Returns on equity

“To raise that return on equity, corporations would need at least one of the following: (1) an increase in turnover, i.e., in the ratio between sales and total assets employed in the business; (2) cheaper leverage; (3) more leverage; (4) lower income taxes; (5) wider operating margins on sales.

And that’s it. There simply are no other ways to increase returns on common equity. Let’s see what can be done with these.”

Thomas Piketty on capital in the twenty-first Century

Wealth concentration is always higher than income concentration because initial higher wealth inequality is compounded at a faster pace due to returns on capital being higher than economic growth.

I thought it was particularly interesting that larger endowments and wealthier persons achieve higher rates of return than smaller peers. This is contrary to the saying often quoted in value circles that the enemy of performance is size.

For the endowments and wealthier persons I would guess the higher returns relative to peers is a function of the access to talent. The talent goes where the money is and unfortunately many who could use the higher returns aren’t able to access them. It would be great if there were a way and maybe there is an opportunity here.

Beau Willimon, House of Cards, and Absolute performance orientation

Despite being stuck on the 405 a few days ago, life was not terrible (about as contrarian a statement as one can make). Marketplace aired an interview with Beau Willimon, House of Cards creator, that provided great insight, albeit in a different field, to the power of an absolute- as opposed to relative- performance orientation.

Willimon shopped House of Cards around to several networks including AMC, Showtime and HBO with the same pitch: he wanted to do one season and no pilot. It was Netflix that not only bit but gave Willimon and HoC two huge benefits.

  • Netflix would order two seasons, no matter what.
  • Netflix would not share its large amount of data with Willimon.

To this day, Willimon says he does not know the actual number of people that watch the show. Both of Netflix’s decisions enabled Willimon freedom to explore his creativity to the utmost.

Willimon says, “Knowing that I had 26 hours meant that I had a broad canvas I could paint on. I knew there were things I could lay in early on in season one that might not fully come back til the end of season two. So you could really delve deeper into characters. You don’t feel rushed. You don’t have to force big cliffhangers or jump the shark in order to try to make something dramatic happen inorganically, the way that some shows feel the pressure to, because they’re in a ratings game week to week, fighting for their life.”

He continued to expand upon the weight of knowing your numbers:

“Those numbers can lead to either forced choices that have nothing to do with the creative process, or, conversely, coming from the creative side, a form of pandering. Because you become obsessed with those numbers and try to cater to them. So, I don’t have to deal with any of that…”

Willimon said friends of his that work on traditional networks constantly contend with their ratings. They’re forced to stick with a popular plot point if it gets good ratings. By knowing the numbers and the relative ratings of every show, and relative to past weeks of their own shows, the writers are forced to make certain decisions that may not be in the best long-term interest of the content.

What was also interesting is that Willimon seemed to say that his writer friends on traditional shows anchor to the ratings even if they try to react against them. They might not include a character with good ratings as much as the networks want out of spite! Neither pandering nor spiteful writers sound like they have the show’s best interests at heart. 

Value investment managers with an absolute performance orientation are empowered in the same way Willimon and HoC are. We are able to ignore what has good ratings (what is popular) and focus on what is out of favor, often where the best bargains are found. If you have the right type of investors or backers that guarantee the long-term nature of their contribution, it allows a manager, like Willimon, to focus on what he thinks will provide maximum performance over the long term, not near-term performance as seen in forced plot points and hot stocks.


Here is a link to the story:


Investing: The Last Liberal Art

This post will begin short chapter summaries that I wrote after reading the book. I write summaries of the books I read in order to have a quick and easy reference of the main points to further embed what I read in my head. So these notes are most likely far more helpful for myself than they may be for someone reading.

Introduction: A Latticework of Mental Models

The book’s relationship to investing is based on Charlie Munger’s idea that stockpicking is part of the subdivision of worldly wisdom. The idea is founded in Benjamin Franklin’s ideas on education but takes modern root in the concept of connectionism. Connectionism holds that learning is a process of trial and error in which favorable responses to new situations (stimuli) alter the neural connections between brain cells. Intelligence is therefore a function of how many connections one has. These connections can be seen as mental models in which each model is representative of a field and the more mental models one is able to connect, the more higher level or metathinking occurs. Through continuous learning one can form a latticework of mental models that can be applied in order to more easily relate what we don’t to what we do know. In investing one is constantly looking at new information, situations and industries. Therefore it is crucial to create the most opportunity for yourself to understand and critically analyze the new information by having the most metaphors or at least many metaphors in which you can draw on. With metaphors at your fingertip, you are able to describe and therefore explain the information.


Newton began at Cambridge exploring the ideas of Kepler, Galileo and Descartes. During his wonder year of 1665, Newton combined Kepler’s laws with Galileo’s observations and Descartes’s vision of a mechanistic world. From Newton’s compilation of these ideas into a synthesized version of celestial mechanics, the idea of equilibrium branched out into many fields, among them economics. Out of celestial mechanics came the law of supply and demand. Alfred Marshall proposed this in Principles of Economics (1890). From Bachelier who advanced the idea that the “mathematical expectation of the speculator is zero”, Samuelson advanced his idea of shadow prices as the rational expectation hypothesis which Eugene Fama translated into Modern Portfolio Theory and the Efficient Market Hypothesis. But the Santa Fe Institute proposes that the market is organic rather than mechanistic. While equilibrium may be in the market, or rationality may be in the market, disequilibrium and irrationality are also present. It is not in a constant state as Newton might suggest but rather a constantly evolving state. While this mental model may occasionally work, we need to combine it with others.


