More Than You Know: Finding Financial Wisdom in Unconventional Places by Michael Mauboussin

Summary

A primer on mental models in investing. Easier to read and more entertaining than Seeking Wisdom, but less than half the depth and breadth. The second half is repetitive and redundant.

Key Takeaways

• Character traits that encourage mental models: intellectual curiosity, integrity, patience and self-criticism.
• Quality investment philosophies:
  • Focus on the decision-making process over the short-term outcome
  • A long-term perspective
  • A probabilistic approach
• Focus on expected value. It forces the consideration of multiple scenarios and mitigates biases.
• Three ways to get to a probability: degrees of belief, propensities, frequencies.
• Increasing your success rate in probabilistic games: focus, examine lots of situations, know that opportunities are limited, play only when the odds favour you.
• Three areas to assess a management’s capabilities when investing:
  • Leadership
    • Learning and intellectual curiosity
    • Teaching
    • Self-awareness
  • Incentives
  • Capital allocation skills (past results) and decision-making framework
• Stress stems from a loss of predictability and a loss of control.
• Overcome Cialdini’s 6 tendencies of influence: 
  • Consider multiple scenarios with associated probabilities (counter-commitment)
  • Expose yourself to diversity in opinion (counter-social validation)
  • Reverse-engineer market expectations to realize if you truly have an informational edge (counter-scarcity)
• Overproduction + pruning is the most flexible and reliable process at preserving information.
• Have between 2 to 7 rules to define direction when running a company.
• Don’t confuse correlation with causation.

What I got out of it

When investing in businesses, projects or opportunities, consider expected value and different scenarios. 
Expose yourself to diverse sources of information.

Play when the odds favour you. Don’t know the odds and cannot approximate it, better not to play.

In a nutshell: read Peter Bevelin’s Seeking Wisdom instead.

Summary Notes

A balanced perspective cannot be acquired by studying disciplines in pieces but through the pursuit of the consilience among them.
You will be a better investor, executive, parent, friend – person – if you approach problems from a multidisciplinary perspective.

Character traits that encourage mental models: intellectual curiosity, integrity, patience and self-criticism.

Investment Philosophy

A quality investment philosophy is like a good diet: it only works if it is sensible over the long haul and you stick with it.
It will not help you unless you combine it with discipline and patience – it is more about temperament than raw intelligence.

Quality investment philosophies have in common:

• A focus on the decision-making process over the short-term outcome
• A long-term perspective
• A probabilistic approach

A focus on probability is sound when outcomes are symmetrical, but completely inappropriate when payoffs are skewed. 
What matters is not the frequency of correctness but the magnitude of correctness.
In other words, asymmetrical return.
A focus on expected value is thus more appropriate.

Expected value is the weighted-average value for a distribution of possible outcomes.
Calculation: multiply the payoff for a given outcome by the probability that the outcome materializes.

Thinking in expected value forces the consideration of multiple scenarios, mitigating behavioural pitfalls such as anchoring and confirmation bias.

It’s not about the most likely winner, but what offers odds that exceed its actual chances of victory.

With both uncertainty and risk, the outcomes are unknown.
The difference:

• Uncertainty: the underlying distribution of outcomes is undefined. Probability becomes subjective. It may or may not include a chance of loss.
• Risk: the distribution is known. Probability is objective. Includes a notion of loss.

Three ways to get to a probability:

• Degrees of belief – subjective probabilities and meant to translate uncertainty into a probability.
• Propensities – reflect the properties of the object or system. Think a die with a 1-in-6 probability of rolling a particular side. Does not always consider all factors that shape an outcome (e.g. human error).
• Frequencies – probability is based on a large number of observations in an appropriate reference class.

Prospect theory shows that our behaviour is irrational.

• Loss aversion: we regret losses 2.5x more than similar-sized gains.
• Myopia: the more frequently we evaluate our portfolios, the more likely we are to see losses and hence suffer from loss aversion.
• Affect: the “goodness” or “badness” we feel based on a stimulus. Shapes our impressions of events and makes us overweight/underweight probabilities.

Increasing your success rate in probabilistic games:

• Focus – learn the ins and outs, seek a competitive edge and stick to your circle of competence
• Examine lots of situations – exposure and experience matter
• Know that opportunities are limited, i.e. favourable situations in which you have an edge
• Ante – deciding when to play (or increase your bet) only when the odds favour you

Building a theory:

1. Describe what you want to understand in words and numbers
2. Classify the phenomena into categories based on similarities
3. Build a theory that explains the behaviour of the phenomena – explains cause-and-effect, why the cause-and-effect works, under what circumstances the cause-and-effect operates, and it must be falsifiable.

