I created a free page with discounts, promos, and free plans for investment tools.

It’s updated every week, currently has 30+ deals, and some of them are temporary, so you have to be quick.

It’s 100% free, so I thought I’d share it in case it’s useful.

Some affiliate links are included, but they’re clearly marked.

Website: https://www.findmymoat.com/deals

Anything else I should include?

https://preview.redd.it/v82tg5wlklbh1.png?width=2242&format=png&auto=webp&s=11665a6791c5ded6d6bb0cb1299a83d602676c41

He escrito una continuación del post sobre diversificación que compartí la semana pasada, ya que a mucha gente pareció gustarle.

La idea anterior era básicamente que diversificar no consiste en tener más acciones. Consiste en tener riesgos diferentes.

Este es un poco más práctico.

He analizado 5 estrategias alternativas (y más desconocidas) que pueden comportarse de forma distinta al stock picking tradicional:

• Trend following
• Carry
• Merger arbitrage
• Cat bonds
• Macro relative value

La idea no es que sean automáticamente buenas inversiones, cada una tiene sus propios problemas (son difíciles de acceder, caras, apalancadas, o lo que sea)

Pero creo que son útiles de estudiar porque cada una cobra por algo distinto y es “fundamentalmente diferente” en muchos sentidos.

• Trend following cobra cuando las tendencias se mantienen.
• Carry cobra por asumir riesgos que nadie quiere.
• Merger arbitrage cobra por asumir riesgo de que una operación corporativa salga bien.
• Cat bonds cobra por asumir riesgo de catástrofes naturales.
• Macro relative value cobra cuando las relaciones entre activos se normalizan.

Creo que es bastante interesante ver algo "fresco" y distinto de la típica estrategia de “buy & hold” o de la típica cartera de acciones/bonos.

dejo el artículo por aquí por si a alguien le interesa:
https://www.jeravalue.com/es/blog/return-engines

también tengo curiosidad por ver qué opinión hay por aquí sobre estrategias así, y qué otras buenas estrategias me estoy dejando fuera

https://preview.redd.it/0lcvlc0e609h1.png?width=1754&format=png&auto=webp&s=798b188160a12a3856590ac8626f3372d795dc5b

I wrote up a continuation of the diversification post I shared last week, since many people seemed to like that one.

The previous idea was basically: diversification is not about owning more stocks. It is about owning different risks.

This one is a bit more practical.

I looked at 5 alternative strategies that can behave differently from normal stock picking:

• Trend following
• Carry
• Merger arbitrage
• Cat bonds
• Macro relative value

The point is not that these are automatically good investments. Some are hard to access, expensive, leveraged, or whatever.

But I think they are useful to study because each one is paid for something different and is “fundamentally different” in many ways.

• Trend following is paid when trends persist.
• Carry is paid for holding risk nobody wants.
• Merger arbitrage is paid for deal completion risk.
• Cat bonds are paid for insurance catastrophe risk.
• Macro relative value is paid when relationships between assets normalize.

I think it is quite interesting to see something fresh and different from the usual “buy & hold” strategy or the typical stock/bond portfolio.

wrote it up here if anyone’s interested:
https://www.jeravalue.com/en/blog/return-engines

i’m also curious to see how people here think about these strategies, and what good ones I might be missing.

https://preview.redd.it/53ye2cta409h1.png?width=1770&format=png&auto=webp&s=8b02bac710abe03418a0f746b85e35105796b6f1

I wrote up a continuation of the diversification post I shared last week, since many people seemed to like that one.

The previous idea was basically: diversification is not about owning more stocks. It is about owning different risks.

This one is a bit more practical.

I looked at 5 alternative strategies that can behave differently from normal stock picking:

• Trend following
• Carry
• Merger arbitrage
• Cat bonds
• Macro relative value

The point is not that these are automatically good investments. Some are hard to access, expensive, leveraged, or whatever.

But I think they are useful to study because each one is paid for something different and is “fundamentally different” in many ways.

• Trend following is paid when trends persist.
• Carry is paid for holding risk nobody wants.
• Merger arbitrage is paid for deal completion risk.
• Cat bonds are paid for insurance catastrophe risk.
• Macro relative value is paid when relationships between assets normalize.

I think it is quite interesting to see something fresh and different from the usual “buy & hold” strategy or the typical stock/bond portfolio.

wrote it up here if anyone’s interested:
https://www.jeravalue.com/en/blog/return-engines

i’m also curious to see how people here think about these strategies, and what good ones I might be missing.

https://preview.redd.it/53ye2cta409h1.png?width=1770&format=png&auto=webp&s=8b02bac710abe03418a0f746b85e35105796b6f1

I wrote up a continuation of the diversification post I shared last week, since many people seemed to like that one.

The previous idea was basically: diversification is not about owning more stocks. It is about owning different risks.

This one is a bit more practical.

I looked at 5 alternative strategies that can behave differently from normal stock picking:

• Trend following
• Carry
• Merger arbitrage
• Cat bonds
• Macro relative value

The point is not that these are automatically good investments. Some are hard to access, expensive, leveraged, or whatever.

But I think they are useful to study because each one is paid for something different and is “fundamentally different” in many ways.

• Trend following is paid when trends persist.
• Carry is paid for holding risk nobody wants.
• Merger arbitrage is paid for deal completion risk.
• Cat bonds are paid for insurance catastrophe risk.
• Macro relative value is paid when relationships between assets normalize.

I think it is quite interesting to see something fresh and different from the usual “buy & hold” strategy or the typical stock/bond portfolio.

wrote it up here if anyone’s interested:
https://www.jeravalue.com/en/blog/return-engines

i’m also curious to see how people here think about these strategies, and what good ones I might be missing.

https://preview.redd.it/53ye2cta409h1.png?width=1770&format=png&auto=webp&s=8b02bac710abe03418a0f746b85e35105796b6f1

hey, I’ve been thinking a lot about the diversification vs concentration debate.

The discussion usually gets stuck between “own 20-25 stocks and you’re diversified” and “just concentrate in your best ideas,” which feels too simplistic.

So I wrote up a piece trying to separate the different reasons investors diversify.

The main idea is that diversification is not really about counting positions. It is about counting risks.

Two portfolios can both own 10 stocks, but one can be genuinely diversified while the other is just one economic bet repeated 10 times.

I also tried to connect it with expected value, position sizing, Kelly, and compounding.

The part I find most interesting is that diversification does not magically increase expected value. If you buy bad investments, owning more of them just means losing money more smoothly.

What diversification can do is change the distribution of outcomes: reduce the chance of large simultaneous losses, reduce dependence on one scenario, and help capital compound without getting hit too hard by one bad assumption.

I also added some simple examples and charts showing how two portfolios can have the same expected value but very different long-term compound results.

wrote it up here if anyone’s interested: https://www.jeravalue.com/en/blog/diversification

https://preview.redd.it/5zkyfssv9m7h1.png?width=1378&format=png&auto=webp&s=46a9618e7dba600c93f09dd6e4e06014a98f921b

hey, I’ve been thinking a lot about the diversification vs concentration debate.

The discussion usually gets stuck between “own 20-25 stocks and you’re diversified” and “just concentrate in your best ideas,” which feels too simplistic.

So I wrote up a piece trying to separate the different reasons investors diversify.

The main idea is that diversification is not really about counting positions. It is about counting risks.

Two portfolios can both own 10 stocks, but one can be genuinely diversified while the other is just one economic bet repeated 10 times.

I also tried to connect it with expected value, position sizing, Kelly, and compounding.

The part I find most interesting is that diversification does not magically increase expected value. If you buy bad investments, owning more of them just means losing money more smoothly.

What diversification can do is change the distribution of outcomes: reduce the chance of large simultaneous losses, reduce dependence on one scenario, and help capital compound without getting hit too hard by one bad assumption.

I also added some simple examples and charts showing how two portfolios can have the same expected value but very different long-term compound results.

wrote it up here if anyone’s interested: https://www.jeravalue.com/en/blog/diversification

https://preview.redd.it/5zkyfssv9m7h1.png?width=1378&format=png&auto=webp&s=46a9618e7dba600c93f09dd6e4e06014a98f921b

hey, I’ve been thinking a lot about the diversification vs concentration debate.

The discussion usually gets stuck between “own 20-25 stocks and you’re diversified” and “just concentrate in your best ideas,” which feels too simplistic.

So I wrote up a piece trying to separate the different reasons investors diversify.

The main idea is that diversification is not really about counting positions. It is about counting risks.

Two portfolios can both own 10 stocks, but one can be genuinely diversified while the other is just one economic bet repeated 10 times.

I also tried to connect it with expected value, position sizing, Kelly, and compounding.

The part I find most interesting is that diversification does not magically increase expected value. If you buy bad investments, owning more of them just means losing money more smoothly.

What diversification can do is change the distribution of outcomes: reduce the chance of large simultaneous losses, reduce dependence on one scenario, and help capital compound without getting hit too hard by one bad assumption.

I also added some simple examples and charts showing how two portfolios can have the same expected value but very different long-term compound results.

wrote it up here if anyone’s interested: https://www.jeravalue.com/en/blog/diversification

https://preview.redd.it/5zkyfssv9m7h1.png?width=1378&format=png&auto=webp&s=46a9618e7dba600c93f09dd6e4e06014a98f921b

La diversificación es uno de los conceptos peor entendidos en finanzas.

¿Debería diversificar la cartera? ¿Debería concentrar? Si diversifico, ¿cuántas empresas hacen falta para protegerme?

Un lado del debate cita a Munger sobre las virtudes de las carteras concentradas. El otro cita estudios sobre las ventajas de la diversificación. Pero muchas veces ambos hablan de cosas distintas.

