The pivotal role of earnings expectations in equity valuation and therefore in certain areas of business finance is widely recognized, yet there is little theoretical or empirical evidence as to the manner in which investors and other groups actually formulate their estimates of future earnings. The resulting necessity to utilize proxy or indirect measures of expected earnings specified largely according to the predispositions of the investigator has led to numerous difficulties in the testing of cost of capital propositions and models of equity valuation.1 This study is intended to supply a preliminary response to the question of how earnings expectations are determined by appraising the extrapolative component of a limited sample of short-term estimates of earnings per common share. More specifically, the issues are the extent to which the earnings estimates (1) are extrapolative in nature and (2) may be approximated by familiar, naive, extrapolative techniques. In this context, “extrapolative” simply means determined by application of a specified weighting scheme to prior observations in the time series.

We propose a novel model-free approach to obtain the joint risk-neutral distribution among several assets that is consistent with options on these assets and their weighted index. We implement this approach for the nine industry sectors comprising the S&P 500 index and find that their option-implied dependence is highly asymmetric and time-varying. We then study two conditional correlations: when the market moves down or up. The risk premium is strongly negative for the down correlation but positive for the up correlation. Intuitively, investors dislike the loss of diversification when markets fall, but they actually prefer high correlation when markets rally.

This study introduces a new measure of ex ante litigation risk using scrutiny of SEC filings by the source of securities litigation (plaintiffs’ lawyers) to reduce measurement error, relative to existing measures. We show that plaintiff-lawyer views proxy for the largely unobservable factors that make firms more likely to face litigation risk. Lagged views precede the public bad news revelation that triggers litigation and predicts future realized litigation risk (i.e., securities class actions filings and plaintiff-lawyer investigations) and stock market outcomes. Finally, we provide new insights into the plaintiff-lawyer case selection process that otherwise cannot be observed.

To assure that the commercial banking industry's performance serves the “convenience and needs” of the public, bank supervisory authorities have been vested with broad powers to alter the competitive environment in bank markets. While several criteria have been used to evaluate the effects of entry, merger, branching, and other changes in the allocation of bank resources, the results have been largely inconclusive. Since the regulatory authorities have pursued somewhat conflicting objectives in seeking a “failure-proof” system that is also “efficient,” there may be no single criterion for evaluation of bank behavior that is wholly consistent with the behavior predicted by the neoclassical theory of the firm.

The problem of allocating a fixed set of time-phased investment allowances among competing investment projects and proposals has received great attention in the literature since Weingartner [11] established a linear/integer programming formulation. Reviews of the literature can be found in Weingartner [12], Hunter [6], Hodges [5], and Bernhard [2].

A spot portfolio of rate-sensitive assets can be hedged by a portfolio of interest-sensitive futures contracts. The hedge ratios of minimum-variance portfolios are unique when the fixed cash flows of underlying instruments are linearly independent and when the covariance matrix of unexpected changes in spot rates over the term of the cash flows is of full rank. Hilliard's (1984) full-rank model has produced smaller portfolio variances than a duration model in a short-term hedging context. However, the methodology typically requires extensive econometric analysis. This paper develops a structured covariance matrix of full rank that requires only one parameter estimate. Hedging examples are provided.

We examine an impartant aspect of emprical estimation of term structure models; the role of conditioning information in dynamic term structure models. The use of both real world or simulated data implicitly incorporates conditioning information. We examine the bias created in estimating the drift by a specific form of conditioning, namely truncation. Using the theory of enlargement of filtrations we provide estimates of the extent of this truncation bias for commonly used short rate models. We find that this truncation bias causes the drift of these models to have a nonlinear structure.

The Sharpe-Lintner Capital Asset Pricing Model (CAPM) has always contained an implicit question: what if all investors are single-period wealth maximizers but the length of the single period varies across investors? Gressis, Philappatos, and Hayya (GPH) [7] have pointed out that as the assumption of investment horizon length is changed, the Capital Market Line (CML) intersects the Efficient Frontier (EF) at different points causing different investors to hold different efficient portfolios. GPH assert that these different portfolio holdings will result in an inefficient market portfolio—and dire consequences for the capital market model.

Recently, the appropriateness of the weighted average cost of capital for making decisions on capital structure and the selection of projects has been seriously questioned. Arditti [1] showed that when project lives were finite, the weighted average cost of capital was not appropriate for valuing the firm. Beranek [2] demonstrates that when the objective is shareholder wealth maximization, the appropriate discount rate for capital budgeting decisions for finite lived projects n > 1 is not the traditional weighted average cost of capital.

Errors in variables due to nonsynchronous trading and benchmark error are significant problems for capital market research. This paper develops the use of direct and reverse regression to bound true coefficient estimates when the data exhibit error structures arising from these two sources both separately and jointly. The approach appears to have broad applicability for capital markets research. As an example, the paper reexamines the small-firm effect to show that it cannot be attributed to nonsynchronous trading or benchmark error in the estimated variance of the market portfolio. This result is shown to hold even when the tax-selling effect is controlled for by excluding January returns.

