The value of information to the investor is best described by Samuelson [15] in his prologue to the theory of speculation: “…Suppose my reactions are not better than those of other speculators, but rather one second quicker… in a world of uncertainty, I note the consequences of each changing event one second faster than anyone else. I make my fortune not once, but every day that important events happen…” Furthermore, the role of heterogeneous expectations was emphasized by Hirshleifer [7]: “…Speculation…emerges not from differences in individual risk aversion, but rather solely from differences in individual belief as to what the future will reveal.” Thus, information which is always partial and different to different investors, in imperfect markets, is perfectly consistent with the existence of heterogeneity in investors' expectations.

The mean-variance capital asset pricing model (CAPM) of Sharpe and Lintner was extended by Brennan [3] to incorporate divergent borrowing and lending rates. He found that in equilibrium the security market line (SML) has the same structure as the SML under the single-rate CAPM of Sharpe and Lintner. That is, the expected return of a security or a portfolio remains linear in its systematic risk, with the intercept replaced by an equivalent risk-free return, which is an average of the divergent borrowing and lending rates weighted by the investors' taste parameters. The equivalent risk-free return is larger than the riskless lending rate and, hence, does not represent an inconsistency with the empirical findings by Friend and Blume [4] and by Black, Jensen and Scholes [1[ that the intercept of empirical SML estimated for the single-rate CAPM is larger than the riskless rate. Moreover, Brennan attempted to show that his construct can be extended to the extreme case where there are no riskless opportunities. The case of no riskless opportunities was of course investigated by Black [2], who generalized the CAPM and SML by inventing the concept of zero-beta port-folio to account for the same empirical problem encountered in the traditional SML tests of CAPM. Since the Sharpe-Lintner single-riskless-rate CAPM implies a perfect loan market, we may view the attempts by Black and Brennan as generalizing the CAPM by incorporating financial restrictions and loan market imperfections. Their primary motive, however, is empirical, i.e., to reconcile the results from the traditional SML tests with their generalized CAPM.

Wall Street abounds with folklore concerning aggregate stock price movements before and after Presidential elections. Some suggest that movements in the Dow Jones Industrial Average (DJIA) portend the outcome of elections. Others attribute movements in the market, prior to and after the election, to the anticipated outcome of the election. In this paper we examine some of the folklore with respect to the effect of Presidential elections on aggregate stock price movements and the implication of these movements on market efficiency.

The Capital Asset Pricing Model (CAPM), an equilibrium model for the price determination of risky assets, was developed by Sharpe [16], Lintner [9, 10] and Treynor [21], following the pioneering work of Markowitz [12, 13] and Tobin [20]. In spite of the tremendous impact of this model on the profession, the CAPM still raises many questions, and is inconsistent with a considerable body of empirical evidence.

The purpose of this article is to extend both theoretical and empirical knowledge on: 1) the relation between accounting-based and market-based measures of systematic risk; and 2) the effect of financial structure on systematic risk.

Several people have attempted to evaluate the performance of mutual funds. Treynor [17] and Sharpe [15] have developed performance measures which make it possible to establish relative rankings for such funds. Treynor and Mazuy [18] have devised a statistical test for determining whether mutual funds successfully anticipate major fluctuations in the stock market. Jensen [7] has provided an absolute measure of performance which can be used to determine whether mutual funds earn higher or lower returns than those expected for the level of risk associated with their portfolios. McDonald [11] has employed the measures of performance developed by Sharpe, Treynor, and Jensen to evaluate the objectives, risk, and return of mutual funds in the period 1960–1969. Although these studies have examined mutual fund performance, none has employed an analytical framework for dealing explicitly with the nonstationarity which is likely to exist in the risk-return relationships for such funds [13].

Studies which deal with portfolio efficiency analysis can be divided into two main categories: (a) those concerned with the development of normative decision rules; and (b) those that discuss the application of the normative rules to empirical data. Most of the research on portfolio efficiency analysis uses some set of empirical data, without considering the possible errors which may arise when a sample rather than the entire population is examined. The prevailing neglect of the sampling errors is a clear reflection of the complexity of the issue.