The stock market is best understood in aggregate as an evolving biological organism. Thomas Malthus proposed the connection between food supply adn human population in which food production grows arithmetically while population grows geometrically. In applying this idea to his observations, he came up with social natural selection or Social Darwinism. It is likely Alfred Marshall when writing “Natura non facit saltum” (Nature does not make leaps), Darwin’s motto, Marshall believed economics needed to be seen in a Darwinian light. While it was unclear where Marshall stood, Joseph Schumpeter proposed that economics is an evolutionary process. Economics is a complex adaptive system that has dispersed interaction, no global controller, continual adaptation, out of equilibrium dynamics and feedback loop characteristics. This is biological economics. Is this an equilibrium of sorts? Yes, in a sense it is both because the market is neither exclusively efficient or behavioral . The behavior of the economy is the outcome of interactions between our logical faculties and our emotional faculties. When these are in balance, markets are relatively efficient (I would say this is more the rule than the exception). As living systems make themselves up as they go along, so does the stock market not maintain a stable mean / equilibrium. Newton is linear, Darwin is nonlinear. Because the market can be seen as a complex adaptive system, the behavior of the system must be studied as a whole because the whole is greater than the sum of its parts. It demonstrates emergent behavior. To understand the market is not as easy as understanding its parts. In contract to Newton and physics, we cannot break it down into the individual forces or influences on the market but must take the picture in its entirety.


To the realm of Charles Mackay and Joseph de la Vega where Vega’s four principles of trading are still often true:

1. be shrewd

2. avoid hindsight bias

3. profits come from anywhere, open mandate

4. need patience, endurance and calmness

In sociology, the market is understood by group behavior. Need to understand the whole because as biology and the Santa Fe Institute have shown, the whole is greater than the sum of the parts. Social sciences have followed two paths, a drive for a unified theory (Auguste Comte) and a push for narrower specialization. Comte argued for a single study of society because society is an individual entity. Despite his plea, the first individual speciality emerged in economics with Adam Smith’s Wealth of Nations in 1776. In proposing laissez faire he did acknowledge the social (class) consequences. This eventually brought responses from the likes of Karl Marx who looked further at how capitalism would pass to a more humane capital system

This interplay between economics and society brought about the political scientists who investigated the role of the state. Anthropology further investigated how man had developed physically and culturally. The twentieth century brought about social psychology and biology. In social biology, social Darwinism became very popular until it disappeared after WWII.

These are all mediums to look at how humans form groups. In order to study this, the concept of complexity is used. In Kant’s wording from Critique of Judgement, how do people self-organize? Complex adaptive systems are self organizing, self reinforcing and exhibit emergent behavior. Self organized systems occur from simple connected local processors, solutions arise from diversity of individual inputs and functionality is greater than the sum of the parts. An example of emergence is ant behavior laying down pheromone trails.

To exhibit smart crowd behavior you need diversity and independence. Information cascades because diversity breaks down. There is also self-organized criticality, systems can break down from one event as well as a series of small events. Think Per Bak, Danish theoretical physicist, sand piles.

These systems are unstable when the agents do not share a common interpretation of available information. May be able to alter complex social systems. When people have different interpretations the instability seems to be a result of the impeding actions of followers.


2002 Nobel Prize in Economics goes to Daniel Kahneman for integrating the study of how the human mind works with investing. By suggesting that humans behave irrationally he upset thinkers and backers of MPT. Kahneman and Tversky upset utility theory with prospect theory which rested on loss aversion because people regret losses about two to two and a half times as much as they welcome gains of equal size. Loss aversion prevents investors from seeing long term.

Time period matters. When offered an unattractive price, ignore it. With more information, it is easier to fall prey to confirmation bias, Odean & Barber. Another error when constructing mental models is to focus on support, not opposing ideas. Easier to focus on what something is rather than what it is not (use for wine class!). We seek patterns and are superstitious, Phillip Sherman, Princeton. We also assume each model is equiprobable.

One must be aware of the psychology of misjudgments as Charlie Munger says because while we are good at coming up with answers, the answer is not always a good one. As humans we need to be aware and filter for our logical flaws and systematic flaws.


The term is derived from two Greek words that are generally translated as love and wisdom. This can be taken to mean a philosopher is a person who loves wisdom and is dedicated to the search for meaning which is an unending process. There are three categories of philosophy: metaphysics, the study of ideas that exist independent of space and time. The second field is split into aesthetics, ethics and politics. The third and our focus is epistemology, the theory of knowledge. It is thinking about thinking.

