Category Archives: Quantitative Value Investing

Thoughts on Quantitative Value

Strategy Shift

As I’ve documented on this blog, I’ve shifted my strategy from a widely diversified portfolio of quantitative bargains.

I’m running this portfolio with a different strategy: a concentrated portfolio of high quality businesses that are temporarily selling for cheap multiples.

This portfolio that I track on this blog is a “variable” portfolio, as Harry Browne put it. I wrote about variable portfolios on Medium here.

I also have my asset allocation strategy, which is where I’ve decided to put the rest of my savings. That’s the weird portfolio, which I’ve written about here.

That portfolio has a strong focus on small cap value, but also has built in protections for different economic environments.

Even though I’ve shifted from a quantitative approach in my variable portfolio, I’m still a believer in it.

Quant Value: Challenges With a DIY Approach

I am still very much a believer in quantitative value strategies and think they’ll come back even though they have underperformed the market for many years. I just think that the manner in which I was implementing it is not the best way to actually do this.

I think that the best way to implement a quantitative value strategy – for me, anyway – is to do it with an ETF. In my passive portfolio (which is where I have most of my net worth), I do that with VBR and VSS, to create a small value portfolio that’s globally diversified.

There are other excellent value ETF’s out there, many of which are more focused on the factor. Probably the best examples of this are Alpha Architect’s IVAL and QVAL, which quantitatively implement a deep value strategy.

One of the coolest value ETF’s out there is Tobias Carlisle’s ZIG ETF – which gives an investor a long/short value oriented hedge fund without the 2 & 20 fee and does it in an optimized tax structure.

When quant value comes back (and I strongly believe it will), my opinion is that the more focused ETF’s ought to experience a more significant outperformance – just like they’ve experienced more significant underperformance during value’s season of woe.

The Vanguard value ETF’s have less exposure to the factor and have underperformed less during value’s troubles. The Vanguard ETF’s are lower octane, which probably reduces long term returns but can help prevent behavioral errors during the bouts of underperformance.

An investor needs to decide how hardcore they want to go, which is a personal preference.

I think that ETF’s are the ideal way to implement a quant value strategy. A quant value approach works over the very long run even though it can underperform for years. It’s a lot easier to deal with that when done passively.

I think the best approach is to put it on autopilot and not look at it for a long time. Give the strategy a long time to work. Value works because it doesn’t always work. An investor can buy an ETF and not look at it for 20 years, which is probably the better approach to take with a quant value strategy.

Actually DIY’ing it – pick the stocks, buy and sell, deeply analyze the companies – can be very labor intensive. When value enters one of its funks as it has for the last 5 years – you can feel that all of those hours were spent fruitlessly. That’s an extremely frustrating process, which is what I’ve been dealing with.

That’s what I’ve discovered over the last 5 years or so. With an ETF, it’s a lot easier to deal with the seasonal underperformance of the strategy.


As a DIY investor, I think it makes more sense to implement a quantitative value strategy by passively owning ETF’s rather than try to DIY it. This is based on my own experience trying to implement a quant value strategy with a discretionary element, which I documented on this blog.

I found that actually analyzing all of the companies in a 30-stock deep value portfolio and trying to buy and sell at the right times is an exhausting process.

It’s also a recipe for behavioral errors. I made plenty of behavioral errors. These companies look like they have severe handicaps and it takes an extremely skilled business analyst to distinguish between those that have permanent impairment from those that are temporary. I think it’s best to do this in a diversified portfolio and not obsess too much over picking the winners.

For me, doing this in a discretionary way also led to foolish market timing. I tried hard to predict the crash and figure out the economic cycle and I completely failed at this.

These errors can be avoided by simply owning the ETF and letting it do its work over a long period of time. I don’t have to actually obsess over and follow closely all of the stocks in the portfolio. I can let the factor work. I can give the factor time to work.

To further prevent behavioral errors, I own my small value ETF’s in a portfolio with safeguards for different economic environments. That’s why I own things like gold and long term bonds to protect against economic catastrophes because I have a pessimistic bent and always worry about this sort of thing. Long term bonds protect against deflation and recessions. Gold protects against extreme inflation, currency collapse, and a global Depression.

I’ve found that these defensive elements were essential for me. They kept me from making behavioral errors in March. In the depths of the crisis, I was only down about 14% in the weird portfolio and I didn’t sell like I did with this variable portfolio. I was able to stick to it. It’s nice to have that asset allocation doing its thing, with the confidence of knowing that it has all kinds of built in protections for different economic environments.

Another consideration is taxes. The account I track in this blog is money I’ve saved up over the years in an IRA and has deferred taxes, so this wasn’t a major consideration.

For a taxable account, though, a high turnover quant value portfolio is going to generate a lot of taxes because there is a lot of trading. That’s the beauty of an ETF. The taxes only need to be paid when the ETF itself is sold. The trading within the ETF – the selling of stocks when the multiples pop, buying new stocks at compressed multiples, doing it over and over again – doesn’t create taxable events due to the ETF structure.

You might still want to pursue a deep value strategy and DIY it, which is fine if that’s your bag. I just found it to be extremely difficult to implement on my own and concluded that it’s easier to simply have an ETF do the work for me.

You Do You

You might feel that you want to implement a DIY, quantitatively oriented, deep value strategy. That’s totally fine.

With that said, I found it extraordinarily difficult to do on my own. I made a number of behavioral errors in implementation. When the turds hit the fan, I found my portfolio terrifying to own.

My conclusion is that implementing a value factor strategy through an ETF – and letting someone else (or a computer) do the work – is the ideal approach. You can just enjoy the sausage and not concern yourself too much with how it is made.

Another great thing about ETF’s is that you can spend your time doing something more important than following companies and the market.

These were the lessons I learned. You might have a different conclusion, which is totally fine. Investing is a personal process and you have to figure out what works for you.


PLEASE NOTE: The information provided on this site is not financial advice and it is for informational and discussion purposes only. Do your own homework. Full disclosure: my current holdings.  Read the full disclaimer.

Horse Racing & Value Investing


Teenage Fun at the Track

I grew up near a horse racing track.

As a teenager, my friends and I didn’t partake in normal teenager activities. As a bunch of geeks, we didn’t go to many school dances or parties.

Instead, we hung out in a smoke filled race track with a bunch of old men.

I instantly fell in love with the track. The atmosphere was something that I gravitated towards.

There was something fun about 20 screens with odds and potential bet combinations flowing at me.

There was the smell of smoke everywhere, along with constant yelling and cursing.

I loved it.

My friends and I would always laugh at the old men who could string together the most colorful combinations of profanity.

They would invent combinations of curse words that I couldn’t imagine. These men were the William Shakespeares of profanity. Maestros of expletives.

We were never carded, despite the fact that we were under 18. It was strange because we were clearly a bunch of 16 year olds. We would place our bets electronically and only go up to the tellers to cash out.

I’m sure that the tellers knew what was going on and just didn’t care. They probably weren’t being paid enough to make a big deal about it. After all, our age was even more apparent when compared to the group of 70 year old men that we were hanging out with.

We would usually go to the track with $5 in our pockets and spend an afternoon at the track, pooling our funds together to share in the losses and occasional winnings.

The amount of effort and brain power that we put into losing money was truly astounding, but we had a lot of fun doing it.

I learned a few early lessons at the track:

1) Horse betting is a game where the house takes 20% of the pool and is a sure fire way to lose money.

2) The worst way to gamble is to bet on the horse with the best odds.

3) Horse racing is a fun way to spend $20 on an afternoon of exhilarating fun.

Point #2 is the most relevant when it comes to investing.