The existence of default risk is an important characteristic of most lending operations, and many of the studies dealing with lending behavior incorporate default risk considerations. Two basic approaches to the modeling of such behavior can be identified according to their treatment of default probabilities: the first approach assumes that the likelihood of default is independent of the actions of the lender under consideration, namely, the volume of the loan granted by the current lender has no impact on the default probability (e.g., the works by Yawitz [9], Feder and Just [3], and Bierman and Hass [2]). Such an assumption may be quite appropriate in situations where the volume of operations of a single lender is rather small relative to the size of borrower's assets (or previous debt), as is the case with most bond buyers or with banks who lend to sovereign borrowers.

This paper obtains equilibrium interest rate option prices for discontinuous short-term interest rate processes. The prices are first obtained for a general distribution of jump sizes using a process with a number of fixed size jumps. The pricing formulas are then used to obtain option prices when the jump distribution is known to be one of the continuous distributions. The commonly used jump-diffusion, stochastic volatility jump-diffusion, and Gamma process option prices can be obtained as limiting cases. The methodology is also applied to obtain the prices of options on stocks. Finally, the paper shows how portfolios to hedge derivative securities can be built.

The pros and cons of bank mergers and multiple-office banking are in the forefront of bank policy consideration today [8, p. 19] Commercial banks have Joined the industrial and merchandising files., as well as the transportation companies, to sweil a rising tide of merger; The Comptroller of the Currency has reported that nearly 2,000 banks with resources of over $40 billion, were acquired by other banks between 1950 and 1962, inclusive, [47, p. ll].

We examine whether underwriters price up weakly demanded initial public offerings (IPOs) to prevent withdrawal. Our empirical strategy exploits a discontinuity in the distribution of IPO prices around the low boundary of the filing range. Offerings with a high ex ante withdrawal probability that are priced at this boundary are likely priced up to meet issuers’ reservation prices. We compare the aftermarket returns of these IPOs to the returns of other weakly demanded offerings where issuers’ reservation prices were likely not binding, and we identify a negative 8.4-percentage-point differential attributable to the aggressive pricing inherent in setting the price at the low boundary when withdrawal risk is high.

This paper investigates hedge funds’ ability to time industry-specific returns and shows that funds’ timing ability in the manufacturing industry improves their future performance, probability of survival, and ability to attract more capital. The results indicate that the best industry-timing hedge funds in the manufacturing sector have the highest return exposure to earnings surprises. This, together with persistently sticky earnings surprises, transparent information environment in regards to earnings releases, and large post-earnings-announcement drift in the manufacturing industry, explain to a great extent why best-timing hedge funds can generate significantly larger future returns compared to worst-timing hedge funds.

This paper examines trading patterns and market integration using laboratory asset markets. Our markets are designed to approximately correspond to the trading day for stocks cross-listed in markets in Europe and North America. Some of our markets feature timing restrictions so that participants cannot trade across markets except during a fully integrated overlap period. Comparison of markets with and without timing restrictions shows that restrictions reduce trading activity and shift transactions to the overlap period. When asset values are extreme, price discovery can be impeded when trading restrictions exist. The measurement of liquidity suggests that trading restrictions increase overall spreads.

The advance of the theory of contingent claim pricing has made it possible to model and analyze very complex financial claims. When the value of the firm can be represented as a contingent claim, then the firm's optimal financial policy can be determined with only a slight modification in standard solution techniques for contingent claims. In this paper, stochastic control theory is used to determine a dynamic investment policy for the valuemaximizing firm. The value of future, stochastic economic rents (i.e., the net present value of the firm), and the firm's optimal investment policy must reflect a rational reaction on behalf of its competitors. A computationally efficient methodology is presented for solving the simultaneous investment-valuation problem for an n-firm game.

We have formulated the mean-variance models of portfolio selection with stochastic cash demand. The results of the general model have indicated that the characteristic of the investor's stochastic cash demand, the liquidity risks of assets (measured by the covariance between an asset's return and the cash demand), and the structure of transfer costs also play important roles in the determination of the investor's optimal portfolio. We have also shown that the model of portfolio selection with stochastic cash demand can be greatly simplified if the assumption of symmetric transfer costs is invoked. Furthermore, it has been shown that the simplified model can be reformulated and solved by the LP techniques. Thus, LP formulation of portfolio selection with stochastic cash demand should have practical usefulness.

Finally, along the line of works by Chen, Jen and Zionts [3, 4], Pogue [14, 15] and Stone and Reback [20], one can extend the analysis in this paper to the problem of dynamic portfolio management with stochastic cash demand and transfer costs.

Cash concentration is the task of moving funds from depository banks into the central cash pool. It is useful to structure cash concentration by dividing it into two major problems––management and design. Management is the day-to-day operation of the cash concentration system once it is designed. Design is specifying the structure of the cash concentration system.

This paper develops a valuation formula for multi-period stochastic cash flows consistent with rational risk-averse investor behavior and equilibrium in securities markets. It shows that the CAPM does not have to be sequentially applied in discounting of the cash flows of multi-period projects, and a single beta can be used to measure the riskiness of an uncertain income stream. Hence, a multi-period project is priced as if it offers a single payment. The formula uses a set of assumptions that is slightly more restrictive than the minimum set Constantinides (1980) uses to produce the multi-period version of the CAPM. This paper also demonstrates that, under certain assumptions, covariances of stochastic cash flows with changes in the term structure may be sufficient measures of the cash flows' riskiness.