Anomalies force you to revisit steps 1 and 2 and refine your theory.
Sound theory reflects context.

Financial markets are not normally distributed but follow power laws with “fat tails”: infrequent but very large price changes.

Deliberate practice allows experts to internalize many of their domain’s features, but can also lead to reducing cognitive flexibility. Reduced flexibility leads to deteriorating performance as problems go from simple to complex.
Two potential issues:

• Functional fixedness: when we use or think about something in a particular way, we have great difficulty in thinking about it in new ways.
• Reductive bias: we tend to treat nonlinear, complex systems as if they are linear, simple systems. A common resulting error is evaluating a system based on attributes versus considering the circumstances.

Expert performance is largely a function of the type of problem the expert addresses.
What matters is how you think and make decisions. Diverse information and perspectives are important.

Long success streaks happen to the most skilful in a field precisely because their general chance of success is higher than average.

Three areas to assess a management’s capabilities when investing:

• Leadership
  • Learning and intellectual curiosity
    • A constant desire to build mental models that can help decision-making
    • Desire to know how the company is performing and confronting facts with brutal honesty
    • Creating an environment where everyone can voice their opinion. Reward intellectual risk-taking.
  • Teaching
    • The ability to communicate a simple, clear vision to the organisation. Passion makes teaching easier.
  • Self-awareness – a balance between self-confidence and humility, and a measure of emotional intelligence
• Incentives – must link to the facets of business an employee can control
• Capital allocation skills
  • Study past capital allocation choices
    • Mergers and acquisitions deserve special mention
  • Interview managers to understand their capital allocation framework
  • Do they know how to put the right people in the right jobs

Psychology of Investing

Distinguish between individual and collective decisions.
In the case of markets: collective decisions are more instrumental as individual errors often cancel out.

Stress stems from a loss of predictability and a loss of control, where the common element is novelty.
Stress encourages a short-term focus.

Consider Cialdini’s six tendencies when evaluating investment decisions and your decision-making:

• Reciprocation
• Commitment and consistency
  • Allows us to avoid thinking
  • Allows us to avoid acting – not changing is easier than changing
• Social validation
• Liking
• Authority
• Scarcity

Tendencies can combine to create lollapalooza effects.
To help overcome these tendencies when investing:

• Consider multiple scenarios with associated probabilities (counter-commitment)
• Expose yourself to diversity in opinion (counter-social validation)
• Reverse-engineer market expectations to realize if you truly have an informational edge (counter-scarcity)

Positive and negative feedback mechanisms help a market function.

• Positive: promotes change, triggers imitation and herd behaviour, which can turn into snowball effects, cascades and amplification.
• Negative: stabilizing factor. Example in the market: arbitrage.

Imitation has a rational basis:

• Asymmetric information
• Agency costs
• Preference for conformity

Herding occurs when positive feedback (loops) gets the upper hand.

Markets can still be rational when investors are individually irrational. Sufficient investor diversity is the essential feature of efficient price formation.

Humans aren’t good at deductive logic (general premises to specific conclusions).
But we are superb at inductive logic (recognizing and matching patterns).

Beware hindsight bias – overestimating pre-event knowledge of an event.
Keep notes of why you make decisions at any given time that can be reviewed later.

Intuition is not a gift, but a skill that can be trained.
The capacity of our sensory system is 11 megabits per second; our conscious bandwidth is only 16 bits per second.
Can be trained by running simulations and scenario analyses with timely and clear feedback.

Our degree of belief in a particular hypothesis depends on:

• Strength – the extremeness – of the evidence
• Weight – predictive validity – of the evidence

Strength tends to dominate the weight of evidence in people’s minds, leading to over- and underconfidence.

Innovation and Competitive Strategy

Predictions about the future are likely to be wildly off the mark.
The only thing we can count on going forward is innovation.
We are terrible at dealing with change and extrapolate almost everything (based on past behaviour).

Neural networks and nature show that overproduction followed by pruning is more flexible and reliable at preserving information than a feed-forward network.
How to apply: start with lots of alternatives and winnow it down to the most useful ones. Accept the apparent inefficiency and ‘wasted’ time.

James Utterback’s 3 phases of industry innovation

1. Fluid phase – great experimentation by companies
2. Transitional phase – evolutionary forces select the dominant product design
3. Specific phase – changes in product or process become modest

Keep this in mind to not get caught up in – or on the other hand to take advantage of – manias and hype cycles.