La pregunta "¿cuántas acciones debería tener en cartera?" está mal planteada. Dos carteras con 10 posiciones pueden tener riesgos completamente distintos: una puede estar diversificada de verdad y otra puede ser una única apuesta disfrazada.

Lo importante no es el número de posiciones, sino qué riesgos estás repitiendo, cuánto capital estás poniendo en cada uno y qué coste de oportunidad asumes al no poner más dinero en tus mejores ideas.

Para no perdernos, la idea central del artículo es esta: diversificar bien no consiste en añadir posiciones. Consiste en reducir la dependencia de un mismo escenario negativo sin sacrificar demasiado valor esperado.

En concreto, voy a separar tres cosas que muchas veces se mezclan:

1. Una cartera con más acciones no siempre está más diversificada.
2. Una buena idea no siempre merece una posición enorme.
3. La mejor cartera no es necesariamente la suma de tus mejores ideas individuales.

Mi preocupación viene, sobre todo, de una gráfica que muchos conocéis y que he visto repetida hasta la saciedad:

https://preview.redd.it/0avra0117m7h1.png?width=3072&format=png&auto=webp&s=895f1bb1259d367cf03146f667322c789de51178

Esta gráfica se suele presentar como el "santo grial" de la diversificación: la prueba definitiva de que deberías llevar de 20 a 25 empresas en cartera.

La gráfica no está mal. El problema es usarla para demostrar algo que no demuestra.

Muchos value investors rechazan la beta y la volatilidad como medidas completas de riesgo, pero luego defienden la diversificación con una gráfica que mide precisamente volatilidad.

Esa gráfica no mide riesgo de pérdida permanente. No mide riesgo de pagar demasiado. No mide fraude, deterioro competitivo, mala asignación de capital o error de análisis. Mide desviación estándar de los retornos. Y eso está bien.

Pero si tu premisa es que “volatilidad no es riesgo”, entonces no puedes usar una gráfica de volatilidad como prueba absoluta de que una cartera de 25 acciones es menos arriesgada que una de 8. Puedes defender la diversificación, claro. Pero tendrás que hacerlo con otro argumento.

Esa confusión es el motivo de este artículo: explicar la diversificación sin esconderse detrás de una gráfica de volatilidad.

Por otra parte, mucha gente piensa que en un mundo en el que el riesgo es perfectamente conocido buscarías la oportunidad con mejor retorno/riesgo y apostarías el 100% de tu capital a esa única baza. Es decir, piensan que tener varias acciones en cartera solo es útil como protección contra la ignorancia. Que si conocieses perfectamente el retorno/riesgo asumido, la cartera óptima estaría formada por una única empresa.

Esto también es falso.

Hay un motivo menos intuitivo para tener más de una posición: incluso cuando una oportunidad tiene un valor esperado altísimo, puede ser óptimo asignarle solo una fracción pequeña del capital. No por ignorancia, sino por supervivencia y por maximizar la rentabilidad a largo plazo.

Por si alguno está interesado he escrito un artículo bastante completo sobre el tema en: https://www.jeravalue.com/es/blog/diversification

La diversificación es uno de los conceptos peor entendidos en finanzas.

¿Debería diversificar la cartera? ¿Debería concentrar? Si diversifico, ¿cuántas empresas hacen falta para protegerme?

Un lado del debate cita a Munger sobre las virtudes de las carteras concentradas. El otro cita estudios sobre las ventajas de la diversificación. Pero muchas veces ambos hablan de cosas distintas.

La pregunta "¿cuántas acciones debería tener en cartera?" está mal planteada. Dos carteras con 10 posiciones pueden tener riesgos completamente distintos: una puede estar diversificada de verdad y otra puede ser una única apuesta disfrazada.

Lo importante no es el número de posiciones, sino qué riesgos estás repitiendo, cuánto capital estás poniendo en cada uno y qué coste de oportunidad asumes al no poner más dinero en tus mejores ideas.

Para no perdernos, la idea central del artículo es esta: diversificar bien no consiste en añadir posiciones. Consiste en reducir la dependencia de un mismo escenario negativo sin sacrificar demasiado valor esperado.

En concreto, voy a separar tres cosas que muchas veces se mezclan:

1. Una cartera con más acciones no siempre está más diversificada.
2. Una buena idea no siempre merece una posición enorme.
3. La mejor cartera no es necesariamente la suma de tus mejores ideas individuales.

Mi preocupación viene, sobre todo, de una gráfica que muchos conocéis y que he visto repetida hasta la saciedad:

https://preview.redd.it/0avra0117m7h1.png?width=3072&format=png&auto=webp&s=895f1bb1259d367cf03146f667322c789de51178

Esta gráfica se suele presentar como el "santo grial" de la diversificación: la prueba definitiva de que deberías llevar de 20 a 25 empresas en cartera.

La gráfica no está mal. El problema es usarla para demostrar algo que no demuestra.

Muchos value investors rechazan la beta y la volatilidad como medidas completas de riesgo, pero luego defienden la diversificación con una gráfica que mide precisamente volatilidad.

Esa gráfica no mide riesgo de pérdida permanente. No mide riesgo de pagar demasiado. No mide fraude, deterioro competitivo, mala asignación de capital o error de análisis. Mide desviación estándar de los retornos. Y eso está bien.

Pero si tu premisa es que “volatilidad no es riesgo”, entonces no puedes usar una gráfica de volatilidad como prueba absoluta de que una cartera de 25 acciones es menos arriesgada que una de 8. Puedes defender la diversificación, claro. Pero tendrás que hacerlo con otro argumento.

Esa confusión es el motivo de este artículo: explicar la diversificación sin esconderse detrás de una gráfica de volatilidad.

Por otra parte, mucha gente piensa que en un mundo en el que el riesgo es perfectamente conocido buscarías la oportunidad con mejor retorno/riesgo y apostarías el 100% de tu capital a esa única baza. Es decir, piensan que tener varias acciones en cartera solo es útil como protección contra la ignorancia. Que si conocieses perfectamente el retorno/riesgo asumido, la cartera óptima estaría formada por una única empresa.

Esto también es falso.

Hay un motivo menos intuitivo para tener más de una posición: incluso cuando una oportunidad tiene un valor esperado altísimo, puede ser óptimo asignarle solo una fracción pequeña del capital. No por ignorancia, sino por supervivencia y por maximizar la rentabilidad a largo plazo.

Por si alguno está interesado he escrito un artículo bastante completo sobre el tema en mi blog.

Os dejo un pequeño adelanto 👀

https://preview.redd.it/ewifa1nn8m7h1.png?width=1504&format=png&auto=webp&s=d61238eb8730523c93b6264de2e1c584552cd6df

https://preview.redd.it/13c6vnuo8m7h1.png?width=1476&format=png&auto=webp&s=678a4e1f8dae0a181807308afb1cbbcddc5de266

https://preview.redd.it/5td4q2oq8m7h1.png?width=1470&format=png&auto=webp&s=d86a5341235e6545c33bd5620a536926bb52c21e

Buenas, últimamente he estado leyendo bastante debate sobre las ineficiencias estructurales del mercado.

Muchas veces la conversación se va a dos extremos: “los mercados son eficientes” o “compra value/momentum/low beta y ya está”. Y creo que la realidad es bastante más interesante.

Así que he juntado una lista de las anomalías que más se repiten: value, momentum, low beta, small caps, quality, accruals, shareholder yield, etc.

Lo que más me interesaba no era solo explicar cuáles han funcionado históricamente, sino por qué pueden seguir existiendo aunque sean conocidas.

Algunas vienen de sesgos humanos. Otras de incentivos institucionales, benchmarks, riesgo de carrera, liquidez o simple sobrerreacción. Y otras solo tienen sentido cuando se combinan con otros factores.

También he añadido gráficos interactivos para explorar los datos históricos y comparar cómo se han comportado los distintos factores a lo largo del tiempo.

Lo dejo aquí por si a alguien le apetece echarle un ojo: https://www.jeravalue.com/es/blog/market-inefficiencies

https://preview.redd.it/r2e54r2iba6h1.png?width=1606&format=png&auto=webp&s=6bde0dcfde3d293475d1cff6d4ab12fd7ce44939

There is no free lunch.

Or is there?

Investors have been told many times that the market is more or less efficient. That there may be occasional mistakes, sure, but they usually get corrected quickly, and exploiting them systematically is very hard. In the abstract, the idea sounds reasonable. If everyone is looking for free money, the normal thing is that the free money disappears.

And yet, the market does not work exactly like that.

Some inefficiencies do not disappear entirely because they do not depend only on someone discovering them. They depend on human biases, institutional constraints, misaligned incentives, liquidity problems, implementation costs, and something much simpler: many strategies are psychologically devastating even when they work.

In other words, these are not inefficiencies that will vanish overnight. They are structural inefficiencies.

You already know many of them. Value, momentum, small caps, illiquidity, quality, buybacks. I am not discovering America here. But maybe you have not stopped to think about why they still exist, how they combine with each other, and above all, what they can add to a real investment process.

But careful: contrary to what it may look like, these factors are not a shopping list.

Just because a company scores well on a factor does not mean it will go up. Just because it is cheap does not mean it is a bargain. Just because it has momentum does not mean it is good. Just because it is high quality does not mean any price is justified. We are talking about patterns that work in aggregate, not an algorithm for picking individual companies.

That said, understanding them helps a lot. It lets you know what kind of tailwind you have, what risks you are taking without realizing it, and what traps are worth avoiding. So here are 12 structural inefficiencies that, used well, can meaningfully improve any investment process.

https://preview.redd.it/kra1e8z18a6h1.png?width=1418&format=png&auto=webp&s=ff610f738599f8925a0e5e74a8da18e776315b34

Value

The best-known one.