When we first starting gambling, we would bet on the favorites and thought that made the most sense.

As we lost money betting on favorites, we realized that the favorite was nearly always over-valued.

It dawned on me that horse racing wasn’t about predicting the horse most likely to win. It was about finding a horse that was undervalued. It was about finding where the payoff would exceed the true odds of the horse.

It didn’t take sophisticated analysis to realize that the favorite was overvalued. This was obvious when analyzing the data.

To find data, we needed an important source: The Daily Racing Form.

Unfortunately, the Racing Form was an expensive document and we didn’t want to dip into our gambling pool to pay for it. We had to get creative.

Usually, to conserve our betting pool, we would sit patiently and listen for a particularly foul mouthed gambler. Usually, the tempo of the profanity was tied closely to the amount of money that they were losing. Eventually, this gambler would throw their racing program on the floor in disgust and vow never to return to the track. They would scream that the place should be burned to the ground, as if it were the city of Carthage and they were the Roman Empire. (Narrator: They would usually return on the following weekend.)

I would then take the mangled program off of the floor and use it to make betting decisions for the rest of the afternoon.

In the program, the best horse was usually given something like 2-1 odds by a professional handicapper. As the bets poured in, the horse was usually assigned odds of something like 1-1 because everyone wants to bet on the winner. In other words, the favorite typically became overvalued.

The favorites were guaranteed to pay out less than they were “worth,” a phenomenon made particularly punishing by the house’s takeout.

In other words, everyone knows that the favorite is the likely winner and the favorite typically became over-valued. Betting on favorites is a way to win more often, but it’s also the way to lose the most money at the track.


I think of the best stocks in the stock market as favorites at the track.

Charlie Munger explains this better than I can:

The model I like—to sort of simplify the notion of what goes on in a market for common stocks—is the pari-mutuel system at the racetrack. If you stop to think about it, a pari-mutuel system is a market. Everybody goes there and bets and the odds change based on what’s bet. That’s what happens in the stock market.

Any damn fool can see that a horse carrying a light weight with a wonderful win rate and a good post position etc., etc. is way more likely to win than a horse with a terrible record and extra weight and so on and so on. But if you look at the odds, the bad horse pays 100 to 1, whereas the good horse pays 3 to 2. Then it’s not clear which is statistically the best bet using the mathematics of Fermat and Pascal. The prices have changed in such a way that it’s very hard to beat the system.

Everyone knows who the winners are in the market. Facebook, Amazon, Google, Netflix, Visa, Mastercard, Costco, Domino’s, etc.

Everyone wants to bet on the winner.

As a result, the favorites are bid up to extraordinary valuations. Valuations that don’t always make sense.

It happens again and again in every market cycle. The Nifty 50. The late ’90s Nasdaq.

Everyone wants to bet on the winner. It’s satisfying because the winners win the most often. There is also comfort in going with the crowd.

However, betting on the favorites is a way to lose money at the track. Similarly, betting on expensive stocks is a way to lose money in the markets, even though it hasn’t seemed that way for the last decade.

Like the track, the trick in investing is to identify under-valued bets. You want to find a bet where the odds don’t make sense. You want to identify situations where the quoted odds are worse than the true odds. In other words, you want to identify mis-priced securities.

With that said, reflexively betting on horses with bad odds is also a way to lose money, as my friends and I learned. They often deserve those bad odds. This is likely why buying all of the stocks at 52-week lows is also a terrible way to invest.

Anyway, like favorites, expensive stocks under-perform over the long run as a group, which is what all of the data screams in every backtest over every long period of time.

Of course, it’s hard to rely on that data when looking at a market. It feels good to bet on the winners.

The trouble is: everyone else knows who the likely winner is. That’s why they command a high price.

The OG Of Quant: Andrew Beyer

In my quest to find a better way to handicap horse races, I looked for a book that would teach me how to handicap.

I discovered the work of the greatest handicapper of all time: Andrew Beyer.

Andrew Beyer was doing quantitative analysis before Moneyball. He was doing it before James O’Shaughnessy wrote What Works on Wall Street or Jim Simons launched Renaissance Technologies.

When Andrew Beyer started betting, most horse handicappers were obsessed with class. Class was based on the breeding history of the horse. Horses of a better lineage were assigned better odds. Handicappers would often assign nonsensical stories to horses in their efforts to attempt to predict horse races.

Andrew Beyer thought that all of this was unreliable sorcery and sought to create a more rigorous way of determining the true odds of the horse.

He developed what became known as the Beyer Speed Figure: a quantitative way to determine how fast a horse ran a race and to do it in a way that captured different conditions and lengths of track. A single number that would assess a horse’s performance in a race.

When Beyer was gambling with his own funds in the 1960’s and early 1970’s, he had to manually calculate speed figures. He would spend his time manually going through old racing forms and figuring out the speed figures of different horses.

At the time, he had a critical piece of information that no other horse player had. He was like the Jim Simons of the racetrack.

Beyer was able to use this information to figure out that the true favorite in the race was a long shot with 30-1 odds that no one was paying attention to.

He had the holy grail of horse racing: a quantitative system that would allow him to collect money from other gamblers at the track.

In 1975, Andrew Beyer wrote a book about his discovery, Picking Winners. This began the gradual unraveling of speed figures as a tool to make money.

Andrew Beyer could have silently continued cashing in wining tickets, but he was a journalist and a young man who wanted to make his mark on the world, so he published his book about it.

The book outlined how to manually calculate speed figures, which many horse players started doing. Unfortunately, because more people knew about the signal, horses with the best speed figures started to command better odds.

In the 1980’s, paid services began providing the speed figures for a fee. This further diminished the labor of horse geeks who were manually calculating speed figures on their own.

Beyer responded to this by publishing The Winning Horseplayer in 1983, which advocated using complex bets (such as Exactas, Trifectas, and Daily Doubles) and combining with speed figure analysis to identify under-valued situations at the track that were not simple win bets.

Of course, the knowledge and availability of speed figures continued to chip away at the potential profits that could be made from this signal. Even the complex bets advocated in The Winning Horseplayer lost their effectiveness.

The final nail in the coffin was applied when The Daily Racing Form began publishing speed figures in 1992. Once that happened, the horse with the best speed figure started to command the best odds. Speed figures were no longer a tool that could be used to regularly generate profits.

With that said, speed figures were still the best tool available to predict the outcome of a horse race but they were no longer a way to consistently cash winning tickets.

Andrew Beyer became a legend and I’m sure he made a significant amount of money from the publication of his books and the incorporation of his method in the Daily Racing Form.

Unfortunately, Beyer speed figures no longer offered an edge to the horse player who could use them to find under-valued bets at the track.

Once everyone knew about them and the information was widely available, the party was over.

Value Investing

I sometimes worry that simple metrics of value are suffering the same fate as the Beyer Speed Figure.

Beyer himself discusses his speed figure in comparison to the stock market and the use of the P/E ratio:

It was a tremendous edge to have the figures at a time when most people didn’t use them or even believe in them. I can only draw an analogy to the stock market if the concept of the P/E ratio were unknown to, or its importance was disbelieved by the majority of people buying stocks, and you were about the only guy who knew what the P/E of different stocks was, it would be a tremendous advantage and I had that advantage for many years.

When everyone realizes that simple measures of cheapness work, doesn’t that crowd out the trade and reduce the effectiveness?

One can imagine that men like Benjamin Graham and Walter Schloss were like Andrew Beyer at the track in the 1960’s. They had a statistical method that no one else used and were able to use the information to make tremendous amounts of money in the stock market.

