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In cases where you are moving information from one persistent store to another you may intentionally duplicate the data into the message for transport. For example, if you are synchronizing two databases with JMS, the data will be in the first database, then in the first database and the message, then in the first and second database. This isn t overreliability; it is just a simple and clean design. Of course, you wouldn t delete the data from the first database to send the message because you want it there as well. But, what you are doing is trusting the message with the copy of the data that belongs to the second database. You might also encounter situations where you are doing work with data before sending a message and expecting some form of reply. This is common in a session bean or servlet where, for example, you are using data about a customer, then send some of that data in a message, but keep a copy for continued use in the bean or servlet. Or you may be doing work between the send and reply. In these cases, you may save data persistently, even though it is in the message, to ensure that the sender has a valid copy.

TABLE 3.13 MACD with profit and stop exit of $2,500.

Tube 3 closed (a) (b)

public class AccountBean implements EntityBean { // Account PK private String identifier; // 1:1 Product bean reference private Product product; // 1:M TransactionEntry bean references. private Collection transactionEntries; public Product getProduct() { return product; } public void setProduct(Product product) { this.product = product; } public Collection getTransactionEntries() { return transactionEntries; } public void setProduct(Collection transactionEntries) { this. transactionEntries = transactionEntries; } public void ejbLoad() { // 1: SQL Select the Account bean. // // 2: JNDI lookup of ProductHome // // 3: Call ProductHome.findByAccount, passing // this account s identifier (i.e., PK) //

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obtained when simulated returns are used Over the 1963 1998 period, the estimated beta using simulated returns is 012 These results suggest that merger arbitrage returns have a very low correlation with market returns However, a closer examination of the data suggests that this might not be the case The data show a high correlation between merger arbitrage returns and market returns in months when market returns are signi cantly negative For example, when the market crashed in October 1987, generating a one-month return of 22%, simulated returns were 82% Similarly, the nancial crisis in August 1998, triggered by the Russian government s default and the fall of the large hedge fund Long-Term Capital Management, generated a one-month market return of 15%, a simulated merger arbitrage return of 45%, and an HFR merger arbitrage return of 57%.

Figure 3.16 Schematic of a light-scattering photometer with a laser source. From Rabek, Experimental Methods in Polymer Chemistry, Wiley: Chichester, UK, 1980, p 199.

The 2001 instrument s dependent questions about prior wave jobs and businesses were an exception to this rule, and in fact closely mirrored the procedures implemented in the 2004 questionnaire for other subject matter areas.

Again, in September 2001, both the market return and merger arbitrage returns were signi cantly negative These results suggest that, rather than a linear model such as the CAPM, a nonlinear model that captures the high correlation between merger arbitrage and the market in down markets, and the lack of correlation in at and appreciating markets, might be more appropriate Exhibit 76 illustrates an alternative model that allows beta to take different values in up and down markets As in the linear model described above, coef cients in this model can be estimated using regression analysis In this case, three coef cients need to be estimated the down-market beta, the up-market beta, and the intercept In addition, one must choose a threshold that de nes the boundary between down markets and at or appreciating markets Exhibit 7.

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7 presents results from estimating the nonlinear model of merger arbitrage returns using both HFR returns and simulated returns While the estimated up-market beta is close to zero in all cases, the downmarket beta is signi cantly greater than zero For example, using HFR returns over 1990 2001, the down-market beta estimate is 044, more.

5 green onions, chopped into 1 (2.5 cm) pieces 1 tsp (5 ml) ginger, grated 3 tbsp (45 ml) low-sodium soy sauce

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