The academic definition of value does not always match the one used by old-school value investors. Academics talk about buying companies that are optically cheap, meaning cheap relative to earnings, book value, sales, FCF, EBITDA, assets, or any other reasonable valuation metric. In other words, companies trading at low multiples relative to their fundamentals.

It can look like a fairly crude way to value businesses. And very often it is. A single multiple tells you almost nothing about a specific company. But in aggregate, historically, companies that are cheap on multiples have tended to outperform expensive companies. We usually call the first group value; the second, glamour.

And here is the good part. The interesting question is: if everyone knows that buying cheap makes sense, why does it still work?

Because people love convincing stories and hate companies that stink.

The market tends to overpay for companies with a promising narrative, visible growth, and an earnings presentation that says all the right things. At the same time, it overpunishes boring, cyclical, dirty, disliked companies that are simply uncomfortable to own.

Fama and French formalized this effect using book-to-market, because it was a fairly stable metric over time. Then the world changed, less tangible-asset-heavy business models appeared, like software, and that metric lost part of its power. That is why many other valuation measures are used today. You may also have heard of HML, high minus low, which is basically a factor built as a strategy that buys value or cheap stocks and sells expensive stocks; its return measures the difference between the two.

Now, careful.

The fact that the value factor works does not mean buying anything at a P/E of 2 is a good idea. Many companies are cheap for a perfectly reasonable reason: because they are bad, because the business is deteriorating, because the accounting is misleading, or because capital will be destroyed before you can get paid.

That is the big problem with value: value traps. Optically cheap companies that are not actually cheap, but rather garbage businesses with justified low multiples.

Also, value can have brutally long periods of pain. You can spend years looking like an idiot while the market pays absurd multiples for growth companies. You can bleed slowly against the index and, right when everyone decides value is dead, get a brutal rotation that recovers years of underperformance in a few months.

It works, yes, but it is not glamorous. You look like an idiot most of the time, and it is a bet that is hard to hold for years.

And that, precisely, helps explain why it still exists.

https://preview.redd.it/yc508qi48a6h1.png?width=1412&format=png&auto=webp&s=0f5077ef31712bc1125c408bb682c0993ea2751d

https://preview.redd.it/z04c6tc68a6h1.png?width=1560&format=png&auto=webp&s=065610bbbb411f56211ec5746cc288549f118e66

Price momentum

Price momentum is one of the most hated factors among many value investors and one of the most loved by technical investors. The idea is to buy the stocks that have done best over the last 6-12 months, usually skipping the most recent month to avoid short-term noise and reversal.

Fundamental investors often look down on it because it is based only on price.

And of course, if you understand the stock market from a business-owner point of view, this does not quite fit your mental model. How can price tell you anything useful while ignoring earnings, margins, debt, returns on capital, or anything that looks remotely fundamental?

Well, it does.

The market takes time to incorporate information. Analysts adjust estimates gradually. Flows chase winners. Narratives build in layers. And prices, whether we like it or not, sometimes start reflecting changes in expectations before those changes become obvious in the numbers.

Also, investors are not robots. We struggle to sell winners, we chase what is going up, we extrapolate trends, and we suffer from FOMO with embarrassing ease. Momentum does not work because the market is "dumb" in some simplistic way; it works because price discovery is slow, social, and emotional.

The problem is obvious: in euphoric phases, momentum can push you into speculative garbage. Companies with no fundamentals, no cash, no profits, and a wonderful story that only needs 17 more funding rounds to become real. And when the market turns, the famous momentum crashes arrive: violent drops that can evaporate a large part of the accumulated return.

It is a very powerful factor, but it is not free.

https://preview.redd.it/6ic518i98a6h1.png?width=1566&format=png&auto=webp&s=63568f3efc9665790558e48fbae290111f09ac6f

Value + momentum

Value and momentum get along better than it seems.

In fact, they tend to be negatively correlated. Value buys what is hated; momentum buys what is rewarded. When the market is in love with growth and value suffers, momentum often helps. When momentum crashes because losers rebound, value often has more exposure to those cheap or cyclical losers that bounce hard.

In practical terms: momentum can help you avoid value traps, and value can cushion part of momentum crashes.

That is why, even if Warren Buffett himself attended your christening, you should understand momentum. You do not have to become a trader or start drawing lines on a chart. It is enough to recognize that price contains information, and that buying cheap while everything keeps getting worse is usually not optimal.

Fundamental momentum

This is the fundamental cousin of price momentum.

Here you are not buying because the stock has gone up, but because expectations about the business are improving. Upward estimate revisions, positive earnings surprises, better guidance, margin expansion, revenue acceleration, debt coming down faster than expected. Things that indicate the business is doing better than the market believed.

The inefficiency exists because the market rarely adjusts everything at once.

One analyst raises estimates. Then another. Then the company beats again. Then the market starts believing the story. Then a new narrative appears. And meanwhile, the price incorporates that improvement in phases.

The classic case is PEAD, post-earnings announcement drift. After a positive earnings surprise, the stock tends to keep rising for a while. After a negative surprise, it tends to keep falling. In theory, the market should adjust the price almost instantly on earnings day. In practice, it often does not fully do so.

This factor is especially useful for value investors, because it helps distinguish between cheap companies that are starting to improve and cheap companies that are still digging downward. If the fundamentals keep deteriorating, maybe you are not buying a bargain; maybe you are buying trash.

https://preview.redd.it/yenbibed8a6h1.png?width=1568&format=png&auto=webp&s=4686f8d3edae946139d3c2dc50408d38acdbd540

Profitability

Business profitability is one of those ideas that seems too obvious to be an inefficiency. It is also one of the main metrics behind "Quality" companies.

Everyone wants companies with good margins, high returns on capital, recurring profits, and the ability to turn sales into cash. A company that earns a lot on the capital it needs to operate has more options: it can reinvest, buy back shares, pay dividends, withstand bad cycles, and grow without depending as much on external financing.

The question is the same as always: if everyone knows profitable companies are better, should that not already be reflected in the price?

Yes. But often not enough.

The market struggles to value the persistence of profitability. Many companies look expensive if you only look at P/E or EV/EBITDA, but if they maintain high returns for many years, reinvest at good rates, and do not need much incremental capital, they can compound value in a way static multiples do not capture well.

The inefficiency is not "buy any good company at any price." That would be an elegant way to overpay. The idea is that, in aggregate, the market tends to underprice how much it matters that a company earns a lot on its capital and that this profitability is more stable than it looks.

As a filter, profitability combines very well with value. It helps you avoid filling your portfolio with companies that are cheap for horrible reasons. It also combines well with momentum, because it reduces the risk of ending up buying purely speculative companies just because they are going up.

Lovely when factors help each other.

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Conservative investment

This inefficiency is fairly counterintuitive.

You would think that the companies investing the most, expanding assets the most, and chasing growth the hardest should do better. After all, they are investing to grow. Was that not what we wanted?

Not necessarily.

In aggregate, avoiding companies that aggressively expand their asset base, sharply increase capex, issue equity, or chase growth at any price tends to work better. The reason is very mundane: many companies invest badly. They buy expensive assets, build capacity they do not need, enter projects with mediocre returns, or make acquisitions so the CEO can say they run a bigger company.

The market, meanwhile, tends to applaud those expansion signals. More growth, more sales, more geographic presence, more verticals, more acquisitions. It all sounds wonderful until you look at the return on capital.

Reinvestment is necessary to build great businesses, yes. But it only creates value when it is done at attractive returns. This factor helps filter empire builders: companies that grow for the sake of growing without translating that growth into better returns for shareholders.

There is an important nuance. In the post about 10 baggers, we saw that many big winners did invest aggressively and expand their asset base. But they did it with one key difference: that growth was backed by real EBITDA growth. It was not empty expansion, but healthy growth.

The line between quality reinvestment and capital destruction can be thin, but it exists. That can be a topic for another post.

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Accruals / earnings quality

This one is less popular among retail investors, but I find it very useful.

The idea is that it is always better to trust profits that come with cash. If a company earns a lot on paper but converts little into cash flow, it is worth looking twice. Sometimes the difference is normal. Other times it is a sign that those profits are less solid than they look. In the end, what pays debt, dividends, buybacks, and reinvestment is not adjusted EPS; it is cash.

Nobody should be surprised that it pays to distrust companies that report beautiful profits but mediocre cash flows. Nor should anyone be surprised that it pays to distrust companies that live among adjustments, extraordinary items, invented metrics, and accounting reconciliations designed so nobody reads them.

The problem, classically documented by Sloan in 1996, is that the market focuses too much on accounting EPS and does not always distinguish between high-quality earnings and low-quality earnings. Sometimes it punishes a company for an ugly quarter even though cash generation remains intact. Other times it rewards an accounting improvement with no economic backing.

This factor is mainly useful for filtering out the worst. Companies with high accruals, weak cash conversion, and too much accounting creativity. It is not for finding jewels, but for avoiding problems.

And that is not nothing.

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Shareholder yield

Value investors tend to like this factor a lot, and for good reason.

The general idea is to buy companies that return capital to shareholders intelligently and avoid companies that systematically dilute them. Broadly speaking, this can include buybacks, dividends, debt reduction, and share issuance. I have already written quite a bit about buybacks in the introduction to buybacks, the post on the math of buybacks, and the empirical backtest, so there is no need to rehash everything.

But the chart below measures something more specific: net payout yield. That means companies that return capital through dividends and net buybacks, versus companies that issue more equity. It does not measure debt reduction. So it should be read mainly as a signal of capital return through equity and anti-dilution.

The base rate is clear: issuance is usually a bad signal.

Maybe the company thinks its shares are expensive. Maybe it needs to fund losses. Maybe it is paying for acquisitions with stock. Maybe it has no other reasonable way to raise capital. It is not always bad, but as a starting point, it is ugly.