The disappearance of net-net’s in the US stock market lends credence to this view. Net-net’s disappeared because everyone knew about them. It’s obvious that a company selling below liquidation value is a tremendous bargain, which is why there aren’t a lot of companies that trade below liquidation value today.

Meanwhile, value as a statistical factor has become very popular since Fama and French made it respectable in the early 1990’s.

If everyone knows that simple statistical measures of value work, will it stop working?


Perhaps the “cheap” buckets of the market were filled with mis-priced securities in the past, but now this cheap universe generates more scrutiny among investors, thus eliminating the mis-pricings.

As the mis-pricings are eliminated and quantitative investors elevate the valuations of the cheapest stocks, will this have the same effect of everyone betting on speed figures and making the signal worthless?

I don’t think so.

If this were happening, I would expect to see the cheap buckets of the market become expensive relative to their long term mean.

This doesn’t seem to be happening. In fact, the cheapest chunk of the market is not expensive relative to its long-term mean.


If value were becoming a crowded trade, wouldn’t this segment of the market become more expensive? Just like horses with high speed figures started to command better odds as the figures became more popular?

I actually think this case could have been made in the mid-2000’s, but no one was making it back then.

By the mid-2000’s, value was triumphant. The unwinding of the tech bubble wrecked the investment world, and the only people with respectable track records were those who embraced cheap stocks.

Quantitative value investing became popular and investors piled into it. This made value stocks more expensive relative to their long-term mean.

We’re now at the opposite end of the spectrum. After a poor decade for value investing, most people hate it. Even value investors are capitulating and buying into the compounders.

I think the absolute cheapness of value is an indication that the party is not over for quantitative value investors. It’s as if the high Beyer speed figure horses are commanding worse odds than they were before the signal was discovered. It’s not a crowded trade right now.

It was a crowded circa 2007.

It should have been obvious in the mid-2000’s that this was becoming a crowded trade when value stocks started to become much more expensive relative to their long term mean.

For this reason, I think it is important for quantitative investors to play close attention to the absolute valuation of the value segment of the market. There are times when the bet on value makes sense and times when it does not.

It’s completely possible that simple metrics of cheap will no longer be useful in the future. Monitoring the absolute cheapness of the factor is a way to monitor whether or not this is happening, just like better odds on fast Beyer horses was a sign that the party was over.

The Equity Risk Premium

Everyone likes to pick on value investors, but I find it amusing that no one ever asks if something like the equity risk premium can be arbitraged away.

In my view, the equity risk premium is not different than the value factor.

If everyone knows that equities command higher returns than other asset classes, why doesn’t that become a crowded trade that reduces long-term returns?

So far, the 21st century hasn’t offered much of an equity risk premium. Since 2000, the US stock market has delivered a 5.62% CAGR, while the US bond market has delivered a 4.98% rate of return. That’s not much of a premium, especially compared to the pain that equity investors had to endure, such as multiple 20-30% meltdowns and two nasty 50% drawdowns. International stocks have actually under-performed bonds entirely, delivering a pathetic 2.58% CAGR.

This all stands in sharp contrast to what happened last century, before everyone knew about the equity risk premium.

In the 20th century, stocks returned 10.16% and bonds returned 4.82%. Now that’s a premium!

Could the poor performance of equities have occurred because everyone realized that equities perform better over the long run in comparison to bonds? As a result, were equities bid up to a level in the late ’90s that guaranteed poor performance going forward?

In fact, right now, equities are so expensive that they are likely to deliver another lost decade like the 2000’s. It’s possible that they may even under-perform the low yields offered by US treasuries. In fact, I think that’s probable.

At the moment, I think that the prospects for the value factor are better than those for the equity risk premium itself. This is high level heresy, but the evidence backs it up. After all, in the 21st century, there hasn’t been an equity risk premium. Small value is one of the only investing styles that has offered a decent return over the last twenty years, a 7.9% CAGR.


Two TV themes that struck terror in my heart as kid in the 1980s:

PLEASE NOTE: The information provided on this site is not financial advice and it is for informational and discussion purposes only. Do your own homework. Full disclosure: my current holdings.  Read the full disclaimer.

Is diversification for idiots?


Concentration is cool

Value investors are prone to make outsized bets on single stocks or have extremely concentrated portfolios.

Mark Cuban has a more blunt view of diversification:

“Diversification is for idiots.”

Warren Buffett, the master himself, made many concentrated bets throughout his investing career. The most famous example is American Express during his partnership days. After the salad oil scandal in the 1960s, American Express stock plunged. Most investors thought the salad oil scandal would put American Express out of business and the stock fell.

Buffett realized that American Express would survive and knew it was a fantastic franchise. He moved an astonishing 40% of the partnership’s assets into American Express, sending the partnership’s returns from great to extraordinary. Buffett would repeatedly make concentrated bets throughout his career when he had conviction. He took on significant, concentrated positions in companies like GEICO and Coca Cola.

Buffett had this to say about diversification:

“Diversification is protection against ignorance. It makes little sense if you know what you are doing.”

Munger has this to say:

“The whole idea of diversification when you’re looking for excellence is totally ridiculous. It doesn’t work. It gives you an impossible task.”

Because value investing has come to mean whatever Warren Buffett and Charlie Munger say it is supposed to mean, most value investors take their advice to heart and make big, courageous, outsize bets on their “best” ideas.

The Other Side of Concentration

Concentration is cool until it is not. Legends like Bill Ackman built up early, stunning returns by concentrating on their absolute best ideas. This worked well until Ackman’s best ideas were Valeant and shorting Herbalife.

Concentration worked out well for Joel Greenblatt, whose early hedge fund used extremely concentrated portfolios of spin offs and special situations to deliver stunning returns.

Ben Graham recommended holding 30 stocks and believed this would provide adequate diversification. The eggheads in academia actually agree with him on this point, and most the research shows that an ideal portfolio is around 20-30 stocks.

Other successful value investors who are more in the Graham state of mind also have diversified portfolios. Walter Schloss owned over 100 stocks. Seth Klarman currently has 35 holdings. Irving Kahn also stuck to Graham’s advice and owned 20-30 positions at any given time.

The Evidence

For me, I’m more interested in the evidence of what works so I conducted backtests on a value portfolio in an effort to identify the ideal number of holdings. I backtested the returns and characteristics on a simple EV/EBIT portfolio. I began with the insane portfolio of buying the single cheapest stock in the S&P 1500. I then added the next cheapest, creating portfolios ranging from 1 stock to 30 to determine the ideal size.

This is pulled from an S&P 1500 universe, the backtests are conducted since 2005, and the portfolios are rebalanced annually.


The return characteristics of different portfolio sizes are all over the place, but they all beat the market over the long run. Strangely, they start out high, bottom out around a dozen stocks, and then start to improve.



I don’t think volatility is risk. I believe risk is the probability of losing money permanently. However, I also believe that volatility accurately measures how big a container of Tums that you should have at your desk.

I also don’t think a Sharpe ratio is the accurate representation of how decent a portfolio is.

With that said, here are the results:



In terms of volatility, most of the benefits come from the first dozen positions. However, in the context of EV/EBIT, the Sharpe ratio tends to max out around 25 positions.

Maximum Drawdown

My view of risk is less about volatility and more preventing blowups that will permanently impair my capital.

The bigger the blowup, the harder it will be to recover from it. In mathematical terms, large drawdowns are increasingly more difficult to recover from.


The deeper the drawdown, the bigger that the bounceback needs to be. Once the drawdown exceeds 50%, it becomes nearly impossible to recover from. Drawdowns of the 80% magnitude are almost certainly fatal to investment results. Common sense tells us that concentration will lead to years where returns are remarkable and that this will likely be balanced out by deep and painful drawdowns later on. The evidence backs this up.


