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3 In an ideal world, you d like to be able to measure the social interfaces in relation to the number of actual transactions For example, suppose you offer a podcast and a discussion with your customers about, say, accounting software You include a Q&A session with the editor from Accounting Today as a guest speaker You can now measure EveryZingcom is one tool the number of requests for information and product downloads You can ask people how they found out about you before allowing them to download information or podcasts The content I ve been talking about in this book creates an information circus When you market to the social web, you have many things going on at once (professionally generated content, user-generated content, interactivity, everything else I ve mentioned in earlier chapters).

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Does this circus of social interaction really have the payoff of pulling people through to try things or buy things I believe it does For example, Ren Algesheimer, an assistant professor of marketing at the University of Zurich, and Paul M Dholakia, an associate professor of management at Rice University s Jesse H Jones Graduate School of Management, designed a year-long eld experiment to nd out Collaborating with eBay Germany s managers, they identi ed 140,120 active eBay users, people who had bought or sold something.

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Unfortunately, neither historical VaR nor traditional price risk management tools tell us anything regarding the probability of losses over the next 24 hours for our current portfolio holdings. By contrast, forward-looking (parametric and simulation) VaR models apply statistical measures, such as standard deviation and correlations, to current portfolio holdings to estimate probability distributions of losses for a particular holding period (i.e., 24 hours). Subsequently, forward-looking VaR models can effectively account for how the addition of a particular position to current portfolio holdings will either reduce or increase portfolio risk (due to its negative or positive correlation to the existing portfolio).6

The rst example is a content service, which is accessed via a mobile endpoint such as a GPRS/WCDMA handset. We are assuming a cellular endpoint since discussion about service quality and security is more clear-cut within 3GPP R99+ framework. The mobile endpoint uses the Public Land Mobile Network (PLMN) of a cellular operator for connecting to services. Charging is performed at an Intelligent network edge element. Provided that GPRS/WCDMA roaming agreements are in place, services can be accessed either from the home PLMN or from a visited PLMN. The overall set-up is illustrated in Figure 6.4, and there will be more discussion about this kind of set-up in 8. It is assumed that there are different kinds of content available, including the following:

where is the stochastic order. We nish this section by stating without proof two related results having important applications.

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maximum number of acquirer shares, that must be exchanged for target shares on completion of the merger collateral In short selling, a cash deposit or a deposit of high-grade, liquid securities provided to the securities lender against delivery of the securities that were borrowed to sell short More generally, the security held against a loan and used to secure the obligation of a borrower to repay the loan collateralized mortgage obligation (CMO) A type of mortgage-backed bond where the repayments of principal are separated into different maturity streams Commodity Futures Trading Commission (CFTC) The US federal regulatory agency that oversees and regulates US markets for futures and options on futures to help insure market integrity and protect market participants constant yield method A method for allocating discount over the term of a debt instrument issued with original issue discount.

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