Buybacks, by contrast, can be very good. But only if they are real and done at a good price. Buying back shares at absurd multiples while funding it with debt can destroy value. Buying back shares only to offset stock-based compensation is not exactly returning capital to shareholders either; it is more like running on a treadmill to stay in the same place.

The interesting thing about shareholder yield is that it looks very good in aggregate, even if case by case it is less obvious. Sometimes issuing shares is necessary or even positive. Sometimes a buyback looks reasonable and ends up being bad. And of course, when a company dilutes its shareholders, it usually comes with a wonderful narrative about growth, strategic opportunities, and long-term value creation.

There is always a nice narrative to justify every screw-up.

https://preview.redd.it/4u34zjxo8a6h1.png?width=1568&format=png&auto=webp&s=a33860a13a178288148f99b4b7aeb2c6346639bf

Illiquidity effect

Illiquidity is one of the best-known inefficiencies and one of the few real advantages individual investors have.

Less liquid stocks, less covered by analysts, less present in institutional portfolios, and harder to buy and hold tend to offer better opportunities. The market pays you for bearing friction: wider spreads, lower coverage, less processed information, more difficulty getting in and out, and less capacity to deploy large amounts of capital.

For a huge fund, many of these companies are basically invisible. It cannot buy enough without moving the price, cannot justify the research time, or cannot assume the liquidity risk. For a patient individual investor, by contrast, that same discomfort is an advantage.

This factor fits especially well with long time horizons. If you do not need immediate liquidity, do not use leverage, do not depend on stops, and do not hold positions that are too large, you can afford to look where others do not.

In other words, it is perfect hunting ground for the retail investor.

And, as almost always, it gets more interesting when combined with other factors: illiquidity plus quality, illiquidity plus value, illiquidity plus insider ownership, illiquidity plus a special situation nobody is looking at.

That is where the little gems tend to show up.

https://preview.redd.it/v8bmrmnr8a6h1.png?width=1198&format=png&auto=webp&s=1b21d68b56756f9562ae01a2c97b227eb87e5db5

Anti-lottery / betting against beta

This is an often ignored gem and one of the pieces behind Buffett's success, as we discussed in the post on whether Buffett is really a good investor.

Boring, defensive, less volatile, lower-beta stocks have historically offered better risk-adjusted returns than their more exciting counterparts. This is where BAB, betting against beta, comes in: buying low-beta stocks and selling high-beta stocks.

The explanation makes a lot of sense.

First, investing in boring companies is not sexy. Everyone wants to find the next Tesla, the next NVIDIA before it was NVIDIA, or that stock that can multiply by 100. Lottery-like stocks attract capital because they promise an asymmetric story, even though the price often already embeds too much fantasy.

Second, many investors have leverage constraints. If you cannot use leverage and you want to increase the expected return of your portfolio, a simple way to try is to buy high-beta stocks. That pushes those stocks to prices that are too high and leaves low-beta stocks relatively ignored.

Third, benchmarks distort incentives. A professional manager does not live only by maximizing risk-adjusted return; they live by being compared against an index. A low-vol portfolio may have a better Sharpe, but if it has less beta than the market, it may lag in bull years. And lagging the benchmark is a very reliable way to lose your job.

That is why this structural inefficiency appears: knowing about it is not enough for it to disappear. Many market participants cannot or do not want to exploit it well.

Now, careful.

Low vol is not always cheap. Sometimes these stocks become bond proxies: utilities, staples, REITs, infrastructure, dividend stocks. When everyone wants safety or yield, safety also becomes expensive.

Even boring can get expensive.

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Reversal

Here it is worth separating horizons.

There is short-term reversal: stocks that fall a lot over days or weeks can rebound because of microstructure, liquidity, rebalancing, or overreaction. The problem is that it is usually hard to exploit because trading costs, spreads, and taxes eat a large part of the opportunity.

And there is long-term reversal: stocks that have been losers for 3-5 years can rebound if the market overestimated bad news. This effect fits quite well with the value intuition: the market overpunishes something, the narrative becomes too negative, and when reality stops getting worse, the price adjusts.

Reversal complements and fights momentum depending on the horizon. Over one month there can be reversal. Over 6-12 months there is usually momentum. Over 3-5 years, reversal can appear again.

That is why classic momentum usually skips the most recent month. And that is why value works better when you are not buying a recent fall simply because it looks cheap, but when there is some signal of stabilization.

And this is where much of the debate about catching falling knives comes from.

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Industry momentum

As you can see, momentum keeps showing up in different disguises.

Industry momentum buys winning industries because winning industries tend to keep winning for a while. It is the more macro part of individual-stock momentum and, in fact, can explain a meaningful part of the momentum in many stocks.

Sometimes you think you have found a company with a lot of momentum, but in reality you only bought the right sector. That can be good or bad depending on what you are trying to do.

There are two reasonable ways to use it.

The first is to do sector-neutral momentum: you look for the best names within each sector to reduce macro or sector bets.

The second is to explicitly accept that winning sectors keep winning for a while and incorporate that as a signal. This exposes you more to the cycle, sector narratives, and rotations, but it can also capture very powerful moves.

If you do stock picking, what you need to do is separate "the company is good" from "the whole sector is going up."

They are not the same thesis.

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Small caps / size factor

We leave the most famous factor for last.

The intuitive explanation for the small-cap effect is simple: small companies live in a part of the market with less attention, less capital arbitraging mistakes, more friction, more real risk, and more chaff mixed in with the wheat.

It is the kind of place where, in theory, a patient investor with a strong stomach can find opportunities that do not exist in mega caps covered by 40 analysts.

But the size factor is also one of the most debated and least clean.

There are several important nuances:

1. The effect is not linear and is concentrated mostly in the smallest companies.
2. The classic size effect weakened quite a bit after being discovered.
3. Part of the historical premium seems concentrated in January. In some analyses, the size premium outside January was practically zero.
4. Market equity does measure size directly, but the size premium is not a pure inefficiency like value, profitability, or momentum. It is often mixed with illiquidity, lower coverage, higher business risk, value, beta, sector composition, and a lot of other things.

In a sense, it is not a free inefficiency. It is compensation for putting up with real crap.

Fama and French incorporated it as SMB, small minus big, but the broad small-cap signal without filters is dirty. The small-cap universe is full of bad companies: companies with no profits, high leverage, constant dilution, binary biotechs, mediocre roll-ups, weak cyclical businesses, expensive growth with no FCF, and projects that only exist because someone is still willing to finance them.

The good news is that size matters if you control your junk.

When you filter by quality, profitability, balance sheet, dilution, and other junk signals, the small-cap premium starts to look more interesting. Again, not because all small companies will do better, but because good, underfollowed small companies can sit in an area of the market where important mistakes still exist.

So, does the small-cap effect still exist?

Yes, but not as a pure, clean, automatic factor. The broad version of "small minus big," without filters, is weak, unstable, expensive to implement, and probably contaminated by liquidity, quality, January, beta, and sector composition.

Is it simply a proxy for liquidity?

Partly yes, but not entirely. Liquidity explains much of the raw effect. But when you control for quality or junk, something unique seems to remain: a premium associated with small companies that are better than the market is willing to look at.

That is why small caps are also interesting territory for the individual investor.

https://preview.redd.it/7mol9gr29a6h1.png?width=1570&format=png&auto=webp&s=43d2457896e9518c6ee3c6f3f50044a59f32f767

How to use all this

The tempting thing after reading a list like this is to open a screener, add 14 filters, and think you now have a return-printing machine.

Spoiler: you do not.

The right way to use factors is not as a recipe, but as a map of probabilities. They tell you what type of company tends to have a statistical tailwind and what type of company tends to have a statistical headwind. Then comes the hard part: understanding the specific case you are studying.

For a retail investor, this is especially useful because the scarcest resource is usually not capital, but attention. You cannot analyze 5,000 companies in depth. You need to decide where it is worth spending hours and where it is better to move on quickly. That is where factors help a lot.

The first practical use is cleaning up the universe. If a company looks cheap but dilutes a lot, converts earnings poorly into cash, invests aggressively at mediocre returns, and also has terrible momentum, the burden of proof is extremely high. It can work, of course. But you already know you are fighting several forces at once.

The reverse also works. A small, undercovered, reasonably cheap, profitable company with a good balance sheet, no dilution, and signs of improvement deserves more attention than a beautiful story with only a good narrative. It does not mean buying it. It means it probably deserves a bit of your time to study it.

The second use is combining signals. One factor is a clue. Several factors pointing in the same direction are more interesting. Value works better when you do not buy garbage. Momentum works better when you do not pay outrageous prices. Small caps are more attractive when you filter for quality. Shareholder yield is more powerful when the company buys back cheap and is not just dressing up dilution.

Some interesting combinations:

1. Value + quality: cheap companies, but with decent profitability, cash, and balance sheets.
2. Value + stabilization: punished companies where the price stops getting worse and fundamentals are no longer deteriorating.
3. Small caps + illiquidity + quality: underfollowed businesses where the individual investor can actually look.
4. Shareholder yield + reasonable valuation: buybacks or dividends that genuinely create value.
5. Momentum + fundamentals: companies where price confirms a real improvement, not just a fad.

You can imagine there are a thousand attractive combinations.

The third use is understanding your portfolio. Often you think you have a diversified portfolio because you own 15 different stocks, but in reality you have a single bet: all expensive, all growth, all high beta, all from the same cycle, or all dependent on high multiples. Factors help you name those hidden exposures. This is where many investors are naive, thinking that diversifying across sectors and countries means they are safe.