Concentrated portfolios are prone to some genuinely epic drawdowns. For the insane 1-stock portfolio, the maximum drawdown was 90%. Over the long run, the 1-stock cheap portfolio delivers a return similar to all of the others, but you will likely jump out of a window or develop a drug problem before you can actually enjoy those returns.

It appears that the first 15 positions can at least get the portfolio down to the market’s maximum drawdown.


  • Most EV/EBIT portfolio sizes beat the market, from the ultra-concentrated to 30 or more stocks.
  • In terms of reducing volatility, the first dozen stocks provide most of the benefits of diversification.
  • It takes at least 15 stocks to reduce the odds of a portfolio blowup that will exceed the maximum drawdown of the S&P 500.
  • Sharpe ratios tend to be maximized in a value portfolio around 25 positions.
  • Diversification is not for idiots.
  • Ben Graham was right (as usual), and the ideal portfolio size is somewhere around 20-30 positions.


“Killing Eve” is a great show.

PLEASE NOTE: The information provided on this site is not financial advice and it is for informational and discussion purposes only. Do your own homework. Full disclosure: my current holdings.  Read the full disclaimer.

Did Ben Graham abandon value investing?


Did Ben Graham abandon value investing?

Benjamin Graham refined and changed many of his views at the end of his life in the 1970s.

Even though he was retired and surrounded by beautiful people and weather in California, he continued to conduct extensive research into the behavior of securities as an intellectual pursuit.

Reading some of his writings and interviews from the period, some have concluded that Graham abandoned his philosophy and embraced the efficient market hypothesis.

Here is a quote of his that led many to this conclusion:

“I am no longer an advocate of elaborate techniques of security analysis in order to find superior value opportunities. This was a rewarding activity, say, 40 years ago, when our textbook ‘Graham and Dodd’ was first published; but the situation has changed a great deal since then. In the old days any well-trained security analyst could do a good professional job of selecting undervalued issues through detailed studies; but in the light of the enormous amount of research now being carried on, I doubt whether in most cases such extensive efforts will generate sufficiently superior selections to justify their cost.”

This is a quote that efficient market types will often throw in the face of value investors. To paraphrase these people: “See, even Ben Graham thought this was all a bunch of nonsense! Shut up an buy an index fund, idiot!”

Reading these quotes, many value investors are left stung in disbelief. It’s like suddenly discovering that the Pope is an atheist, Mr. Miyagi was secretly helping the Cobra Kai, Picard collaborated with the Romulans, or that Johnny eventually put Baby in a corner.

The Truth

The truth is more nuanced. Yes, Ben Graham didn’t think detailed, individual security analysis was as useful as it was when he originally wrote the book in the 1930s. That doesn’t mean he gave up on the concept of value investing.

In fact, Graham did not agree with the efficient market crowd. He had this to say about them:

“They say that the market is efficient in the sense that there is no practical point in getting more information than people already have. That might be true, but the idea of saying that the fact that the information is so widely spread that the resulting prices are logical prices – that is all wrong, I don’t see how you can say that the prices made in Wall Street are the right prices in any intelligent definition of what right prices would be.”

The behavior of markets is, indeed, crazy. You have to be slightly brainwashed by the beautiful, peer-reviewed, academic work of the Church of Beta to think that prices are logical. Look at all of the insane bubbles that have plagued securities markets in the last few decades. Look at the nonsensical valuation of stocks in early 2009.

Look at the activity in multiple asset classes. Dotcom stocks, crypto, even housing. Look at the wild ride that the S&P 500 had in the 1990s and 2000s. Was the late ’90s run up rational? Was the hammering that stocks endured in 2008 logical or emotional?

It was all irrational, it was crazy. It wasn’t a market unemotionally weighing information. It was herds of professional investors reacting emotionally to events.

Mr. Market is alive and still doing crazy shit. If you don’t believe me, just watch the cable coverage of market action every day. Cable financial news is a torrent of speculation, FOMO, greed, and fear.

If Mr. Market were a person, he would live in Florida.

Another important snippet from the quote really stands out: “the information is so widely spread.” Graham was writing in the 1970s. We tend to think of the 1970s as a time when people were using stone tablets in between bong hits and classic rock albums. The thinking is that modern markets are so much better because we have the internet, computers, financial Twitter, blogs. We are so sophisticated and technologically advanced!

This is a conceit of every generation. Everyone thinks that their era is remarkably sophisticated and eras of the past were the dark ages. The experiences of our ancestors are primitive and not useful. The reality is that history rhymes and human nature never changes, no matter our level of technological sophistication. Eventually, the innovations of every era are ultimately discarded and regarded as quaint.

In reality, the critical information people needed to know about markets was available in the 1970s. Just because there is more information and it is more convenient in today’s world, it doesn’t make modern investors any more sophisticated or less emotional than the investors of yore. Indeed, the critical information about stocks has been widely available for a long time.

There is a perception that because stock screening technology and the information is readily available, that the edge for value investors has been eliminated. I think that’s bunk. Whether it was Moody’s manuals in the 1950s, Value Line in the 1970s, or stock screeners today – it has never been hard to find cheap stocks. What’s hard is actually buying them, not discovering them.

The source of returns in value investing has never been informational, it has been behavioral. It has been revulsion towards companies that are in trouble contrasted with starry-eyed love for companies that are making all the right movies.

There is a perception that the cheap stocks of past markets were diamonds in the rough. With technology, the thinking goes, those diamonds have been scooped up. Nothing could be further from the truth. Cheap stocks were always ugly stocks. The idea that there were cheap situations without any hair on them is a myth. The reason that cheap stocks outperform in historical analysis is because they were ugly. It was because they had problems.

The only thing that has changed is the methods of gathering that information.

Quantitative Value Investing

Back to the topic at hand.

While Ben Graham thought that detailed individual security analysis was a waste of time, he also believed that the efficient market theory was bunk.

Graham supported quantitative value investing. In other words, systematically purchasing portfolios of cheap stocks. Within the portfolio, some stocks would undoubtedly be value traps. As a group, however, they would generate returns that would beat the market.

Graham sums it up in this quote:

“I recommend a highly simplified strategy that applies a single criteria or perhaps two criteria to the price [of a stock] to assure that full value is present and that relies for its results on the performance of the portfolio as a whole — i.e., on the group results–rather than on the expectations for individual issues.”

 In other words, he believed that investors should select a portfolio of cheap stocks and construct a portfolio of them to systematically take advantage of market inefficiency.

What quantitative criteria, then, did Graham recommend to select bargain stocks?


The first method, which Graham was most famous for, was purchasing stocks selling below their net current asset value. Graham referred to investing in net-net’s in the following fashion:

“I consider it a foolproof method of systematic investment—once again, not on the basis of individual results but in terms of the expectable group outcome.”

A crucial part of Graham’s quote is his point that the results of individual net-net’s are not dependable. Graham recommends buying a basket of them and allowing the portfolio to generate returns.

The problem with this approach is that they aren’t available in bulk frequently in the U.S. markets. As Graham pointed out, net-net’s should only be purchased as a portfolio. The only time that there are enough net-net’s to create a portfolio is in market meltdowns like the early 2000s or 2008-09.

I am eagerly anticipating the next decline so I can buy a portfolio of net-net’s.

Simple Graham: Low Price/Earnings & Low Debt/Equity

The next approach that Graham outlined was buying a portfolio of stocks with simultaneously low P/E ratios and low debt/equity. The great thing about this approach is that it is applicable in the United States outside of meltdowns, unlike the net-net approach.

