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The present article shows how Bayesians should shift beliefs among a family of models concerning the probability distribution of daily changes in the Standard & Poor 500 Index, given a particular sample. The preceding article in this issue showed that classical (R.A. Fisher, Neyman-Pearson) inference can be highly misleading for Bayesians, as can the assumption of a diffuse prior. The present article discusses how to bound Bayesian shifts in belief for compound hypotheses generally, as well as the specific shifts in beliefs among simple and compound hypotheses implied by the particular sample.

This is the first of two articles which apply certain principles of inference to a practical, financial question. The present article argues and cites arguments which contend that decision making should be Bayesian, that classical (R. A. Fisher, Neyman-Pearson) inference can be highly misleading for Bayesians as can the use of diffuse priors, and that Bayesian statisticians should show remote clients with a variety of priors how a sample implies shifts in their beliefs. We also consider practical implications of the fact that human decision makers and their statisticians cannot fully emulate Savage's rational decision maker.A companion article (Markowitz and Usmen, 1996) which follows in this issue describes how remote Bayesian clients should shift beliefs among various hypotheses concerning the probability distribution of daily changes in the Standard and Poor (S&P) 500 Index of stock prices, given a particular sample. The original motivation for the study was methodological. We wanted to see if useful data analysis could be performed on practical financial problems within constraints imposed by certain philosophical principles, namely, that financial research is primarily to improve financial decisions; rational decision making is Bayesian; the commonly used, commonly published classical (R.A. Fisher, Neyman-Pearson) inference methods are highly unreliable guides to Bayesians; the conjugate or diffuse priors frequently assumed in Bayesian studies are either restrictive (few hypotheses admit nontrivial conjugates) or are possibly highly misleading; and the Bayesian statistician should show remote clients, with a wide variety of priors, the extent to which they should shift beliefs, from prior to posterior, given the sample at hand (as opposed to assuming a single set of priors and showing the posterior beliefs implied by the sample mad these priors).

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The arguments for and against transfer pricing schemes so far have focused on profitseeking approaches based on tax differentials, or on evasion of government enforced goods and fund flow restrictions. This article shifts to a value-seeking framework where transfer prices act as strategic tools that may enhance value for the multinational with a foreign affiliate by exploiting financial and/or tax arbitrage that also lead to ownership arbitrage. The results show that there is an optimal level of transfer price depending on the specific exchange rate distribution when the cost structure allows for a penalty for overcharging. Moreover, this article introduces a new form of tax arbitrage benefit of transfer prices that is based on present value of tax shields.