The importance of epistemology is similar to psychology in that for investing we must adopt an epistemological framework that constantly checks that our thinking is correct. An ontological issue is one that is impossible to understand where as an epistemological issue is one we currently don’t possess the knowledge to understand. Failure to explain is caused by failure to describe, Benoit Mandelbrot.We describe with words and our ability to explain is limited by our descriptions.

With stories we suspend belief in order to be entertained but with statistics we are less willing in order that we are not duped. In investing you must get used to redescriptions. Through the concept of pragmatism, William James argued that truth in statements and rightness in action are defined by their practical outcomes. Therefore, int he spirit of Darwin our understanding of truth evolves. Truth is a verb, not a noun. Pragmatism in terms of our mental models would be to constantly reevaluate and reweight which models are useful in light of their practical outcomes. Pragmatism is a way of doing philosophy, not a philosophy.

The successful investor should enthusiastically examine every issue from every possible angle, from every possible discipline, to get the best possible description or redescription of what is going on. Only then is an investor in a position to accurately explain.

Therefore it is not about having an informational advantage in investing but rather a description advantage. In the current market when it is the exception that a stone is unturned, the edge comes from seeing information that everyone has access to in a unique light. That is to say, being able to see the correct interpretation out of the possible interpretations.


Read for understanding in addition to reading for information. Keep in mind Adler’s four questions:

1. What is the book about as a whole?

2. What is being said in detail?

3. Is the book true, in whole or part?

4. What of it?

Must be aware that philosophical questions must be answered personally otherwise you are evading them. Imaginary books convey an experience. Don’t try to resist the effect an imaginative book has on you. Imaginative stories also exhibit emergence. Reading of these experiences in imaginative literature helps us to be better investors because we learn from these experiences. In literature we find a dramatization of the complexity of events.

If your education gives you specific, practical knowledge but not broad understanding, then it is up to you to fill in the rest through intelligent, analytical reading. You can’t just read, you must reflect on what you have read in order to absorb it. You need a curious persistence. (Munger)


John Burr Williams believed prices in a fundamental market were ultimately a reflection of the assets’ values, not the collective view of capital gains. He is credited with inventing the idea of DCF.

Risk can be traced back to 1654 France where Chevalier de Mere, a noble gambler who challenged Blaise Pascal to divide the stakes of an unfinished game of chance when one player is ahead. Pascal and Fermat worked together using geometry and algebra to come up with decision theory.

Bernoulli in thinking about the quality of the information and probabilities explained that nature’s patterns are only partly established so probabilities are more degrees of certainty rather than absolute.

Bayes put the foundation developed by Pascal, Fermat and Bernoulli into practical action. He came up with initial beliefs + recent objective data = a new and improved belief. Priori is the probability of the initial belief, likelihood for the probability of a new hypothesis and posterior for the probability of a newly revised belief. Again, we are Darwin and pragmatism. In machine learning this can be thought of as a decision tree.

If a criticism of DCF is that it is a linear extrapolation in a nonlinear world, then the solution is to come up with many scenarios and weight them by how probable they appear.

Kelly formula is 2p-1 = x. Investors and humans have a strong desire to identify trends which often leads us to detect a directionality that does not exist.

Horace (65-8 BCE) wrote down regression to the mean in writing “Many shall be restored that now are fallen and many shall fall that are now in honor”. Joseph telling Pharaoh that seven years of famine would follow seven years of plenty.

Why is forecasting difficult?

1. Time period

2. Hard to know when reversion to the mean has occurred

3. The mean is changing, markets are biological systems

Frank Knight who is credited with founding the Chicago School of Economics distinguished between risk and uncertainty. Risk involves situations with unknown outcomes but known probability distributions. Uncertainty is when we don’t know either.

Taleb describes black swans as:

1. outlier

2. extreme impact

3. post implied causality and predictability

Taleb writes that people believe we live in mediocristan where probability is a bell-shaped curve but we actually live in extemistan. History does not crawl, it jumps. “We are never certain, we are always ignorant to some degree” Peter Bernstein.

Decision Making

System 1 thinking is intuitive where as system 2 thinking is reflective. System 2 requires concentration and does a poor job of monitoring system one thinking. When to use intuition?

1. the environment must be sufficiently regular to be predictable

2. there must be opportunity to learn these regularities through prolonged practice

Intuition is recognition, Kahneman. Experts suffer from overconfidence, hindsight bias, belief system defense and lack of Bayesian process which can be interpreted as an inability to evolve.

Sir Isaiah Berlin wrote that the hedgehog views the world through a single idea where as the fox draws on a wide variety of experiences. Foxes appreciate the limits of their own knowledge. Foxes have three distinct cognitive advantages:

1. They begin with reasonable starter probability estimates; better inertial guidance systems

2. They are willing to acknowledge mistakes and update views in response to new information

3. They can see the pull of contradictory forces.

Humans are cognitive misers because our basic tendency is to default to least computational effort. Decisions through entropy. System 2 requires self control.


We must teach ourselves our own metacurriculum, using metaphors to move from what we understand to what we do not. We must be like ants, native american hunts and Norwegian fishermen; exploiting what we know but also allocating some capital to exploring new possibilities. If we cannot accurately describe a phenomenon then it will be hard to accurately explain a phenomenon.