This is the approach that I take with my own investments, albeit with other criteria (low price/sales, low EV/EBIT, high F-Scores, etc.) and qualitative analysis added to it.

Regarding price/earnings ratios, Graham recommended purchasing stocks that double the yield on a corporate bond. He suggested looking at the inverse of the P/E ratio, or earnings yield. A P/E of 10 would be a 10% earnings yield, for instance.

“Basically, I want to double the interest rate in terms of earnings return.”

“Just double the bond yield and divided the result into 100. Right now the average current yield of AAA bonds is something over 7 percent. Doubling that you get 14, and 14 goes into 100 roughly seven times. So in building a portfolio using my system, the top price you should be willing to pay for a stock today is seven times earnings. If a stock’s P/E is higher than 7, you wouldn’t include it.”

In other words, the value criterion was remarkably simple: a low P/E ratio.

Graham’s second criteria was a low debt/equity ratio.

“You should select a portfolio of stocks that not only meet the P/E requirements but also are in companies with a satisfactory financial position . . . there are various tests you could apply, but I favor this simple rule: a company should own at least twice what it owes. An easy way to check on that is to look at the ratio of stockholders’ equity to total assets; if the ratio is at least 50 percent, the company’s financial condition can be considered sound.”

Concerning portfolio management, Graham recommended holding onto the stock for either two years or a 50% gain. I think this is an important point: Graham never recommended holding shares forever. That’s Buffett’s approach. Graham, in contrast, suggested a high turnover portfolio: buy a large group of undervalued stocks, wait for them to return to a reasonable valuation, then sell and move on to the next situation.

Graham backtested this method going back to the 1920s and found that it generated a 15% rate of return over the long run.

Wesley Gray and his team at Alpha Architect also backtested Graham’s method and found that Graham was right. The technique delivered 15% rates of return over several decades.

Even with an exceptional 15% rate of return, the strategy underperformed at some key moments. In 1998, for instance, it lost 1.94%. The S&P 500 was up 28% that year. In 1999, it gained only 2.51%. The S&P 500 was up 21% that year.

There was similar underperformance in the Nifty 50 era. In 1971, the Graham strategy returned only 1.57%. The S&P 500 gained 14.31% that year.

I believe we are in a similar moment right now. Only time will tell if I am correct.

The Simple Ben Graham Screen

I run multiple screens, but I use Graham’s criteria as a cornerstone in my stock selection. Even if I am wrong in my analysis, I know that I am at least looking in the right neighborhood.

Here are ten stocks that currently meet Graham’s criteria for earnings yield and debt/equity:

graham stocks

I am not recommending that you go out and purchase any of these stocks. I am merely showing that even in a frothy market like the U.S. today, there are still opportunities which meet Ben Graham’s criteria.


  • The source of Graham’s 1970s quotes featured in this blog post is The Rediscovered Benjamin Graham by Janet Lowe. The book is a collection of articles written about Graham, Congressional testimony, interviews, and articles written by Graham himself. Of particular interest are the bullish articles that he wrote in the early 1970s and early 1930s, discussing the deep undervaluation of American stocks. You can buy it here on Amazon. It’s a great read and gives you a clear perspective on how Graham’s thoughts evolved over time.
  • Captain Picard is coming back. I can’t express how much I am pumped about this. Here is Patrick Stewart explaining his enthusiasm for the role.
  • Turkey. Yes, I own the Turkey ETF. Fortunately, it is a small position. Here is an excellent article on the crisis.
  • I watched a random, weird, and goofy movie last night: The Final Girls. It’s a parody of ’80s slasher movies. If you’re familiar with all of the tropes of the genre and you’re in the mood for some lighthearted fun, I recommend it. If you’re not familiar with the genre, the jokes will probably not resonate.
  • Better Call Saul is back. If you’re not watching, you’re missing out. If you were a fan of Breaking Bad and aren’t watching this one, what the hell? As the series moves along, it is living up to its predecessor.

PLEASE NOTE: The information provided on this site is not financial advice and it is for informational and discussion purposes only. Do your own homework. Full disclosure: my current holdings.  Read the full disclaimer.

Debt/Equity: A simple way to identify companies that can go the distance

Value Works

I could name a litany of research, books, blogs, articles all showing a simple conclusion. The conclusion is pretty basic: the less you pay for a stock relative to a fundamental metric (earnings, asset value, etc.) the more likely it is to outperform. It’s common sense and all of the research shows it works.

Much of this blog is about how these simple approaches work over the long run. Cheap stocks outperform expensive stocks. In this blog post, I performed a simple backtest against the effectiveness of different valuation metrics. My conclusion was pretty simple: every one of them works even though some work better than others.

Why does value work?

Every valuation ratio is a measure of the expectations. A low valuation implies that the market has low expectations about the prospects of the stock. Embedded in expectations are emotional and behavioral biases. Value investors exploit these emotional and behavioral biases.

Value investing “works” because other investors overreact to news because they are more emotional. That’s the essence of Ben Graham’s allegory about “Mr. Market.”

The expectations embedded in a valuation ratio tend to be false. In reality, a stock with low expectations is likely to exceed those expectations. A stock with high expectations is likely to disappoint. This is true at a macro level when looking at entire stock markets based on CAPE ratios, and it’s true at a micro level when looking at individual companies.

I like to think of a value stock as a C-average kid. All that kid needs to do to impress their parents is come home with a B on their report card. Their parents are going to be thrilled. That’s Gamestop right now. In contrast, a straight-A student that comes home with a B is going to be grounded. That’s Amazon right now. Valuation = expectations.

Why Does EBIT/EV Work?

Most research shows that EBIT/EV is the best valuation metric of all. My own above backtesting reflects this along with backtests and analysis performed by people much smarter than I.

The reason why EBIT/EV works is the focus of Tobias Carlisle’s books that I highly recommend: The Acquirer’s Multiple and Deep Value.

It works for a few reasons. Like all the valuation metrics, the enterprise multiple captures low expectations. It goes further than a standard market cap metric for two other reasons: (1) Using enterprise value in the calculation brings the strength of the balance sheet into the valuation ratio. Enterprise values also reveal the actual size of the enterprise, as debt can sometimes dwarf market cap (this is something General Motors investors found out the hard way 10 years ago). (2) The further that you move up an income statement, the less likely that the accounting numbers are prone to manipulation. This is the reason that metrics like price/sales work better than price/earnings – it’s harder for an accountant to fake sales than it is for them to fake earnings.


How do we improve on valuation alone?

We know that low valuation metrics work and we know that EBIT/EV is the best metric of all. Joel Greenblatt sought to improve upon EBIT/EV in The Little Book That Beats the Market. He added his own quality metric: return on invested capital (ROIC). He showed that the combination delivers impressive results.

However, separate research from Tobias Carlisle in Deep Value and James Montier in The Little Note that Beats the Market shows that the “quality” component actually brings performance down.

The question is: why doesn’t return on invested capital work?

Quite naturally, a company earning high returns on invested capital will attract competition. A high ROIC is a target on a company’s back. It attracts competitors, which over the long run depresses ROIC. In contrast, a low ROIC implies that competitors are leaving the industry. The industry is likely near a cyclical trough and is about to rebound. The goal of the deep value investor is to identify these moments and buy.

Warren Buffett emphasizes high ROIC, but the key to his success is that he can identify businesses with sustainable high ROIC. In other words, companies that can maintain a high ROIC over decades. Moreover, he recognizes these businesses when their price offers a margin of safety. This is a skill that hardly any other investors have been able to duplicate. You should be skeptical of anyone who claims they can identify these companies.

ROIC is probably useless for average investors because, unlike Buffett, we don’t have the ability to determine whether or not a company can sustain it.

