• Authored and posted by Peter Moore, Bullet Point Network, L.P.
If you want to improve the odds for the future valuation multiples of your company and your investments, you may want to look through our illustration of how we can all do so by Connecting Stories to Statistics in 3 Steps. Here we illustrate Step 1 for a future event involving a fictitious company that we call virtualgoods.shop. Any resemblance to actual companies, living or dead, or actual events is purely coincidental . . .
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  • Authored on Sep 18, 2018 by Stephen Blyth, Harvard University
  • Posted on Dec 19, 2018 by Peter Moore, Bullet Point Network, L.P.
The lesson for finance is stark. If our dataset, however large, is in a minimal but systematic way not representative of the population, big data does not preclude big problems. Those who revert to a proceduralist approach of throwing complex algorithms and large datasets at challenging questions are particularly vulnerable. Who can tell how non-representative our data today is in terms of representing the future? Yes, we may never again assume house prices cannot fall simultaneously in every state, but we do not know what other assumptions are implicitly being made. More than ever, judgment — necessarily subjective and based on experience — will play a significant role in moderating over-reliance on and misuse of quantitative models. The judgment to question even the most successful of algorithms, and to retain humility in the face of irreducible uncertainty, may prove the difference between financial stability and the “horrific damage” of another crisis.
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  • Authored on Nov 14, 2018 by Ray Dalio, Bridgewater Associates
  • Posted on Nov 17, 2018 by Peter Moore, Bullet Point Network, L.P.
This combination of man and machine is wonderful. The process of man’s mind working with technology is what elevates us—it’s what has taken us from an economy where most people dig in the dirt to today’s Information Age. It’s for that reason that people who have common sense, imagination, and determination, who know what they value and what they want, and who also use computers, math, and game theory, are the best decision makers there are. At Bridgewater, we use our systems much as a driver uses a GPS in a car: not to substitute for our navigational abilities but to supplement them. In contrast, the main thrust of machine learning in recent years has gone in the direction of data mining, in which powerful computers ingest massive amounts of data and look for patterns. While this approach is popular, it’s risky in cases when the future might be different from the past.
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  • Authored on May 22, 2018 by Eugene Wei, Amazon.com, Inc.
  • Posted on Nov 13, 2018 by Peter Moore, Bullet Point Network, L.P.
It didn't take long for me to see that our visibility out a few months, quarters, and even a year was really accurate (and precise!). What was more of a puzzle, though, was the long-term outlook. Every successful business goes through the famous S-curve, and most companies, and their investors, spend a lot of time looking for that inflection point towards hockey-stick growth. But just as important, and perhaps less well studied, is that unhappy point later in the S-curve, when you hit a shoulder and experience a flattening of growth.
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  • Authored on Feb 6, 2018 by Annie Duke, World Series of Poker
  • Posted on Nov 12, 2018 by Peter Moore, Bullet Point Network, L.P.
For us to make better decisions, we need to perform reconnaissance on the future . . . When I consult with enterprises on building decision trees and determining probabiltiies of different futures, people frequently resist having to make a guess at the probability of future events mainly because they feel like they can't be certain of what the likelihood of any scenario is. But that's the point. The reason why we do reconnaissance is because we are uncertain. We don't (and likely can't) know how often things will turn out a certain way with exact precision. It's not about apporaching our future predictions from a point of perfection. It's about acknowledging that we're already making a prediction about the future every time we make a decision, so we're better off if we make that explicit . . . By at least trying to assign probabilities, we will naturally move away from the default of 0% or 100%, away from being sure it will turn out one way and not another. Anything that moves us off those extremes is going to be a more reasonable assessment than not trying at all.
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  • Authored on Feb 15, 1994 by Charlie Munger, Berkshire Hathaway Inc.
  • Posted on Nov 11, 2018 by Peter Moore, Bullet Point Network, L.P.
If you don't get this elementary, but mildly unnatural, mathematics of elementary probability into your repertoire, then you go through a long life like a one-legged man in an ass-kicking contest. You're giving a huge advantage to everybody else. One of the advantages of a fellow like Buffett, whom I've worked with all these years, is that he automatically thinks in terms of decision trees and the elementary math of permutations and combinations.
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  • Authored on Oct 31, 2018 by Ray Dalio, Bridgewater Associates
  • Posted on Nov 9, 2018 by Peter Moore, Bullet Point Network, L.P.
Think of every decision as a bet with a probability and a reward for being right and a probability and a penalty for being wrong. Normally a winning decision is one with a positive expected value, meaning that the reward times its probability of occurring is greater than the penalty times its probability of occurring, with the best decision being the one with the highest expected value.
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  • Authored on Sep 23, 2017 by David Teten, PEVCTech
  • Posted on Nov 9, 2018 by Peter Moore, Bullet Point Network, L.P.
Private equity and venture capital investors are copying our sisters in the hedge fund and mutual fund world: we’re trying to automate more of our job. But we’re doing it slowly.
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  • Authored on Jul 11, 2017 by Aswath Damodaran, New York University
  • Posted on Nov 2, 2018 by Peter Moore, Bullet Point Network, L.P.
What comes more naturally to you, story telling or number crunching? That is the question that I start every valuation class that I teach and my reasons are simple. In a world where we are encouraged to make choices early and specialize, we unsurprisingly play to our strengths and ignore our weaknesses. I see a world increasingly divided between number crunchers, who have abandoned common sense and intuition in pursuit of data analytics and complex models and story tellers, whose soaring narratives are unbounded by reality. Each side is suspicious of the other, the story tellers convinced that numbers are being used to intimidate them and the number crunchers secure in their belief that they are being told fairy tales. It is a pity, since there is not only much that each can learn from the other, but you need skills in investing and valuation. I think of valuation as a bridge between stories and numbers, where every story becomes a number in the valuation and every number in a valuation has a story behind it.
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  • Authored on Aug 1, 2016 by Ilya Strebulaev, Stanford Graduate School of Business
  • Posted on Nov 2, 2018 by Peter Moore, Bullet Point Network, L.P.
Abstract of Stanford GSB Working Paper No. 3492: We survey 885 institutional venture capitalists (VCs) at 681 firms to learn how they make decisions across eight areas: deal sourcing; investment selection; valuation; deal structure; post-investment value-added; exits; internal firm organization; and relationships with limited partners. In selecting investments, VCs see the management team as more important than business related characteristics such as product or technology. They also attribute more of the likelihood of ultimate investment success or failure to the team than to the business. While deal sourcing, deal selection, and post-investment value-added all contribute to value creation, the VCs rate deal selection as the most important of the three. We also explore (and find) differences in practices across industry, stage, geography and past success. We compare our results to those for CFOs (Graham and Harvey 2001) and private equity investors (Gompers, Kaplan and Mukharlyamov forthcoming).
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