Industry sales and earnings tend to follow an S-curve after a discontinuity or technological change. Shift your (investment) expectations accordingly.

4 behaviours consistent among chess champions and relevant in thinking through short-term vs long-term scenarios:

• Don’t look too far ahead. Look a few steps ahead. Any more than that is a waste of time due to too much uncertainty.
• Develop options and continuously revise them based on changing conditions. Good thinking is a matter of making comparisons.
• Know your competition. Become good at reading people and understanding human behaviour.
• Seek small advantages.

To succeed in life: become able to read other people and able to understand yourself.

Have between 2 to 7 rules when running a company, which define direction without containing it.
Types of rules:

• How-to rules: spell out key features of how a company should execute a process. It answers the question “What makes our process unique?”
• Boundary rules: focus managers on which opportunities they should pursue and which are outside the pale.
• Priority rules: help managers rank the opportunities they accept.
• Timing rules: synchronize managers with the pace of opportunities that emerge in other parts of the company.
• Exit rules: help managers decide when to pull out of yesterday’s opportunities.

3 types of industry landscapes:

• Stable: companies within these sectors primarily improve their fitness at the expense of their competitors. Tend to have structural predictability at the expense of limited opportunities for growth and new businesses. Discounted Cash Flow (DCF) model can be applied here.
• Coarse: the landscape is in flux, but the changes are not so rapid as to lack predictability. Runs the risk of being unseated by disruptive technology.
• Rolling: contains dynamic businesses with evolving business models, substantial uncertainty, and ever-changing product offerings. Consider strategic options over the DCF model.

For averages to be comparable over time, the statistical properties of the population must be the same, or stationary. If the properties of the population change over time, the data are nonstationary. When data are nonstationary, applying past averages to today’s population can lead to misleading conclusions.
Three drivers that make the price-earnings (PE) ratio nonstationary:

• Taxes and inflation
• Changes in the composition of the economy
• Shifts in the equity-risk premium

Focus on understanding how and why circumstances are different and their impact on the PE ratio, instead of accepting the PE ratio at face value.

Three questions to ask when doing your strategic analysis:

• Is the company generating returns on investment above the cost of capital, or is there good reason to believe it will earn attractive returns in the future?
  • A company’s return on investment reverts to the cost of capital over time.
• If returns do exceed the cost of capital, for how long can the company sustain its excess returns?
• Once a company’s returns dip below the cost of capital, what’s the probability it can stage a sustained recovery to above-required returns?

Competitive markets need not be zero-sum.
Cooperation between competitors is much more likely to evolve in an iterated prisoner’s dilemma because companies “learn” to work together.
Best strategy: tit-for-tat, which starts by cooperating and then uses the competitor’s prior move as its next move. Best to start, provides clear negative feedback for defection and is quick to forgive.

Science and Complexity Theory

To be an expert in a complex system like the stock market:

• Be able to create a “simulation” in your head, allowing you to conceive and select strategies
• Pursue diverse ideas as it improves the odds of finding a useful idea

Decentralized systems – like beehives and ant farms – are robust and very effective at solving complex problems.

Necessary conditions of information aggregation to work:

• Having an aggregation mechanism
• An incentive to answer correctly
• Group heterogeneity

St. Petersburg Paradox challenges classical theory, which says that a player should be willing to pay the game’s expected value to participate, with a scenario in which the expected value is infinite.
Question to ask yourself: what do you pay today for a business with a low probability of an extraordinarily high payoff?

Fractal: a geometric shape that can be separated into parts, each of which is a reduced-scale version of the whole. Humans prefer fractals with dimensions between 1.3 and 1.5.
Fractal systems – such as many natural systems – follow a power law and have (1) an ever-larger number of smaller pieces and (2) similar-looking pieces across the different size scales. As a result, average values don’t characterize these systems properly.

Complex systems exhibit the following properties:

• Aggregation: the emergence of complex, large-scale behaviour from the collective interactions of many less-complex agents
• Adoptive decision rules
• Nonlinearity: the aggregate behaviour is more complicated than would be predicted by totalling the parts
• Feedback loops – can amplify or dampen an effect

Complex systems, like the stock market, rarely have clear cause-and-effects. 
Don’t confuse correlation with causation.
The behaviour of the market and the behaviour of individual agents in such systems are not the same.

Sentiment shifts can be compared to how flu spreads:

• Degree of contagiousness – how easily an idea spreads
• Degree of interaction – how much people bump into one another

---