In that case, if ROIC doesn’t work for the average investor, then what metric improves on merely buying cheap?

Debt/Equity: A simple metric with significant results

What quality metric actually works? Ben Graham provided his answer: quality isn’t ROIC, it’s about the quality of the balance sheet.

Graham’s preferred metric was the debt/equity ratio. In 1976, Graham recommended buying baskets of 30 companies that have a simultaneously low P/E ratios and a low debt/equity ratio. His backtesting revealed that this strategy returned 15% per year.

My backtesting reveals that Graham (as usual) is right. A low debt/equity ratio improves the performance of every single valuation ratio and reduces maximum drawdowns.

My backtest only goes back to 1999, the universe is the S&P 1500, the portfolios are rebalanced annually, and the portfolio size is 30 stocks. The results are below.


As you can see, merely restricting the backtest to a population of companies with a debt/equity ratio below 50% (i.e., they have triple the assets that they have in debts) improves every single valuation metric that I tested. It seems absurd that such a simple metric would vastly improve the performance of every valuation metric, but that’s the result.

Why is this the case?

I think it is because any company whose stock has a cheap valuation is going to be in some type of trouble. The strength of a balance sheet is the reason that a company survives the crisis that it is mired in.

For most value stocks, all that they need to do to thrive is merely survive. There is nothing that guarantees survival more than a strong balance sheet. Usually, these companies are in an industry that is going through a difficult time (like retail right now). When the industry is going through a tough time, competitors go out of business or leave the industry voluntarily. When the competition is gone, the stage is set for the industry to come back to life. When the industry comes back to life, the survivors reap the rewards.

In the retail sector right now, the casualties are going to be the highly leveraged firms. An excellent example of this is Sears. Sears currently has negative equity and is highly leveraged (debts exceed assets). The Sears balance sheet is probably not strong enough to survive the “retailpocalypse”. In contrast, a company like Foot Locker (one of my holdings) has a strong balance sheet and will likely survive the shakeout. There is no way to know for sure, but common sense tells me that this is the likely outcome.

The survivors of an industry decline will have plenty of reasons for why they survived: Our management is excellent, our product is better, we have strategic vision, our employees are just so damn good, blah blah blah MBA buzzwords.

The real reason the company survived is that it had a stronger balance sheet than everyone else going into the downturn. The balance sheet made the company survive the tough time and hang in there longer than everyone else — in other words, a company with a good balance sheet can survive. As Rocky Balboa might have put it, the company can “go the distance”. In battered industry going through a crisis, all that a company needs to do to win is go the distance and survive.

The lesson of Rocky: Going the distance is the same thing as winning.

PLEASE NOTE: The information provided on this site is not financial advice and it is for informational and discussion purposes only. Do your own homework. Full disclosure: my current holdings.  Read the full disclaimer.


Growth doesn’t bring much to the party


Counterintuitive Findings

Prior to reading Deep Value by Tobias Carlisle, I always thought that the key in value investing was to find cheap companies that could grow fast.

Buffett even discussed the merits of combining growth and value in his 1992 letter:

Most analysts feel they must choose between two approaches customarily thought to be in opposition: “value” and “growth.” Indeed, many investment professionals see any mixing of the two terms as a form of intellectual cross-dressing.

We view that as fuzzy thinking (in which, it must be confessed, I myself engaged some years ago). In our opinion, the two approaches are joined at the hip: Growth is always a component in the calculation of value, constituting a variable whose importance can range from negligible to enormous and whose impact can be negative as well as positive.

A key point of Tobias’ book is that growth is not how value delivers returns. The discount from intrinsic value and the closing of that gap is the key driver of return in a value portfolio.

De Bont & Thaler

He cites two studies in the book that are compelling because of how counterintuitive they are. They were both conducted by Werner De Bondt and Richard Thaler. The first study looked at the best-performing stocks and compared them to the worst performing stocks in terms of price performance. They found that the worst performers go on to outperform the best by a substantial margin.

They also looked at this in terms of fundamental earnings growth. They reached the same conclusion: the worst companies outperform the best.

A Simple Backtest

I decided to backtest more recent data myself to see if this still holds true. In an S&P 500 universe, I performed a backtest going back to 1999. I compared the performance of the 30 companies with the fastest growth in earnings per share to a portfolio of the 30 worst stocks, rebalanced annually.

Just as De Bont & Thaler determined before, the 30 worst companies continue to outperform the 30 best.


Keep in mind: there is no other factor involved here except for 1-year earnings growth. We’re not even looking at these stocks in a value universe: it’s simply the 30 fastest growers vs. the 30 worst.

Value Drives Returns

The evidence suggests that value alone is the best determinant of future returns. Growth isn’t nearly as powerful.

For instance, I also tested the performance of a universe of stocks with a P/E less than 10. This universe of stocks delivered an 11.69% rate of return since 1999. Within this winning universe, if you bought the 20 stocks with the best earnings per share growth then the return actually declined to 9.77%. Fast growing value stocks actually underperform the overall value universe.

The same is also true from a macro standpoint. Looking at the performance of the S&P 500 since the 1950s, the greater determinant of future returns is starting valuation, not actual business performance.


As you can see, the valuation of the market at the start of the decade (Shiller CAPE and the average investor allocation to equities – both valuation metrics are discussed in this blog post) are far more predictive of future returns than actual business performance.

Look at where the divergence is widest — the 1980s versus the 2000s.

The 2000s was a much better decade than the 1980s in terms of actual business performance. During the 2000s, earnings grew by 191%. In the 1980s, earnings only grew by 16.40%.

However, the 1980s witnessed a 409% total return for the S&P 500, while the 2000s actually clocked in a net decline of 9%.

Of course, there were macro events driving both markets. In the 1980s, returns were bolstered by interest rates declining from all-time highs once inflation was brought under control. At the end of the 2000s, we suffered the worst financial crisis since the Great Depression and the worst recession since the early 1980s, which negatively impacted stocks at the end of the decade.

With that said, the key factor behind the returns was the overall valuation of the market, not macro events or even business performance.

The undervaluation of the US market in the early 1980s was the true force that propelled the bull market forward.  In 1980, the Shiller CAPE for the US market was 8.85 and the average investor allocation to equities was only 23%.

In contrast, the overvaluation of the US market in 2000 was the key force that drove down returns over the next decade. In 2000, the Shiller CAPE was 43.77 and the average investor allocation to equities was 50.84%. Actual corporate results were impressive but that wasn’t enough. Valuation mattered more.


The conclusion is both simple and radically counterintuitive: valuation matters more than growth in predicting future returns for a single company stock or an entire market.

In the long run, growth simply doesn’t bring much to the party.

PLEASE NOTE: The information provided on this site is not financial advice and it is for informational and discussion purposes only. Do your own homework. Full disclosure: my current holdings.  Read the full disclaimer.

What is the best measure of quality?


In an earlier post, I examined the performance of different value metrics. My conclusion was simple: cheap stocks beat expensive stocks.

Most value investors don’t simply look for cheap alone. They try to find companies that are both cheap and good. Good is typically defined as companies that can earn high returns on their capital.

Finding these companies is a worthwhile pursuit but it is difficult to pull off systematically because companies earning high returns on capital are going to attract significant competition. I think it is far more difficult to do this than most value investors appreciate. Mean reversion, fueled by competition, inevitably pulls these returns down. Finding the rare birds that don’t succumb to this is hard.  These companies usually have a “moat“, which is hard to identify. Needless to say, this kind of investing requires a touch of genius that I don’t have. When investing, I operate under the assumption that every company succumbs to mean reversion.

