Mathias Brandewinder on .NET, F#, VSTO and Excel development, and quantitative analysis / machine learning.
by Mathias 8. July 2010 11:03

A new method of forecasting has been brought to my attention: Paul. Paul is an English-born octopus, currently living in Germany, and has been predicting with high accuracy the results of the German soccer team, by picking between two boxes marked with the flag of the two competing teams:

How unlikely is it that Paul would have such a string of correct forecasts? Pretty unlikely. If you assume that Paul’s picks were completely random, with a 50% chance of correctly calling the winner, the probability of making 11 good calls out of 12 comes down to 0.29%. Does this mean Paul is the next big thing in forecasting? It’s possible, but I don’t think so (this said with all due respect to Paul and his skills). Leonard Mlodinow, in his excellent book, The Drunkard's Walk, makes the following comment:

This example illustrates an important point: even with data significant at, say, the 3 percent level, if you test 100 nonpsychic people for psychic abilities […], you ought to expect a few people to show up as psychic.

Mallarme In other words, if a phenomenon is random, you should typically see the “average” case regularly, but you should also see highly unlikely cases happen from time to time – observing such a rare occurrence doesn’t contradict the randomness of the phenomenon. Or, in the words of the French poet Mallarmé, Un Coup de Dés Jamais N'Abolira Le Hasard (A throw of the Dice will Never Abolish Chance).

by Mathias 12. October 2008 10:52

Via Twitter, an interesting NYT piece on political prediction markets. Just like real-life market, they are not immune to unscrupulous manipulation attempts. Apparently, in recent days, the McCain value has had odd fluctuations, possibly indicating agents trying to artificially boost its "price". But how can you recognize a regular fluctuation from an artificial manipulation? The New-York Times piece notes that:

The biggest difference between typical market movements and manipulation is that honest traders will usually try to minimize the impact of their trades on the market price; paying higher prices for an asset only cuts into profits. But a market manipulator, intent on buoying the market’s ratings of their preferred candidate, will work to maximize the impact of their trading on the price.

More on price manipulation and prediction markets here.

Edit, Oct 12, 18:43: and how the financial crisis could have been adopted with prediction markets here...

by Mathias 1. October 2008 17:22

In my previous post, I described how the Bass model can be used to forecast the market potential for a newly introduced product, using limited post-introduction data. In this post, I will apply the method to a real-world situation, to see how the method holds up in practice, what practical problems may arise, and how to address them.

The data

My objective is to evaluate the long-term share of internet traffic of Chrome, the new Google browser. I will be using actual traffic data from a medium-sized website, the technology blog of Donn Felker. In case you wonder why I didn’t use my own data, unfortunately my own traffic is not steady enough to get a “statistically decent” sample of Chrome users, and Donn was gracious enough to share his data with me (Thank you!).

The data I will be using is the percentage of visits coming from users using Chrome as a browser. It covers September 2 to September 17, 2008, the 2 first weeks of Chrome on the market.



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