296 and $101,992, respectively.”
In 2010, three researchers, our friend Tobias Carlisle, along with S. Mohanyu and J. Oxman from the University of St. Thomas, extended Oppenheimer’s original research to the years 1984 – 2008. Their paper is titled Ben Graham’s Net-Nets: Seventy-Five Years Old and Outperforming. Unfortunately, they had used the same methodology of starting portfolios with one-month-old data and had not imposed any restriction on illiquidity.
Here are their results:
“The mean monthly return on stocks meeting the NCAV rule in the period we examined, December 31, 1983, to December 31, 2008, was 2.55%. The mean monthly returns for the NYSE-AMEX and Small – Firm indices were 0.85% and 1.24% respectively. This indicates an outperformance by the NCAV portfolio over the NYSE-AMEX Index of 1.70% per month, or 22.42% per annum and an outperformance over the Small – Firm Index of 1.31 % per month, or 16.90% per annum.
In total, Oppenheimer and Carlisle et al. results sum up to a CAGR of 35% over 38 years. Nevertheless, the question remains – can individual investors replicate such a performance?
There are two things to be said on the results of both Oppenheimer and Carlisle et al. First, they present an outperformance which was statistically significant even after factoring the effect of small-cap stocks (called SMB by academics, Small Minus Big), and even after factoring the value effect (a.k.a HML, High Minus Low Book to Market). At least from an academic perspective, net-nets are a well-researched technique for outperforming the benchmarks, even more than 75 years after their introduction by Graham and Dodd. Secondly, the papers do not discuss volatility with ample rigor, in the author’s opinion. They provide Beta figures, but Beta is not an adequate measure of volatility, rather – a certain kind of volatility, one that is correlative to the market’s performance. We believe that the standard deviation on monthly stock returns is a more telling measure of volatility, indicating the thrust and thrills that a real investor suffers while invested.
While net-nets were shown to perform well even more than 75 years after their introduction, their number was significantly reduced over the years.
Graham and Dodd indicated (p. 563):
“It seems to us that the most distinctive feature of the stock market of those three years was the large proportion of issues which sold below their liquidating value. Our computations indicate that over 40% of all the industrial companies listed on the New York Stock Exchange were quoted at some time in 1932 at less than their net current assets. A considerable number actually sold for less than their cash-asset value…In the recession of 1937–1938 this situation was repeated on a smaller scale. Available data indicate that 20.5% of the industrial companies listed on the New York Stock Exchange sold in early 1938 at less than their net-current-asset value.
Carlisle et al. table show a much-reduced count of net-net stocks:
Note that while the comparison is indicative, it is not exactly apples-to-apples. Carlisle et al.’s NCAV rule limits selections to those trading at ⅔ of the NCAV, while Graham & Dodd referred to stocks trading at lower prices than their net current assets, without imposing a required margin.
My Findings: Are Net-Nets Too Good To Be True?
Using the portfolio123 platform, I created a universe of net-nets with the following characteristics:
- Not trading in China, due to the high probability of fraud in Chinese stocks
- Financials, REIT, and Biotech, are excluded, as their accounting is different and does not fit the net-net formula.
- Positive Common Equity, to exclude companies with non-orthodox accounting.
- Delinquent companies that did not file a 10K/10Q for more than six months are excluded.
- Companies with less than ten employees and companies with zero sales are excluded, to avoid skeletons and bogus entities.
- Liquidity: Market cap > $10M, price > 20 cents; average daily volume of at least $1000 a day.
- Valuation: Market Cap lower than 75% of NCAV.
The rules are described below:
Here are the performance results from every year during 2000-2018, starting on June 30th, to June 30th the following year:
The green rows show years in which net-net outperformed the S&P 500 by a substantial amount, typically by more than 20%. The orange rows show years when net-nets significantly underperformed the S&P 500.
Several interesting insights emerge:
- The number of net-nets – There are not enough net-nets to be able to build a diversified portfolio from net-nets alone. The average number of net-nets meeting my criteria was 23.4, but during most of the years, the count was below 20, even single-digit for many of the years. After a big drop, the number of net-nets typically increases. In June 2002, following the dot-com meltdown, there were 83 net-nets meeting my criteria to be found. Following the crash of 2007-2009, in June 2009, there were 60 net-nets. Other periods resulted in much lower figures.
- Net-nets outperform the S&P 500, on average. But they are so volatile and so non-correlative to the market that I doubt if there’s any investor who would be willing to allocate a significant amount of his or her capital to net-nets. Would you enjoy average temperatures of 25 degrees centigrade? Most people would. But what if I told you that the mild 25 degrees centigrade is the average of 0 degrees during half of the year and 50 degrees during the second half? (my apologies to my North American readers who now have to do the conversions to Fahrenheit). Similarly with net-nets, in one year you earn an average of 90% while the market made 14%, and in another year you are down 21%, while the market is up 8%.
- No pattern emerges for outperformance. Not one that I could find, at least. Net-nets did well following the 2000-2002 crash and the 2007-2009 crash, but why did they do so poorly during 2013-2016, when the market direction was generally up? And why did they resurge during 2016-2018? Those questions remain open.
- While the average returns are good, at 17.16% per year, my other quant strategies perform just as well, and even better. Quantitative Value and Quantitative Momentum, each show average yearly returns exceeding 18%. Deep Value portfolios are close with up to 17%, but with lower volatility.
Next, I modified my rules a bit to see if returns could be further improved, and volatility could be further reduced.
In the following table, the valuation rule was changed to MV/NCAV < 1, meaning that the market cap should be equal to or lower than NCAV.