With that said, finding these rare opportunities certainly pays off over the long run. You can park money in a company like Coca-Cola or Nike and earn high returns over long stretches of time, while reducing taxes and transaction costs.

The Magic Formula 

Joel Greenblatt sought out a systematic quantitative method to find companies that are simultaneously cheap and earn high returns on capital. The result was The Little Book that Beats the Market. In the book, Greenblatt demonstrated that simultaneously buying cheap companies that earn high returns on invested capital will outperform. He calls this the magic formula and generously maintains a free screener here.

Tobias Carlisle took this a step further in Deep Value and discovered that the quality metric of high returns on invested capital actually reduces returns from the magic formula. He explains in Deep Value how mean reversion tends to bring these returns down. Cheap alone is better than cheap plus good. In other words, it takes qualitative insight to determine which companies have a moat that will allow them to sustain high returns on capital. Tobias maintains a free large cap screen for this here.

I would recommend reading both The Little Book That Beats the Market and Deep Value.

Backtesting Quality Metrics

I decided to test the returns for myself and try to see which “quality” metric works best when combined with a value factor. The test I ran is limited to Russell 3000 components. My definitions of cheapness were:

  • EBITDA/Enterprise Value is higher than 20%
  • Price to free cash flow is less than 15
  • Price to sales is less than 1
  • Earnings Yield is over 10% (i.e., P/E is less than 10)
  • Price to book less than 1
  • Price to tangible book less than 1

In addition to examining metrics that define high returns on capital, I also included metrics for financial quality, such as the debt to equity ratio and the Piotroski F-Score. Below is a summary of all of the quality metrics that I tested.

Return on Equity – This is the oldest and most simple method of corporate quality. It is simply the company’s net income divided by equity (assets – liabilities). For the purposes of the test, I define high ROE as over 20%.

Return on Invested Capital – Joel Greenblatt’s preferred measure of quality. This is earnings before interest and taxes divided by invested capital. Invested capital is defined as working capital plus net fixed assets. For the purposes of the test, I define high ROIC as over 15%.

Gross Profits/Assets – Robert Novy-Marx created a very simplistic measure of quality, gross profits/assets. He found that this  method works extremely well, because it uses profits further up of the income statement where it is more difficult for a firm to manipulate the numbers. You can read his paper on the subject here. For the purposes of the test, I define good gross profitability as over 30%.

Debt/Equity – This is another simplistic measure of financial quality. It is simply total debt divided by equity (assets-liabilities). For the purposes of the test, I define a good debt/equity ratio as under 50%.

F-Score – This is a more complex measure of financial quality designed by Joseph Piotroski, who is currently a professor at Stanford. Piotroski designed a 9 point scale of financial quality in a paper written back in 2000. Piotroski backtested combining this measure of financial quality with price to book and found that the results greatly exceeded the market. Each component adds to the score. A perfect F-Score would be a 9. It’s too bad F-Scores don’t go up to eleven. The components of the F-Score are defined below.

  • A net decline in long-term debt for the current year.
  • A net increase in the current ratio in the current year. The current ratio is current assets/current liabilities. It measures the liquidity of the company’s balance sheet to meet short-term obligations.
  • A positive increase in gross margins in the current year.
  • Faster asset turnover in the current year.
  • The total number of shares outstanding is flat or decreasing. In other words, the company isn’t issuing new equity and diluting the current pool of shares.
  • Return on assets is positive.
  • Operating cash flow is positive.
  • Return on assets for the current year is higher than the previous year.
  • Operating Cash Flow/Total Assets is higher than return on assets.

The results of the backtest are below. The results are in the Russell 3000 universe with annual rebalancing beginning in 1999.


The High Return Metrics – ROE, ROIC, GP/Assets

Measures for returns on capital – ROE, ROIC and GP/Assets – actually detract from the performance of EBITDA/Enterprise Value. They add performance to the other valuation metrics slightly, with Gross Profits/Assets being the best.

The middling performance of high return quality metrics is due to mean reversion, or the propensity for high return businesses to eventually succumb to the pressures of competition.

With that said, if you are trying to identify high return businesses, the best metric to use appears to be Gross Profits/Assets.

Financial Quality (The Debt/Equity Ratio and the F-Score)

In contrast, measures of financial quality, such as the debt/equity ratio and the F-Score, supercharge all of the valuation metrics that are examined here. Why is this?

Cheap stocks are only cheap because they are in some kind of trouble. There is an “ick” factor. Any time you run a value screen, you will scratch your head and think “Do I really want to invest in this garbage?”

This is why financial quality metrics are more useful than business quality metrics. If a company has a good balance sheet and is financially healthy then it has time to resolve its problems. Managers have time to implement a new strategy that can turn things around. Even without a new strategy, time will help the financially healthy company. For instance, if the company is in a crowded, competitive industry, the financially healthy company can weather the storm while the highly leveraged firms will go out of business first. Less firms means less competition. Less competition means that future returns in the industry will improve.

This is the essence of the simple Graham method that I follow with my own portfolio. I am looking for cheap companies that have the financial ability to weather the storm that they’re in.

This is also why the high return metrics add a little to the other valuation metrics but detract from the EBITDA yield. Unlike the other valuation metrics tested here, the EBITDA yield is the only one here that uses Enterprise Value in the calculation. Enterprise Value brings balance sheet health into the valuation ratio. For EBITDA/Enterprise Value to result in a high yield, the company must have little debt and a lot of cash on hand. In other words, valuation metrics that use Enterprise Value will identify companies that are both cheap and have safe balance sheets.

RadioShack vs. Best Buy

This reminds me of an article I read over at the Motley Fool. The author explains why Radio Shack fell apart and Best Buy was able to turn around.

Radio Shack had numerous problems, including asking for your phone number when you buy batteries.

Best Buy’s problem is that it basically became a showroom for Amazon. The referenced article explain how Best Buy successfully turned things around by cutting costs, emulating the Apple Store, expanding the Geek Squad and improving their website.

I have a more simplistic explanation for why Best Buy was able to recover and Radio Shack fell apart. Radio Shack had a lot of debt and Best Buy didn’t. Radio Shack’s debt to equity ratio was over 670%. Best Buy’s debt to equity ratio was 41% a few years ago and is 29% today.

Best Buy’s balance sheet gave them an edge: time. They had time to work through their problems and try to find a solution. If Best Buy had Radio Shack’s debt levels, the CEO would have never been able to pursue the turnaround strategy. All troubled companies are trying to turn things around, but only those with financial strength will have the time to do so.

Screening for High F-Scores and EBITDA Yield

One of the most robust combinations tested was the F Score and the EBITDA Yield, with a 17.69% rate of return since 1999. I ran a screen for companies with an EBITDA yield over 20% and an F-Score of 8 or higher. This combination of criteria is very stringent. It only returned 5 results out of the entire Russell 3000. Best Buy actually comes up in this screen, implying that it is still a financially healthy bargain. Output from the screen is below.


I am not going to buy positions in these companies, but wanted to share the results of what I found. Hopefully this will give you some useful leads that are worthy of further research.

PLEASE NOTE: The information provided on this site is not financial advice and it is for informational and discussion purposes only. Do your own homework. Full disclosure: my current holdings.  Read the full disclaimer.

How to Overvalue a Company: Use Discounted Cash Flow Analysis


Discounted Cash Flow Rationale

Discounted cash flow (DCF) analysis is the most popular method of business valuation.  It is taught extensively in most finance classes.  The goal is to find a reasonable price for a future stream of cash flows and compare it to a risk-free rate of return, usually US treasuries.

