Misconceptions About Valuation

 In a 2012 Valuation Roundtable of San Francisco’s 26th Annual Seminar, keynote speaker Aswath Damodaran (Professor of Finance at the NYU Stern School of Business) presented the myths of valuation.

Myth One: A valuation is an objective search for “true” value

• Truth: All valuations are biased. The only questions are how much and in which direction.
• Truth: The direction and magnitude of the bias in a valuation is directly proportional to who pays the valuator and how much that valuator is paid.

Myth Two: A good valuation provides a precise estimate of value

• Truth: There are no precise valuations.
• Truth: The payoff to valuation is greatest when valuation is least precise.

Myth Three: The more quantitative a model, the better the valuation

• Truth: One’s understanding of a valuation model is inversely proportional to the number of inputs required for the model.
• Truth: Simpler valuation models do much better than complex ones.

Sources:

1. Blog; Aswath Damodaran Explains 3 Misconceptions About Valuation
2. Original presentation
3. PPT Presentation

New Survey Methods: Tools to Dig for Gold

Surveys are widely used by scholars, companies, and public policymakers to generate invaluable insights. Despite the popularity of surveys, there are several biases that can affect the validity of self-reported data. In his inaugural address,

Martijn de Jong discusses how new survey methods can help to extract valid information from surveys. Several examples are presented that showcase the relevance of better research design and careful statistical modeling of the response process. In addition, De Jong addresses some commonly held perceptions about the ability to make causal inferences with survey data.

From the report:
Let’s go back in history a bit to see why survey researchers make allusions to causality. In an Econometrica paper from the 1970s, Goldberger defines a structural equation as one representing a causal relationship, as opposed to a relationship that simply captures statistical associations (Goldberger 1972). The article’s conclusion contains a fascinating sentence: 

“economic, sociological, psychological, and political theory all have something to say about the causal links…” (p. 999).........


Download (pdf): New Survey Methods: Tools to Dig for Gold

Source: RePub

Crossing the street in traffic

Crossing the street in traffic....
We've all done this: you’re in a hurry, so instead of waiting for the “walk” sign you look both ways and see that the nearest cars are far enough away that you can cross safely before they arrive where you are. You start walking and (I’m guessing) make it across just fine.

1. Did you know (with absolute certainty) that the cars you saw in the distance weren't moving fast enough to hit you? If so, how did you come to know this? If not, how could you possibly justify making a decision like this, given the extremely high stakes? After all, you were literally betting your life ...
2. Can logic help us understand how a rational person could make a risky decision like this, despite not having perfect knowledge of all relevant factors?

The street-crossing example is chosen for the vivid consequences of making a wrong decision,
but less dramatic examples would make the point. We almost never know with absolute certainty what the consequences of our actions will be, but we usually manage to make reasonably confident decisions nonetheless — and most of the time we choose right. This needs explaining.

Original Source:
Probabilistic reasoning and statistical inference:
An introduction (for linguists and philosophers)

What is Risk?

Sometimes it makes sense to go back to to your roots and ask yourself, what is RISK really?

Enjoy!

Original Source: What is Risk? (pdf)