In a world characterized by perfect and complete capital markets, the success (or failure) of a merger is judged by the merger's impact on stockholder wealth. With completeness, the merger's impact on the probability distribution generating stockholder returns is unimportant. The perfect market assumption guarantees that the stockholder not satisfied with the consolidated firm's return distribution can frictionlessly sell his shares and reorder his portfolio; hence his only concern is the merger's impact on wealth. However, if we acknowledge the existence of commissions, taxes, and other frictions, or if markets are not complete, the merger's impact on the stockholder return distribution becomes relevant. In this study we will analyze 149 mergers involving large N.Y.S.E. firms. We will examine four different hypotheses related to the impact of merger on attributes of the stockholder return distribution. We focus our analysis on risk-related attributes including beta, total variance, residual variance, and several other risk-related attributes. In a companion paper, merger's impact on wealth is calculated for the same sample but will not be reported here.

For financial management to make wealth maximizing capital budgeting decisions, a model that will determine correctly the market value of a project's levered cash flows is required. A capital budgeting model should account not only for the effects of the investment decision, but also for the effects of the financing decision and the interactions between the two decisions. In perfect capital markets all the effects of the financing decision pertain to the tax shield created by debt financing. Thus, as originally shown by Modigliani and Miller [8], the value of a project's levered cash flow stream equals the market value the stream would have if it were unlevered plus the market value of the stream of tax savings on interest payments associated with the debt employed to finance the project. While this result is completely general with respect to the specific processes utilized by the market to value the two components, MM specified the value of the unlevered component as the present value of the unlevered cash flows discounted at the appropriate risk adjusted unlevered cost of capital and they specified the value of the tax savings component as the present value of the tax shield on interest discounted at the cost of debt. Accordingly, the value of a project's levered cash flows is specified as the sum of these two present values, one representing the effects of the investment decision and the other capturing the effects of the financing decision. The MM valuation model has been extended to normative capital budgeting analysis by Myers [9] in terms of the adjusted present value (APV) model.

Several recent studies of the capital asset pricing model were designed to improve the understanding of the pricing of capital assets by expanding the singlefactor market model to include macroeconomic information, industry influences and individual firm characteristics. Stone [20] has offered another means of expanding the market model. He has proposed a two-index model consisting of the traditional “equity market” index and a “debt market” index. Stone justified the model by arguing that individual equity securities exhibit varying degrees of sensitivity to interest rates and that the opportunity to invest in risky debt securities may represent an attractive alternative to riskless assets and risky equity securities. He indicated the incorporation of an index for the return on debt in the market model might improve its explanatory power for such securities as “…gold, bank, savings and loan, public utility, and similar stocks exhibiting considerable interest rate sensitivity [20, p. 710].”

Since the early 1960's the European capital market has witnessed rapid growth as a source of short- and long-term dollar denominated funds to international borrowers and as an alternative investment area to potential lenders. While considerable work has analyzed the determinants of short-term dollar denominated Eurorates (Eurodollar yields), less work has concentrated on the determinants of long-term dollar denominated ($Eurobond) yields. In addition, work on the determinants of long-term $Eurobond rates has been conducted on data derived from periods of fixed exchange rates and capital controls and may not be applicable to periods of post-capital controls or floating exchange rates. Theoretically, there should exist a strong relationship between yield levels and yield changes in domestic and foreign securities under a fixed exchange rate system. Under a floating exchange rate system, however, domestic yield levels may move independently of foreign yields with the exchange rate adjusting price differentials. In addition, in a period of capital controls, the $Eurobond market and U.S. bond market may not necessarily be regarded as competing segments of a larger market or dollar denominated financial assets. The existence of a floating rate exchange system and the removal of U.S. capital controls may have, therefore, affected the relative behavior of $Eurobond, U.S., and foreign security yields. While previous studies have analyzed $Eurobond yields under fixed exchange rates and capital controls, in this paper we also have analyzed $Eurobond yields under floating rates and post-capital controls.

Since the early work of Durand (1941), there has been considerable interest in using quantitative models of consumer credit behavior for credit-granting decisions. Most models are based on the concept of “scoring” by use of weights usually determined as statistically significant coefficients of some linear statistical model, frequently the linear discriminant model. It is the purpose of this note, however, to propose maximum likelihood estimation of the logit model as an alternative, and to compare the two models in a “scoring experiment.”