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Geither Plan Explained

Posted in Finance, NYTimes by Pankaj Gudimella on March 27, 2009

Leave on one side the question of whether the Geither plan is a good idea or not. One thing is clearly false in the way it’s being presented: administration officials keep saying that there’s no subsidy involved, that investors would share in the downside. That’s just wrong. Why? Because of the non-recourse loans, which reportedly will finance 85 percent of the asset purchases.

Let me offer a numerical example. Suppose that there’s an asset with an uncertain value: there’s an equal chance that it will be worth either 150 or 50. So the expected value is 100.

But suppose that I can buy this asset with a nonrecourse loan equal to 85 percent of the purchase price. How much would I be willing to pay for the asset?

The answer is, slightly over 130. Why? All I have to put up is 15 percent of the price — 19.5, if the asset costs 130. That’s the most I can lose. On the other hand, if the asset turns out to be worth 150, I gain 20. So it’s a good deal for me.

Notice that the government equity stake doesn’t matter — the calculation is the same whether private investors put up all or only part of the equity. It’s the loan that provides the subsidy.

And in this example it’s a large subsidy — 30 percent.

The only way to argue that the subsidy is small is to claim that there’s very little chance that assets purchased under the scheme will lose as much as 15 percent of their purchase price. Given what’s happened over the past 2 years, is that a reasonable assertion?

Source:NYTimes

FT has a 3 min video explaining the plan.

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Data mining in the credit crisis

Posted in Analytics, Business Intelligence, Data Mining, NYTimes by Pankaj Gudimella on February 2, 2009

In recent months, American Express has gone far beyond simply checking your credit score and making sure you pay on time. The company has been looking at home prices in your area, the type of mortgage lender you’re using and whether small-business card customers work in an industry under siege. It has also been looking at how you spend your money, searching for patterns or similarities to other customers who have trouble paying their bills.

More here

Guessing the Online Customer’s Next Want

Posted in Data Mining, NYTimes, Online Marketing by Pankaj Gudimella on May 19, 2008

Marketers have always tried to predict what people want, and then get them to buy it.

Among online retailers, pushing customers toward other products they might want is a common practice. Both Amazon and Netflix, two of the best-known practitioners of targeted upselling, have long recommended products or movie titles to their clientele. They do so using a technique called collaborative filtering, basing suggestions on customers’ previous purchases and on how they rate products compared to other consumers.

Figuring that out is not so easy. For one thing, people do not always buy what they like. Someone may buy a sweater for their grandmother even though they dislike it and would never get it again. Similarly, a person who rents a movie may actually detest it but knows her child likes it. Or a film that was seen on a small airplane screen may garner a lower rating than if it were seen at a large multiplex.

More here from NYTimes.

ONLINE SEARCH ADS FARING BETTER THAN EXPENSIVE DISPLAYS

Posted in Analytics, NYTimes, Online Marketing by Pankaj Gudimella on May 19, 2008

In the past few years, Web publishers have made a big bet on booming online advertising revenues. But the economic slowdown may be throwing a wrench into those plans.

While search advertising remains strong, there are signs that the growth in online advertising — particularly in more elaborate display ads — is slowing down. In the past few weeks, major online-advertising players, like Yahoo and Time Warner, have posted mixed results.

And online publishers may be getting less money for the ad space they do sell. The prices paid for online ads bought through ad networks dropped 23 percent from March to April, according to PubMatic, an advertising-technology company in Palo Alto, Calif., that runs an online-pricing index. Large Web publishers fared the worst in PubMatic’s study, with the prices they received through networks dropping 52 percent.

More here from NYTimes.