They also help you stop cheating at solitaire. If you say you are value but all your positions depend on perfect growth for 10 years, maybe you are not that value. If you say you are quality but your companies do not convert earnings into cash, maybe you are buying accounting quality, not economic quality. If you say you are contrarian but you only buy stocks that keep going up because of momentum, maybe you are on the consensus side.

And I could keep going... in the end, knowledge has a thousand branches and applications.

To finish, I want to make one thing clear: this should not be misunderstood. No factor works all the time. No good company scores perfectly on everything. And no backtest replaces understanding the business. This is just one more tool to add to your investor toolkit, not the Ten Commandments.

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The original post includes interactive charts showing how the different factors have performed over time, plus an extra appendix comparing all the factors. I’m avoiding sharing it here so it doesn’t come across as spam, but if anyone is interested, leave me a comment.

No existe una free lunch.

¿O sí?

A los inversores se nos ha repetido muchas veces que el mercado es más o menos eficiente. Que puede haber errores puntuales, sí, pero que suelen corregirse rápido y que explotarlos de forma sistemática es muy difícil. La idea, en abstracto, suena razonable. Si todo el mundo busca dinero gratis, lo normal es que el dinero gratis desaparezca.

Y, sin embargo, el mercado no funciona exactamente así.

Existen ineficiencias que no desaparecen del todo porque no dependen solo de que alguien las descubra. Dependen de sesgos humanos, restricciones institucionales, incentivos mal alineados, problemas de liquidez, costes de implementación y de algo mucho más simple: muchas estrategias son psicológicamente devastadoras incluso cuando funcionan.

Es decir, no son ineficiencias que vayan a desaparecer de un día para otro. Son ineficiencias estructurales.

Muchas de ellas las conocéis de sobra. El value, el momentum, las small caps, la iliquidez, la calidad, las recompras. No estoy descubriendo América. Pero quizá no os hayáis parado a pensar en por qué siguen existiendo, cómo se combinan entre sí y, sobre todo, qué pueden aportar a un proceso de inversión real.

Pero cuidado, al contrario de lo que pueda parecer, estos factores no son una lista de la compra.

Que una empresa puntúe bien en un factor no significa que vaya a subir. Que esté barata no significa que sea una ganga. Que tenga momentum no significa que sea buena. Que sea de calidad no significa que cualquier precio esté justificado. Estamos hablando de patrones que funcionan en agregado, no de un algoritmo de selección de empresas individuales.

Dicho esto, entenderlos ayuda mucho. Te permite saber qué tipo de viento tienes a favor, qué riesgos estás asumiendo sin darte cuenta y qué trampas conviene evitar. Así que vamos con 12 ineficiencias estructurales que, bien usadas, pueden mejorar bastante cualquier proceso de inversión.

Value

La más conocida de todas.

La definición académica de value no siempre coincide con la que usan los inversores value de toda la vida. Los académicos hablan de comprar empresas baratas "ópticamente", es decir, baratas frente a beneficios, valor en libros, ventas, FCF, EBITDA, activos o cualquier otra métrica razonable de valoración. Es decir, empresas que cotizan a múltiplos bajos frente a sus fundamentales.

Puede parecer una forma algo burda de valorar negocios. Y muchas veces lo es. Un múltiplo aislado no te dice casi nada sobre una empresa concreta. Pero en agregado, históricamente, las empresas baratas por múltiplos han tendido a hacerlo mejor que las empresas caras. A las primeras solemos llamarlas value; a las segundas, glamour.

Y aquí viene lo bueno. La pregunta interesante es: si todo el mundo sabe que comprar barato tiene sentido, ¿por qué sigue funcionando?

Pues porque a la gente le encantan las historias convincentes y odia las empresas emponzoñadas.

El mercado suele pagar demasiado por compañías con una narrativa prometedora, crecimiento visible y una presentación de resultados que dice todas las cosas correctas. Y, al mismo tiempo, castiga demasiado empresas aburridas, cíclicas, sucias, mal vistas o directamente incómodas de tener en cartera.

Fama y French formalizaron este efecto usando book-to-market, por ser una métrica bastante estable en el tiempo. Luego el mundo cambió, aparecieron modelos de negocio menos intensivos en activos tangibles, como el software, y esa métrica perdió parte de su fuerza. Por eso hoy se usan muchas otras medidas de valoración. También puede que hayas escuchado hablar de HML, high minus low, que básicamente es un factor construido como una estrategia que compra acciones value o ‘baratas’ y vende acciones ‘caras’; su rendimiento mide la diferencia entre ambas.

Ahora bien, cuidado.

Que el factor value funcione no significa que comprar cualquier cosa a PER 2 sea buena idea. Muchas empresas están baratas por un motivo perfectamente razonable: porque son malas, porque el negocio se está deteriorando, porque la contabilidad engaña o porque el capital se va a destruir antes de que tú puedas cobrarlo.

Ese es el gran problema del value: las value traps. Empresas ópticamente baratas que en realidad no están baratas, sino que son basura con múltiplos bajos justificados.

Además, value puede tener periodos larguísimos de sufrimiento. Puedes pasar años pareciendo idiota mientras el mercado paga múltiplos absurdos por compañías de crecimiento. Puedes desangrarte lentamente contra el índice y, justo cuando todo el mundo decide que el value ha muerto, tener una rotación brutal que recupera años de diferencia en pocos meses.

Sí funciona, pero no es glamuroso. Pareces imbécil la mayor parte del tiempo y es una apuesta que cuesta mantener durante años.

Y eso, precisamente, ayuda a explicar por qué sigue existiendo.

Momentum de precio

El momentum de precio es uno de los factores más odiados por muchos value investors y uno de los más queridos por los inversores técnicos. La idea es comprar las acciones que mejor lo han hecho en los últimos 6-12 meses, normalmente saltando el último mes para evitar ruido y reversión de muy corto plazo.

Es un factor denostado por los inversores fundamentales porque se basa solo en el precio.

Y claro, si entiendes la bolsa desde el punto de vista empresarial, esto no acaba de cuadrar en tu marco mental. ¿Cómo va a decirte algo útil el precio, ignorando beneficios, márgenes, deuda, retornos sobre capital o cualquier cosa que parezca remotamente fundamental?

Pues lo dice.

El mercado tarda en incorporar información. Los analistas ajustan estimaciones poco a poco. Los flujos persiguen ganadores. Las narrativas se construyen por capas. Y los precios, nos guste o no, a veces empiezan a reflejar cambios en expectativas antes de que esos cambios sean evidentes en los números.

Además, los inversores no somos robots. Nos cuesta vender ganadores, perseguimos lo que sube, extrapolamos tendencias y sufrimos FOMO con una facilidad bastante vergonzosa. El momentum no funciona porque el mercado sea "tonto" de una manera simplista; funciona porque el proceso de descubrimiento de precios es lento, social y emocional.

El problema es evidente: en fases de euforia, el momentum puede empujarte hacia basura especulativa. Empresas sin fundamentales, sin caja, sin beneficios y con una historia increíble que solo necesita 17 rondas más de financiación para hacerse realidad. Y cuando el mercado gira, llegan los famosos momentum crashes: caídas violentas que pueden evaporar buena parte de la rentabilidad acumulada.

Es un factor potentísimo, pero no es gratis.

Value + momentum

Value y momentum se llevan mejor de lo que parece.

De hecho, tienden a estar negativamente correlacionados. Value compra lo odiado; momentum compra lo premiado. Cuando el mercado está enamorado del growth y value sufre, momentum suele ayudar. Cuando momentum crashea por un rebote de los perdedores, value suele tener más exposición a esos perdedores baratos o cíclicos que rebotan con fuerza.

En términos prácticos: momentum puede ayudarte a evitar value traps y value puede amortiguar parte de los crashes de momentum.

Por eso, aunque el mismísimo Warren Buffett haya asistido a tu bautizo, te conviene entender el momentum. No tienes que convertirte en trader ni empezar a dibujar líneas en una gráfica. Basta con reconocer que el precio contiene información y que comprar barato mientras todo sigue empeorando suele no ser óptimo.

Momentum de fundamentales

Este es el primo fundamental del momentum de precio.

Aquí no compras porque la acción haya subido, sino porque las expectativas sobre el negocio están mejorando. Revisiones al alza de estimaciones, sorpresas positivas de resultados, mejora de guidance, expansión de márgenes, aceleración de ingresos, reducción de deuda más rápida de lo esperado. Cosas que indican que el negocio va mejor de lo que el mercado creía.

La ineficiencia existe porque el mercado rara vez ajusta todo de golpe.

Un analista sube estimaciones. Luego otro. Luego la compañía bate otra vez. Luego el mercado empieza a creerse la historia. Luego aparece una nueva narrativa. Y, mientras tanto, el precio va incorporando esa mejora por fases.

El caso clásico es el PEAD, post-earnings announcement drift. Después de una sorpresa positiva de resultados, la acción tiende a seguir subiendo durante un tiempo. Después de una sorpresa negativa, tiende a seguir cayendo. En teoría, el mercado debería ajustar el precio casi instantáneamente el día de resultados. En la práctica, muchas veces no lo hace del todo.

Este factor es especialmente útil para quien invierte en value, porque ayuda a distinguir entre empresas baratas que están empezando a mejorar y empresas baratas que siguen cavando hacia abajo. Si los fundamentales no paran de deteriorarse, quizá no estés comprando una ganga; quizá estés comprando basura.

Profitability

La rentabilidad del negocio es una de esas ideas que parecen demasiado obvias para ser una ineficiencia. Además, es una de las principales métricas de las empresas "Quality".

Todo el mundo quiere empresas con buenos márgenes, altos retornos sobre capital, beneficios recurrentes y capacidad de convertir ventas en caja. Una empresa que gana mucho sobre el capital que necesita para operar tiene más opciones: puede reinvertir, recomprar acciones, pagar dividendos, aguantar ciclos malos y crecer sin depender tanto de financiación externa.