It’s also fraught with peril because it usually results in overvaluing businesses.  It is the preferred method of valuation in investment banking.  I suspect this is because investment bankers can easily game the numbers and make companies appear more valuable than they actually are.  Allow me to explain.

Apple Valuation (AAPL)

To show the power of assumptions, let’s try a real world valuation example.  Let go with Apple (AAPL) using this method.

Free Cash Flow

Let’s start with a fact: in the 2016 fiscal year, Apple’s free cash flow was: $65.844 billion in operating cash flow – $13.548 billion in capital expenditures = $52.296 billion in free cash flow

At the end of the 2016 fiscal year, there were 5.336 billion shares of Apple common stock.

$52.296/5.336 = $9.80 of free cash flow per share of Apple stock.

So what’s the value of $9.80 in discounted cash flow?  Let’s use DCF analysis to figure it out.

Zero Growth Example

For example 1, let’s take an extreme approach.  Let’s say Apple won’t grow at all (unlikely).  For the interest rate, we’ll use US treasuries.  The 10-year US treasury currently pays 2.38%.  Here is a link with a detailed explanation of the math.

If you want to do this quickly, Gurufocus has a calculator tool that you can use here.  Another nice shortcut is a formula that can be used in Microsoft Excel or in Google sheets.  Simply input the following formula into a cell:

=NPV(discount rate, cash flow 1, cash flow 2, etc.)

Now, let’s try to find the present value of a $9.80 stream of cash flows.  Based on no growth and 10 years of cash flows and using the 2.38% rate, we get a value for Apple of $86.30.

Growth and Different Discount Rates

2% Growth, 2.38% rate, 10 years = $96.02

What if instead of using the 10 year treasury as our base, we used the 10 year average AAA corporate bond yield, 2.96%

2% Growth, 2.96% rate, 10 years = $93.11

Terminal value

Most likely, Apple isn’t going to go out of business in 10 years.  For this reason, most DCF analysis adds a terminal value to the value after the 10 years of cash flows.  Let’s proceed with our assumption that there will be 2% growth for 10 years, then let’s say after 10 years the growth rate drops to 1%.

Present value of 10 years of cash flows + Terminal Value = $173.52

Now, what if we increased our assumptions?  Let’s say Apple grows by 5% a year, and then the terminal value grows earnings at 3% into the future?  Now the value goes up to $228.54!

With DCF analysis, you can make the data say whatever you want.  That’s great for investment bankers but it’s not very good for investors.


By messing around with different assumptions, I produced valuations for Apple that ranged from $86.30 to $228.54.  All of these assumptions are debatable.  You can’t say with any degree of certainty where interest rates are going, what Apple’s cost of capital will be, what their growth rate will be, how long the business will be viable, etc.  All of these are assumptions.  Also keep in mind that I produced this wide range of values with one of the the largest and most recognizable company in the United States.  If we can’t safely value Apple, how can we safely value a micro-cap stock?

For this reason, I avoid discounted cash flow analysis.  It is simply too easy to twist around the data with your assumptions and get the result you want.  If you want to find a margin of safety with DCF analysis, you’re going to find one.  I suspect that this is what the investment banking community does when they want to convince corporate managers to make acquisitions that may not be in the best interests of the acquirer.

A simple ratio (i.e., the stock trades at 10 times earnings) is a far more simplistic . . . and far more telling . . . statistic than DCF analysis.  The cheapness of something should hit you over the head and should be abundantly obvious.  If it’s not, move onto something else.  There are plenty of publicly traded companies.  Torturing the data to get the result you want is not a prudent path.

I prefer the Graham approach and focus on what’s actually known in the here and now without making so many assumptions about the future.  This is why the Grahamian balance sheet approach (because what’s more clear cut than the value of a balance sheet?) of net-net’s is a nearly foolproof method of investment.

For the goal of finding the present value of cash flows in analysis of stocks, I think a more useful metric is one that is more simple: price to free cash flow or enterprise value to free cash flow. If you can find a decent company like Apple trading at a price to free cash flow of 10 or less, then DCF analysis would likely yield a number close to the current price even with very conservative assumptions. It is probably then a good candidate worthy further research.

In the world of finance, there is a tendency to make things more complicated than they really are.  I like to keep things simple.

PLEASE NOTE: The information provided on this site is not financial advice and I am not a financial professional. I am an amateur and the purpose of this site is to simply monitor my successes and failures.  Full disclosure: my current holdings.

What is the Best Stock Valuation Ratio?


Value investors use a number of ratios to assess whether a stock is cheap.  Everyone has their favorite.  Everyone debates the merits of one versus the other.  I backtested some of the popular ratios to see how they would perform if you simply split the market up into deciles and compared the cheapest to the most expensive deciles.  The population I used for this analysis was the S&P 1500.  In this case, we are comparing the most expensive 150 stocks to the cheapest 150 stocks.  These are the total returns since 1999, with the 150 stocks re-balanced annually, because re-balancing monthly is impractical.

Below is a list of the ratios that I tested:

EBIT/Enterprise Value – This is the ratio identified by Joel Greenblatt in The Little Book that Beats the Market.  EBIT is “earnings before interest and taxes”.  Tobias Carlisle refers to this as the “Acquirer’s Multiple” in Deep Value

The Enterprise Value is the total cost of the firm to an acquirer.  Enterprise values are the total cost of the firm to an acquirer at the current market price.  In other words, if you were to buy this company in its entirety, you wouldn’t simply pay the market price.  You would also assume all of its debt obligations and would inherit all of its cash on hand.  It is the cost to acquire the entire company.  EV gives you a good idea of the true size of the business.  The calculation for Enteprise Value is:

EV = market value of common stock + market value of preferred equity + market value of debt + minority interest – cash and investments.

Price/Cash Flow – The price per share divided by the total trailing twelve month cash flow for the last calendar year.  In other words, how much total cash is the stock generating for what you are paying?

Price/Sales – The price per share divided by the total revenue per share.  This ratio was popularized by Kenneth Fisher in his 1984 book Super Stocks.

Price/Free Cash Flow – Free cash flow is the company’s operating income minus its capital expenditures.  Free cash flow strips away the company’s other financial performance variables and looks simply at how the core business is doing.  This ratio looks at how much free cash flow is being generated per share relative to the price of the stock.

Free Cash Flow/Enterprise Value – This is the same thing as price/free cash flow, but instead compares free cash flow to the total size of the business.

Price/Book Value – Book value is the total balance sheet value of the company.  It’s the simple equation Assets – Liabilities = Shareholder Equity.  The goal of many asset-based value investors is to buy company’s that are trading at or below book value.

Price/Earnings – This is the most basic and common valuation metric.  It takes the per share price of the stock and divides it by the earnings per share.

Price/Tangible Book Value – The same thing as price/book value, with a twist.  When calculating shareholder equity, intangible assets are taken out of total assets.  The goal here is to look at what assets can actually be sold and turned into cash.

The results are below:


I think it is best to look at the effectiveness of the ratio based on the difference in return between the cheapest and most expensive decile, rather than looking at its total return for the cheapest decile.  A ratio that is effective in identifying cheap stocks should be equally effective in identifying expensive stocks.  Based on the backtesting, the acquirer’s multiple popularized by Tobias Carlisle is the most effective.  Tobias maintains a nice screener here.

Here is a visualization of the value premium in chart form:


The important takeaway is that no matter which ratio you prefer, they all work to some extent and buying expensive stocks is a risky bet.  Value investors can debate about which ratio works best, but they all work!  No matter how you slice it, cheap beats expensive.

PLEASE NOTE: The information provided on this site is not financial advice and it is for informational and discussion purposes only. Do your own homework. Full disclosure: my current holdings.  Read the full disclaimer.