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Slide 1
Business Strategy, Luck, and Poor Judgment Jerker Denrell,
Stanford Christina Fang, NYU
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Strategizing and forecasting Accurate forecasting of the value
of resources is the only systematic way to earn above normal
returns Barney, 1986, 1990
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Forecast accuracy versus skill Skill Accuracy
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I think there is a world market for about five computers.
Thomas J. Watson, Chairman of IBM, 1943 Accurate vs Good
Forecasts
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I think there is a world market for about five computers.
Thomas J. Watson, Chairman of IBM, 1943 I think there is a world
market for about five billion computers. Thomas Crank, Chairman of
Wild Ideas, 1943 Accurate vs Good Forecasts
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I think there is a world market for about five computers.
Thomas J. Watson, Chairman of IBM, 1943 Accuracy: bad Judgment:
good I think there is a world market for about five billion
computers. Thomas Crank, Chairman of Wild Ideas, 1943 Accurate vs
Good Forecasts
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I think there is a world market for about five computers.
Thomas J. Watson, Chairman of IBM, 1943 Accuracy: bad Judgment:
good I think there is a world market for about five billion
computers. Thomas Crank, Chairman of Wild Ideas, 1943 Accuracy:
good Judgment: bad Accurate vs Good Forecasts
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If someone predicts that an activity will be very successful
Accurate vs Good Forecasts
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If someone predicts that an activity will be very successful
and the prediction turns out to be correct Accurate vs Good
Forecasts
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If someone predicts that an activity will be very successful
and the prediction turns out to be correct the individual is
probably a poor forecaster Accurate vs Good Forecasts
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If someone predicts that an activity will be very successful
and the prediction turns out to be correct the individual is
probably a poor forecaster Accurate foresight about the next big
thing signals bad judgment Accurate vs Good Forecasts
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Skill Accuracy Forecast accuracy versus skill
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Skill Accuracy Forecast accuracy versus skill
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Model You observe a noisy signal:
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Model You observe a noisy signal: S = m + error
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Model You observe a noisy signal: S = m + error
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Model You observe a noisy signal: S = m + error
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Model Task: Make a Forecast
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Model Task: Make a Forecast Forecast = b*S
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Model Task: Make a Forecast Forecast = b*S Bayesian b =
0.5
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Model Task: Make a Forecast Forecast = b*S Bayesian b = 0.5
Overreactor b = 1
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If m = 0, distribution of forecasts Forecast
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If m = 0, distribution of forecasts Bayesian Forecast
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If m = 0, distribution of forecasts Bayesian Overreactor
Forecast
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If m = 3, distribution of forecasts
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Bayesian Forecast
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If m = 3, distribution of forecasts Bayesian Overreactor
Forecast
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Accuracy vs Expected Accuracy Mean Square Error (MSE) E[ (
forecast actual) 2 ]
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If m = 3, what is the expected MSE given the forecast?
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Forecast
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If m = 3, what is the expected MSE given the forecast?
Forecast
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Experiment You observe a noisy signal: S = m + error Task: Make
a Forecast
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Experiment You observe Test Sales Task: Make a Forecast of the
Actual Sales
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Experiment You observe Test Sales Task: Make a Forecast of the
Actual Sales Can observe 50 previous test sales and actual
sales
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Experiment Artist Test Actual 100158.6275.33 100227.6816.17
100363.8170.79 100447.8646.46 100543.9464.67 100648.0544.31
100771.0452.03 100854.651.37 .
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Experiment Artist Test Actual 100158.6275.33 100227.6816.17
100363.8170.79 100447.8646.46 100543.9464.67 100648.0544.31
100771.0452.03 100854.651.37 . Test i = m i + error (mean of m i
=50) Actual i = m i + error
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Suppose the actual outcome is between 40 and 60
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Suppose the actual outcome is above 60
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Conclusion Accurate Foresight about the Next Big Thing Signals
Bad Judgment Are entrepreneurs who made money by betting on a
vision that turned out to be correct worse forecasters?