As you can see, the results have not improved. While volatility was reduced from 30% to 23% (a significant reduction), average returns also declined from 17% to little over 15%. There was no significant change in the crazy performance profile.
Next, I tried to tighten the liquidity requirement. I now required a daily average volume of $20,000 and stocks with a price of 90 cents or above, vs. the 20 cents threshold in the previous experiments.
The returns were reduced even more, while the volatility stayed a course.
In the next experiments, I tried to assess liquidation value differently. Rather than using NCAV, I used the following measure of Adjusted Net Asset Value:
Adjusted Net Assets = Cash & Equivalents + 0.75*Receivables + 0.5*Inventory + 0.5*Fixed_Assets + 0.5*Other_Investments – all liabilities – preferred stocks
This formula has two advantages over using NCAV. First, it more accurately estimates the liquidation value, yet still enables mechanical calculation using an algorithm. Secondly, it is less stringent and thus results in a higher stocks count.
The following table shows the results of stocks trading at 75% of Adjusted Net Assets or lower, with the original liquidity thresholds of 20 cents per share minimum, and $1,000 minimum average daily volume.
Worst results yet. Average returns are a mere 11%, and volatility is above 41%.
I then relaxed the valuation rule to have stocks trading at or below Adjusted Net Assets:
And, finally, I demanded higher liquidity by raising the requirements to $20,000 average daily volume and a minimum stock price of 90 cents.
The last experiment (series 6) resulted in the best results. Average yearly returns were 28.54%, a phenomenal figure. Volatility, as measured by the standard deviations of monthly returns, is still excessive, though, at 36%. The performance profile is still crazy and unpredictable. In 2011-2012, had you been investing in all 15 net-nets, you would have made 237% vs. 6.28% for the S&P 500, while during 2014-2015, investing in the 12 net-nets would have resulted in a loss of almost a third of your invested capital, while the market was up by 8%. Had you been able to time the market bottoms of 2003 and 2009 correctly, investing in net-nets would have made you over 40% per year.
Finally, I tried to devise a practical quantitative portfolio based on the rules of series six above. Here are my stocks selection rules:
- Not trading in China, due to the high probability of fraud in Chinese stocks
- Exclude Financials, REIT, and Biotech, as their accounting is different and do not fit the net-net formula
- Positive Common Equity, to exclude companies with non-orthodox accounting
- Exclude delinquent companies which did not file a 10K/10Q for more than six months
- Exclude companies with less than ten employees and companies with zero sales, to avoid skeletons and bogus entities
- Liquidity: Market cap > $10M, price > 20 cents, Average daily volume of at least $1000 a day.
- Valuation: Market Cap equal or lower than Adjusted Net Assets (as defined above)
I created a simulation for a portfolio comprised of up to 20 such stocks, equal-weighted. The simulation spans a period of 18 years, from June, 30th 2000 to June 30th, 2018. Stocks are rebalanced once per year. If, for a given year, there are less than 20 net-nets available, we give the available net-nets a larger allocation, up to 7.5% of the portfolio per holding (vs. the nominal 5% allocation). Here are the results:
Would you invest in a portfolio that behaves like this?
I know I wouldn’t.
Unfortunately, albeit my elaborate experimentation, I have not been able to find a viable quantitative strategy that combines high expected performance, with reasonable volatility and asset turnover. I even tried applying market timing techniques to dampen the volatility and drawdown, yet the results were not robust enough.
Conclusions and Takeaways
Net-nets outperforms the market by a significant margin.
Their outperformance is statistically significant and had been shown as such (I am cautious not to use the word “proven.” No investing strategy is proven in the scientific sense of the word) by top academic researchers.
Nevertheless, meeting the academic standards of statistical rigor is not enough for a practitioner. Anyone who wants to profit from net-nets in real life, with real money, rather than in the context of academia – would have additional requirements. Such a strategy would not only have to have positive expected returns, but also bearable volatility, low-enough asset turnover, and sufficient liquidity and Availability of assets to invest in.
Net-nets possess several disadvantages, making them impractical for systematic investing, i.e., investing through the sole discretion of a carefully-crafted algorithm:
- Limited Availability – there aren’t always enough net-nets to invest in to be able to have an adequately diversified portfolio. Per my experience, the minimum is 20 stocks, while on occasion, the number of net-net drops to a single-digit number.
- Illiquidity – most net-nets are highly illiquid, making them unsuitable most investors’ accounts.
- Excessive Volatility – the variation in performance, as well as the low correlation to the general market, is crazy high, as discussed above.
So how can an investor play net-nets?
To enlarge his or her investable universe, an investor can go global. The analysis herein consisted solely of U.S.-traded stocks. An investor may overcome the availability constraint and even the liquidity constraint by building a diversified portfolio of both U.S. and global net-net stocks. Unfortunately, I do not currently possess global stocks data to be able to expand my research beyond the U.S.
Moreover, an investor can combine quantitative techniques and some manual analysis. A quantitative screener (such as the one available on TalDavidson.com) can be used to zero down on the list of net-nets candidates. Then, by manually researching the companies and their unique situations, an investor can select only the ones with the most potential. The net-net quantitative screeners are a time saver.
I believe Benjamin Graham’s advice still applied. One must apply discrimination in selecting the net-nets to invest in. Such discrimination requires analysis of the company, its underlying business, and its idiosyncratic situation.
Net-nets are not broken. There is a lot of money to be made with net-nets. It just got harder as you need to identify the right ones. My net-net quantitative screeners can take you through half of the way, but you have to make the other half yourself.