La pregunta es la misma de siempre: si todo el mundo sabe que las empresas rentables son mejores, ¿no debería estar ya reflejado en el precio?

Sí. Pero muchas veces no lo suficiente.

Al mercado le cuesta valorar bien la persistencia de la rentabilidad. Muchas empresas parecen caras mirando solo PER o EV/EBITDA, pero si mantienen retornos altos durante muchos años, reinvierten a buenas tasas y no necesitan demasiado capital incremental, pueden componer valor de una forma que los múltiplos estáticos no capturan bien.

La ineficiencia no es “comprar cualquier empresa buena a cualquier precio”. Eso sería una forma elegante de sobrepagar. La idea es que, en agregado, el mercado tiende a infravalorar cuánto importa que una empresa gane mucho sobre su capital y que esa rentabilidad sea más estable de lo que parece.

Como filtro, profitability combina muy bien con value. Te ayuda a no llenar la cartera de empresas baratas por motivos horribles. También combina bien con momentum, porque reduce el riesgo de acabar comprando compañías puramente especulativas solo porque están subiendo.

Qué bonito es cuando los factores se ayudan unos a otros.

Conservative investment

Esta ineficiencia es bastante contraintuitiva.

Uno pensaría que las empresas que más invierten, más expanden activos y más persiguen crecimiento deberían hacerlo mejor. Al fin y al cabo, están invirtiendo para crecer. ¿No era eso lo que queríamos?

Pues no necesariamente.

En agregado, evitar empresas que expanden agresivamente su base de activos, aumentan mucho el capex, emiten capital o persiguen crecimiento a cualquier precio suele funcionar mejor. La razón es muy mundana: muchas compañías invierten mal. Compran caro, construyen capacidad que no necesitan, se embarcan en proyectos con retornos mediocres o hacen adquisiciones para que el CEO pueda decir que dirige una empresa más grande.

El mercado, mientras tanto, suele aplaudir esas señales de expansión. Más crecimiento, más ventas, más presencia geográfica, más verticales, más adquisiciones. Todo suena muy bien hasta que miras el retorno sobre el capital.

La reinversión es necesaria para crear grandes negocios, sí. Pero solo crea valor cuando se hace con retornos atractivos. Este factor ayuda a filtrar empire builders: empresas que crecen por crecer sin traducir ese crecimiento en mejores retornos para los accionistas.

Hay un matiz importante. En el post sobre las 10 baggers vimos que muchas grandes ganadoras sí invertían agresivamente y expandían su base de activos. Pero lo hacían con una diferencia clave: ese crecimiento estaba respaldado por crecimiento real del EBITDA. No era expansión vacía, sino crecimiento sano.

La línea entre reinversión de calidad y destrucción de capital puede ser fina, pero existe. Esto puede ser un tema para otro post.

Accruals / earnings quality

Esta es menos popular entre inversores retail, pero me parece muy útil.

La idea es que siempre es mejor confiar en beneficios que vienen acompañados de caja. Si una empresa gana mucho en el papel pero convierte poco en cash flow, conviene mirar dos veces. A veces la diferencia es normal. Otras veces es una señal de que esos beneficios son menos sólidos de lo que parecen. Al final, lo que paga deuda, dividendos, recompras y reinversión no es el EPS ajustado; es la caja.

No debería sorprender a nadie que convenga desconfiar de empresas que presentan beneficios preciosos pero flujos de caja mediocres. Tampoco debería sorprender que convenga desconfiar de compañías que viven entre ajustes, extraordinarios, métricas inventadas y reconciliaciones contables que están diseñadas para que nadie las lea.

El problema, documentado de forma clásica por Sloan en 1996, es que el mercado se fija demasiado en el EPS contable y no distingue siempre entre beneficios de alta calidad y beneficios de baja calidad. A veces castiga a una empresa por un trimestre feo aunque la generación de caja siga intacta. Otras veces premia una mejora contable que no tiene respaldo económico.

Este factor sirve sobre todo para filtrar lo peor. Empresas con accruals altos, conversión de caja débil y demasiada creatividad contable. No es para encontrar joyas, sino para evitar problemas.

Y eso no es poco.

Shareholder yield

Este factor suele gustar mucho a los value investors, y con razón.

La idea general es comprar empresas que devuelven capital al accionista de forma inteligente y evitar compañías que diluyen sistemáticamente. En sentido amplio, aquí pueden entrar recompras, dividendos, reducción de deuda y emisiones de acciones. Sobre recompras ya he hablado bastante en otros artículos, así que no hace falta recrearse demasiado.

Pero la gráfica de abajo mide algo más concreto: net payout yield. Es decir, empresas que devuelven capital vía dividendos y recompras netas de emisión de acciones frente a empresas que emiten más equity. No mide reducción de deuda. Por eso hay que leerla sobre todo como una señal de devolución de capital vía equity y de antidilución.

El base rate es claro: las emisiones suelen ser una mala señal.

Puede que la empresa crea que sus acciones están caras. Puede que necesite financiar pérdidas. Puede que esté pagando adquisiciones con papel. Puede que no tenga otra forma razonable de levantar capital. No siempre es malo, pero como punto de partida es feo.

Las recompras, por el contrario, pueden ser muy buenas. Pero solo si son reales y se hacen a buen precio. Recomprar acciones a múltiplos absurdos financiándose con deuda puede destruir valor. Recomprar solo para compensar stock-based compensation tampoco es exactamente devolver capital al accionista; es más bien correr en una cinta para no moverse.

Lo interesante de shareholder yield es que se ve muy bien en agregado, aunque caso por caso sea menos obvio. A veces emitir acciones es necesario o incluso positivo. A veces una recompra parece razonable y acaba siendo mala. Y, por supuesto, cuando una empresa diluye a sus accionistas, normalmente viene acompañada de una narrativa maravillosa sobre crecimiento, oportunidades estratégicas y creación de valor a largo plazo.

Siempre hay una bonita narrativa para respaldar cada cagada.

Illiquidity effect

La iliquidez es una de las ineficiencias más conocidas y una de las pocas ventajas reales que tiene el inversor particular.

Las acciones menos líquidas, menos cubiertas por analistas, menos presentes en carteras institucionales y más difíciles de comprar y mantener tienden a ofrecer mejores oportunidades. El mercado te paga por soportar fricción: spreads más amplios, menor cobertura, menos información procesada, más dificultad para entrar y salir, y menos capacidad para meter capital grande.

Para un fondo enorme, muchas de estas empresas son directamente invisibles. No puede comprar suficiente sin mover el precio, no puede justificar el tiempo de análisis o no puede asumir el riesgo de liquidez. Para un inversor particular paciente, en cambio, esa misma incomodidad es una ventaja.

Este factor encaja especialmente bien con horizontes temporales largos. Si no necesitas liquidez inmediata, no usas apalancamiento, no dependes de stops y no tienes posiciones demasiado grandes, puedes permitirte mirar donde otros no miran.

Vamos, que es el terreno de caza perfecto para el inversor retail.

Y, como casi siempre, se vuelve más interesante al combinarlo con otros factores: iliquidez más calidad, iliquidez más value, iliquidez más insider ownership, iliquidez más una situación especial que nadie está mirando.

Ahí siempre se encuentran perlitas.

Anti-lottery / betting against beta

Esta es una joya a menudo ignorada y una de las piezas detrás del éxito de Buffett, como ya comentamos en el post sobre si Buffett es realmente un buen inversor.

Las acciones aburridas, defensivas, menos volátiles y con menor beta han ofrecido históricamente mejores retornos ajustados por riesgo que sus contrapartes más excitantes. Aquí entra BAB, betting against beta: comprar acciones low beta y vender acciones high beta.

La explicación tiene bastante sentido.

Primero, invertir en empresas aburridas no es sexy. Todo el mundo quiere encontrar la próxima Tesla, la próxima NVIDIA antes de ser NVIDIA o esa acción que puede multiplicarse por 100. Las acciones tipo lotería atraen capital porque prometen una historia asimétrica, aunque muchas veces el precio ya incorpora demasiada fantasía.

Segundo, muchos inversores tienen restricciones de apalancamiento. Si no puedes apalancarte y quieres aumentar el retorno esperado de la cartera, una forma sencilla de intentarlo es comprar acciones de alta beta. Eso empuja esas acciones a precios demasiado altos y deja relativamente olvidadas las de baja beta.

Tercero, los benchmarks distorsionan incentivos. Un gestor profesional no vive solo de maximizar rentabilidad ajustada al riesgo; vive de compararse contra un índice. Una cartera low vol puede tener mejor Sharpe, pero si tiene menos beta que el mercado quizá se queda por detrás en años alcistas. Y quedarse por detrás del benchmark es una forma asegurada de perder tu trabajo.

Por eso aparece esta ineficiencia estructural, porque no basta con que se conozca para que desaparezca. Muchos participantes del mercado no pueden o no quieren explotarla bien.

Ahora bien, cuidado.

Low vol no siempre es barato. A veces estas acciones se convierten en proxies de bonos: utilities, staples, REITs, infra, dividend stocks. Cuando todo el mundo busca seguridad o yield, la seguridad también se encarece.

Incluso lo aburrido puede ponerse caro.

Matiz sobre BAB

Conviene separar la intuición low beta del factor BAB concreto. Frazzini y Pedersen documentan muy bien la anomalía, pero Novy-Marx y Velikov critican que parte de la rentabilidad de BAB depende de detalles de construcción difíciles de replicar, exposición a microcaps y tilts hacia profitability e investment. Por eso no incluyo gráfica aquí: el theme low risk de JKP no representa bien el BAB usado en Buffett's Alpha ni el factor BAB de AQR.

Reversal

Aquí conviene separar horizontes.

Existe short-term reversal: acciones que caen mucho en días o semanas pueden rebotar por microestructura, liquidez, rebalanceos o sobrerreacción. El problema es que suele ser difícil de explotar porque los costes de trading, spreads e impuestos se comen buena parte de la oportunidad.

Y existe long-term reversal: acciones que han sido perdedoras durante 3-5 años pueden rebotar si el mercado sobreestimó malas noticias. Este efecto encaja bastante con la intuición value: el mercado se pasa castigando algo, la narrativa se vuelve demasiado negativa y, cuando la realidad deja de empeorar, el precio se ajusta.

Reversal complementa y pelea con momentum dependiendo del horizonte. A un mes puede haber reversión. A 6-12 meses suele haber momentum. A 3-5 años puede volver a aparecer reversión.

Por eso el momentum clásico suele saltarse el último mes. Y por eso value funciona mejor cuando no compras una caída reciente simplemente porque parece barata, sino cuando hay alguna señal de estabilización.

Y de aquí surge buena parte del debate sobre si se deberían coger "cuchillos cayendo".

Industry momentum

Como veis, el momentum no para de aparecer con distintos disfraces.

El industry momentum compra industrias ganadoras porque las industrias ganadoras tienden a seguir ganando durante un tiempo. Es la parte más macro del momentum individual y, de hecho, puede explicar una parte importante del momentum de muchas acciones.

A veces crees que has encontrado una empresa con mucho momentum, pero en realidad solo has comprado el sector correcto. Eso puede ser bueno o malo según lo que estés intentando hacer.

Hay dos formas razonables de usarlo.

La primera es hacer sector-neutral momentum: buscas los mejores nombres dentro de cada sector para reducir apuestas macro o sectoriales.

La segunda es aceptar explícitamente que los sectores ganadores siguen ganando durante un tiempo e incorporarlo como señal. Esto te expone más al ciclo, a narrativas sectoriales y a rotaciones, pero también puede capturar movimientos muy potentes.

Si haces stock picking, lo que tienes que hacer es separar “la empresa es buena” de “todo el sector está subiendo”.

No son la misma tesis.

Small caps / size factor

Dejamos el factor más famoso para el final.

La explicación intuitiva del efecto small caps es sencilla: las empresas pequeñas viven en una zona del mercado con menos atención, menos capital arbitrando errores, más fricción, más riesgo real y más paja mezclada con el grano.

Es el tipo de sitio donde, en teoría, un inversor paciente y con estómago puede encontrar oportunidades que no existen en mega caps cubiertas por 40 analistas.

Pero el size factor es también uno de los más debatidos y menos limpios.

Hay varios matices importantes:

1. El efecto no es lineal y se concentra sobre todo en las empresas más pequeñas.
2. El size effect clásico se debilitó bastante después de ser descubierto.
3. Parte de la prima histórica parece concentrada en enero. En algunos análisis, el size premium fuera de enero era prácticamente cero.
4. Market equity sí mide tamaño de forma directa, pero la prima size no es una ineficiencia pura como value, profitability o momentum. Muchas veces viene mezclada con iliquidez, menor cobertura, mayor riesgo de negocio, value, beta, composición sectorial y un montón de cosas más.

En cierto sentido, no es una ineficiencia gratis. Es compensación por soportar mierda real.

Fama y French lo incorporaron como SMB, small minus big, pero la señal broad de small caps sin filtros es sucia. El universo small cap está lleno de empresas malas: compañías sin beneficios, alto leverage, dilución constante, biotechs binarias, roll-ups mediocres, negocios cíclicos débiles, growth caro sin FCF y proyectos que solo existen porque todavía hay alguien dispuesto a financiarlos.

Lo bueno es que size matters if you control your junk.

Cuando filtras por calidad, rentabilidad, balance, dilución y otras señales de basura, la prima small cap parece más interesante. De nuevo, no porque todas las pequeñas empresas vayan a hacerlo mejor, sino porque las pequeñas empresas buenas y poco seguidas pueden estar en una zona del mercado donde todavía hay errores importantes.

Entonces, ¿sigue existiendo el efecto small cap?

Sí, pero no como un factor puro, limpio y automático. La versión broad de “small minus big”, sin filtros, es débil, inestable, cara de implementar y probablemente contaminada por liquidez, calidad, enero, beta y composición sectorial.

¿Es simplemente un proxy de liquidez?

En parte sí, pero no del todo. La liquidez explica mucho del efecto bruto. Pero cuando controlas por calidad o junk, parece quedar algo único: una prima asociada a pequeñas empresas mejores de lo que el mercado está dispuesto a mirar.

Por eso las small caps son también un territorio interesante para el inversor particular.

Cómo usar todo esto

Lo tentador después de leer una lista así es abrir un screener, meter 14 filtros y pensar que ya tienes una máquina de imprimir retornos.

Te lo adelanto: no la tienes.

La forma correcta de usar los factores no es como una receta, sino como un mapa de probabilidades. Te dicen qué tipo de empresa suele tener viento estadístico a favor y qué tipo de empresa suele tenerlo en contra. Luego viene lo difícil, que es entender el caso concreto que estás estudiando.

Para un inversor retail, esto es especialmente útil porque el mayor recurso escaso no suele ser el capital, sino la atención. No puedes analizar 5.000 empresas en profundidad. Necesitas decidir dónde merece la pena dedicar horas y dónde es mejor pasar página rápido. Ahí los factores ayudan mucho.

El primer uso práctico es limpiar el universo. Si una empresa parece barata pero diluye mucho, convierte mal los beneficios en caja, invierte agresivamente con retornos mediocres y encima tiene momentum horrible, la carga de la prueba es altísima. Puede salir bien, claro. Pero ya sabes que estás peleando contra varias fuerzas a la vez.

Al revés también funciona. Una empresa pequeña, poco cubierta, razonablemente barata, rentable, con buen balance, sin dilución y con señales de mejora merece más atención que una historia preciosa que solo tiene una buena narrativa. No significa comprarla. Significa que probablemente merece que le dediques un poco de tiempo a estudiarla.

El segundo uso es combinar señales. Un factor aislado es una pista. Varios factores apuntando en la misma dirección son algo más interesante. Value funciona mejor cuando no compras basura. Momentum funciona mejor cuando no pagas precios desorbitados. Small caps son más atractivas cuando filtras por calidad. Shareholder yield es más potente cuando la empresa recompra barato y no solo maquilla la dilución.

Algunas combinaciones interesantes:

1. Value + calidad: empresas baratas, pero con rentabilidad, caja y balance decentes.
2. Value + estabilización: empresas castigadas donde el precio deja de empeorar y los fundamentales ya no se deterioran.
3. Small caps + iliquidez + calidad: negocios poco seguidos donde el inversor particular sí puede mirar.
4. Shareholder yield + valoración razonable: recompras o dividendos que realmente crean valor.
5. Momentum + fundamentals: compañías donde el precio confirma una mejora real, no solo una moda.

Ya te puedes imaginar que hay mil combinaciones atractivas.

El tercer uso es entender tu cartera. Muchas veces crees que tienes una cartera diversificada porque tienes 15 acciones distintas, pero en realidad tienes una sola apuesta: todas caras, todas growth, todas high beta, todas del mismo ciclo o todas dependientes de múltiplos altos. Los factores te ayudan a poner nombre a esas exposiciones ocultas. Aquí muchos inversores pecan de inocentes pensando que por diversificar en sectores y países ya están a salvo.

También te ayudan a no hacerte trampas al solitario. Si dices que eres value pero todas tus posiciones dependen de crecimiento perfecto a 10 años, quizá no eres tan value. Si dices que eres quality pero tus empresas no convierten beneficios en caja, quizá estás comprando accounting quality, no calidad económica. Si dices que eres contrarian pero solo compras acciones que siguen subiendo de precio por momentum, quizás estás del lado del consenso.

Y podría seguir... al final el conocimiento tiene mil derivadas y aplicaciones.

Para acabar, quiero dejar claro que esto no debe malinterpretarse. Ningún factor funciona siempre. Ninguna empresa buena puntúa perfecto en todo. Y ningún backtest sustituye a entender el negocio. Esto es solo una herramienta más que añadir a tu repertorio como inversor, no los diez mandamientos.

El post original contiene gráficos interactivos para ilustrar cómo han funcionado los diferentes factores a lo largo del tiempo además de un apéndice extra con todos los factores comparados. Evito ponerlo por no hacer spam, pero si a alguno le interesa que me deje un comentario.

Most people will be familiar with Warren Buffett, possibly the greatest investor of all time. But what few people know is that there is another side to this story: a group of people who argue that, as unbelievable as it may sound, his success was due to luck. We are not talking about idiots with no prestige, but Nobel Prize winners, top-level economists, and respected people.

This argument is not as stupid as it sounds.

They are not saying that Buffett is dumb and simply wins by chance without knowing what he is doing; it is something more interesting, and worth paying attention to.

The Statistical Anomaly

In the 1960s and 1970s, the idea that the market incorporates available information became consolidated. Fama formulated the efficient market hypothesis, and the idea that beating the market is statistically improbable started to become popular. Through that lens, Buffett was an anomaly the model could not explain: if nobody can beat the market except by chance, what do you do with a guy who beats it for decades?

Warren Buffett’s returns have traditionally been ignored or discredited in academia, even by some of the most influential and reputable finance professors, because his track record is classified as a statistical outlier.

The discussion begins with Michael Jensen, who dismissed Warren Buffett’s returns in a debate held in 1984 at Columbia University:

>If I look at a group of analysts with no talent, all of whom do nothing but flip coins, I expect to find some who have flipped two heads in a row, and even some who have flipped ten heads in a row.

Years later, Merton Miller summarized that same intuition even more directly:

>If there are 10,000 people looking at stocks and trying to pick winners, one in 10,000 is going to get, just by chance, a great success, and that is all that is happening. It is a game, a chance operation, and people think they are doing something deliberate, but in reality they are not.

Another Nobel Prize winner, William Sharpe, described Buffett as a “3-sigma event” and said he was “a statistical aberration so far outside the norm that it requires no further attention.”

Burton Malkiel also repeated something similar:

>In any activity in which a large number of people participate, although the average will probably prevail, the unexpected is bound to happen. The tiny number of truly good managers we find in the investment management industry is not at all inconsistent with the laws of chance.

With these arguments, and others like them, Warren Buffett’s extraordinary returns are attributed entirely to chance. And again, although this may seem insane, it is not that far-fetched.

The argument becomes more convincing when you hear people like Mauboussin explain a counterintuitive idea: in competitive markets, it is very difficult to distinguish real skill from a statistical tail.

Paradoxically, the more skilled and evenly matched the players in a game are, the more the result depends on luck. One would expect that, at the highest competitive levels, skill would decide the outcome, but since all the players are extremely capable and their skill barely differs, luck often ends up dictating the result.

Knowing this, is it so crazy to think that in the most competitive market in the world, the stock market, Buffett’s returns could be due to luck? Is he really that far ahead of everyone else?

Orangutans Flipping Coins

The idea is simple: in an experiment with millions of investors, someone has to come out on top. Just as if millions of people flipped coins, some would string together 20 heads in a row, something extremely unlikely. That does not prove skill; it proves the sample space was huge and someone had to win.

Luckily for all of us, Buffett’s response to these claims has gone down in history:

>To begin with, if (a) you had taken 225 million orangutans distributed roughly like the population of the United States; if (b) after 20 days there were 215 winners left; and if (c) you found that 40 came from a particular zoo in Omaha, you would be pretty sure you had found something.

You would probably go ask the zookeeper what he feeds them, whether they do special exercises, what books they read, and who knows what else. In other words, if you find a truly extraordinary concentration of success, you may want to see whether you can identify a concentration of unusual characteristics that could be causal factors.

The analogy is brilliant. In a world of millions of coin flippers, of course someone will have an absurd streak. But if a disproportionate number of the winners come from the same place, with the same principles and the same intellectual school, perhaps the right question stops being “was he lucky?” and becomes “what did they have in common?”

That intellectual school is, of course, the Graham & Dodd school.

Charlie Munger told a similar story in his Herb Kay Memorial Lectureat the University of California, Santa Barbara:

>For a long time, there was a Nobel Prize-winning economist who explained the success of Berkshire Hathaway in the following way:

First, he said Berkshire had beaten the market by investing in common stocks thanks to one sigma of luck, because nobody could beat the market except by luck. This hard version of the efficient market theory was then being taught in most economics departments. People were taught that nobody could beat the market.

Then the professor moved to two sigmas, and three sigmas, and four sigmas, and when he finally got to six sigmas of luck, people laughed so much that he stopped doing it.

Then he reversed the explanation 180 degrees. He said: “No, it was still six sigmas, but it was six sigmas of skill.”

Now, I do believe that Warren Buffett has been very lucky. But not from the coin-flipping perspective.

Buffett was born in the right place, at the right time, with the right market. In post-war America, with less efficient markets, less quantitative competition, early access to small caps and special situations, and then the ability to reinvest for decades. That was his luck, which of course does not negate his skill, but it does explain why repeating “another Buffett” today is much harder.

The Quantitative Explanation

The classic paper Buffett’s Alpha provides a very interesting explanation of Buffett’s returns without resorting to “luck” or to the esoteric idea of “Buffett’s innate ability.” In the version published in the Financial Analysts Journal, Frazzini, Kabiller, and Pedersen estimate that Berkshire achieved a Sharpe ratio of 0.79 and that Buffett used average leverage of approximately 1.7x. In addition, after controlling for factors such as Betting-Against-Beta and Quality-Minus-Junk, the alpha is no longer statistically significant.

In other words, his success can be explained by factor exposure, leverage, and a very special corporate structure.

I particularly like this paper because it acts as a bridge between the academic world and the world of value investors, something that rarely happens. While it is not perfect, it helps us understand that Buffett’s genius is compatible with academic theory, which is also not perfect, and with the school of value investing.

What Factors Explain Buffett’s Success?

1. He bought cheap, safe, high-quality stocks. It was not just “value” in the academic sense. Berkshire had a lot of exposure to value, low beta, and quality factors: profitable, stable companies, with low risk, reasonable growth, and healthy payout policies. In other words, something quite similar to what value investors usually mean by “value,” which is consistent with his investment philosophy. In addition, when the authors control for BAB and QMJ, much of the “alpha” disappears. In other words, Buffett’s returns are explained by those variables without needing to resort to more intangible and esoteric explanations.
2. He used cheap and stable leverage. The paper estimates that Berkshire had a Sharpe ratio of 0.79: very good, yes, but not spectacular. What turns that into a gigantic fortune is applying leverage to it for decades. The paper estimates average leverage of around 1.7 times. The interesting part is that Berkshire did not borrow like a normal hedge fund. Berkshire had insurance float: insurance premiums collected in advance and paid later in the form of claims. The paper estimates that around 35-36% of its liabilities came from float, with an average cost below the T-Bill rate. This significantly increases returns, and low-cost financing is a brutal advantage.
3. He had the reputation. Another advantage, this time qualitative, is that Berkshire, thanks to the reputation built after decades of strong performance, achieved something very valuable: it did not have to sell at the worst moment. Many people can have a good strategy but fail to survive the drawdowns. Berkshire lost 44% between June 1998 and February 2000 while the market rose 32%. Many managers would have lost clients, funding, or directly their jobs. Buffett, thanks to his prestige, was able to endure it.

In reality, Buffett’s great skill seems to have been detecting earlier and better than other investors what many people now know: that buying “quality, value, and low beta” stocks with ridiculously cheap financing, and holding them for decades, was a great investment strategy.

At this point, value investors and academics can finally hug each other and agree on something. Buffett’s success is demonstrable according to both “schools,” and both find common ground where they can have a conversation.

Berkshire Dominated for 40 Years

Perhaps, after reading Charlie’s quote earlier, some of you may have thought he was exaggerating. It is hard to understand how incredible and consistent Buffett’s returns have been, so it is no surprise that academics had a hard time accepting that they were due to something other than luck.

Luckily, the charts in the paper’s appendix make this quite clear. Buffett is not simply “a little above average,” but dominates all his peers.

In the universe of U.S. equity funds with at least 40 years of history, Berkshire appears completely isolated in the right tail. Nobody comes close.

(can't attach images so refer to my original blog post or the original paper for the charts)

The same thing happens when the comparison is made against U.S. stocks with more than 40 years of trading history. How could you not think that something strange is going on?

Among U.S. stocks with at least 40 years of history, Berkshire had the highest Sharpe ratio; and among comparable mutual funds, it did too.

So, while the paper provides a theoretical framework for understanding Berkshire’s spectacular performance, this does not dilute how incredible the event was in any way. In fact, I think it helps us understand it even better.

Qualitative Factors

Many of you will be thinking: where does the qualitative part fit in?

Well, you are not the only ones. As I said, the previous paper is not perfect, nor does it pretend to be, so a later paper titled “Buffett’s Alpha: Further Explanations from a Behavioral Value Investing Perspective” adds qualitative layers to Buffett’s performance. The main argument is that the CAPM and its derivatives have little practical application and, of course, their equations fail to capture the qualitative nuances that truly explain Berkshire’s returns.

The factors added by the paper are no less important, but in my opinion they are less interesting. They are things any value investor has heard hundreds of times. Still, I will mention some of them briefly, not all of them, because they should be kept in mind, since investing in practice is very different from investing in theory.

1. Temperament: Buffett not only knew what to buy; he had the psychological ability to hold a strategy for decades, even when it looked stupid or out of fashion. The quantitative strategy explains the “what,” but temperament explains “how you survive long enough to capture it.”
2. Reputation: Here it appears in a different form from the one already mentioned. Berkshire became the “preferred buyer” for owners of good family businesses. Many sellers preferred to sell to Buffett because they trusted that he would not break up the company, destroy the culture, or mistreat the managers. That gave him access to deals other financial buyers did not have.
3. Circle of competence: Buffett and Munger avoid many mistakes simply by not playing games where they do not have an edge. The paper connects this with behavioral biases, especially overconfidence in one’s own abilities. Their advantage was not knowing everything, but knowing where they did not know enough.
4. Berkshire’s organizational culture: Berkshire combines two rare things: capital allocation centralized in Buffett/Munger and highly decentralized operations in the managers of each business. That reduces bureaucracy, attracts independent managers, and allows capital decisions to be made rationally without the typical pressure of running the day-to-day business.

This paper concludes that factor models explain an important part of Buffett’s result, but they do not capture the human and organizational mechanisms that made that result possible.

In other words, the first paper explains the exposures: quality, value, low beta, leverage. This second paper tries to explain the deeper source: discipline, culture, reputation, patience, rationality, and capital allocation.

Conclusion

I felt like writing this article to tell the funny story of the orangutans flipping coins and the arrogance of academia toward results that escape its models. But I also wanted to show that academic research is not incompatible with different schools of investing, such as value investing, as long as one understands what it is about and remains coherent.

Both sides, academia and value investing, would benefit from listening more carefully to what the other has to say; so would individual investors who lean toward either of them. Let this small blog serve as a point of reconciliation between both doctrines, using one of the greatest investors of all time as an excuse.