Sudipto Bhattacharya's paper, “An Exploration of Nondissipative Dividend–Signaling Structures,” is in two parts. The first develops a nondissipative “quota–based” signaling model. Optimal contingent (wage) contracts are shown to induce workers, whose productivity is not directly observable, to “self–select” by the choice of level of committed productivity. However, the most novel feature of the paper is the attempt to apply the quota–based signaling model to the problem of dividend signaling in the capital– –market that is, the modeling of the “information content” of corporate dividends as an ex–ante signal of future earnings. As Bhattacharya notes, the “information content” of hypothesis of dividend payment is somewhat ill–defined to date, so such an application of the quota–based signaling model, if successful, would be a definite contribution to dividend theory.

Economists generally agree that a basic characteristic of a good tax is economic neutrality. That is, a tax should not influence economic behavior unless it was intentionally designed to produce a specific effect. In this context, the economic effects of the current system of capital gains taxation in the United States have been the subject of considerable concern. Most researchers have concluded that the current system of capital gains taxation has an undesirable and destabilizing effect on the securities markets because the practices of taxing capital gains only when they are realized and, correspondingly, allowing tax deductions for capital losses only upon realization, presumably cause investors to defer the realization of capital gains and to accelerate the realization of capital losses. Based upon this behavioral assumption, many economists infer an effect on the securities markets.

Most research in modern portfolio theory and capital market theory is based on investor selection of portfolios that are efficient in the sense that they are not dominated by other portfolios in terms of their risk-expected return characteristics. The most widely used measure of portfolio risk is the variance about the mean of the exante distribution of portfolio returns. The theoretical framework from which this measure of risk is usually derived was initially suggested by Markowitz [12], and is by now well known. Although variance has the attention of most researchers, another measure, semivariance, had some early support from Markowitz himself, and from Quirk and Saposnik [17], Mao [10], and others. Semivariance as a measure of risk can be derived from the same theoretical framework as is variance; it requires only a slightly different utility function. The semivariance of returns of portfolio p below some point h is defined as

where fp (R) represents the probability density function of returns for portfolio p. Semivariance portfolio theory is enjoying something of a revival in the works of Porter [15, 16], Hogan and Warren [6] and Klemkosky [8], and semivariance capital market models have been developed by Hogan and Warren [7] and Greene [5].

Recently, there has been an increased interest in the role that bankruptcy or ruin plays in the valuation process. Several authors have discussed this subject (Gordon [17], Quirk [27], and Smith [35]) and some have constructed theoretical models attempting to show how the probability or risk of ruin introduces an element of risk into valuation (for example, Bierman [5], Borch [8], Tinsely [37]). The question of corporate survival is, therefore, central to the financial considerations of the firm. None, however, has attempted empirical tests of the role of such a probability in valuation.

Since the beta systematic risk coefficient and the standard deviation are both important statistics in the received capital market theory [22] and the received option theory [1], considerabe effort has been expended on obtaining empirical estimates of these statistics [30]. The ordinary least squares (OLS) technique is typically utilized to estimate beta as the regression coefficient of a simple linear regression. However, the OLS betas for common stocks were found to be disconcertingly unstable over time [5, 6, 13, 15, 25]. But, whether the OLS beta or an adjusted beta were used, the regression statistics could still only explain less than half of the variability of most New York Stock Exchange (NYSE) stocks' returns (more specifically, R2 < .5).

The classical Capital Asset Pricing Model of Sharpe [5] and Lintner [4] has been generalized by Brennan [2, 3] to include the effect of investors' taxes. In Brennan's formulation, investors' portfolio incomes are divided into capital gains and ordinary income components whose marginal tax rates differ both from one another and across investors. In this paper, Brennan's model is further generalized to admit tax–exempt portfolio income as well. The results, which may accord with empirical data better than have those of the less general models, include a three–fund separation theorem, a three–term Capital Asset Pricing Formula which relates the excess rate of return of any risky portfolio to those of three fundamental risky portfolios whose rates of return are pairwise uncorrelated, and a representation suitable for empirical testing.

It Is commonplace within the confines of finance literature to explain variations in the firm's residual income stream via the dichotomy of business risk and financial risk. On an ex-post basis the business risk of the enterprise is a direct result of the firm's investment decision and is, thereby, embodied in its asset structure. It follows that the company's cost structure, product demand characteristics, intra-industry competitive position, and managerial talent all affect its business risk posture.

The problem of the portfolio demand for money was first rigorously studied by Tobin [22]. It has been analyzed since then, by Hicks [8] and Arrow [1], among many others. Many interesting results and implications regarding liquidity preference and risk-taking are derived in these studies. However, the effect of purchasing power risk on liquidity preference has been overlooked in these studies.

Of the many conglomerate studies to date, some have dealt with the risk-return performance of conglomerates in the context of the capital asset pricing model [2,7,10,14], others have considered the motives for the formation of conglomerates [4,5,6,13], and still others have examined the operating characteristics of conglomerates [9,12,15]. Within the last group, Weston and Mansinghka [15, p. 928] argued that the primary motivation for conglomerate formation is defensive diversification, “…defined as diversification to avoid adverse effects on profitability from developments taking place in the firm's traditional product market areas.” Another motivation is provided by Levy and Sarnat [4] and Lewellen [5] who demonstrated that the only economic gain from a purely conglomerate merger may be the increased debt capacity resulting from the combination of entities having imperfectly correlated earnings streams.

Haugen and Senbet address several interesting issues, such as: a costless solution, with the use of options, to the agency problem and the incorporation of informational asymmetry into the agency problem, as well as the use of options to convey information. I'll start my discussion on their paper by providing general comments which are followed by some clarifying comments on the authors' argument for signaling with options.

In a recent article Fabozzi and Francis [3] presented the results of empirical tests designed to determine if the regression coefficients of the single-index market model were significantly different in bull and bear markets. Using three alternative definitions of bull and bear markets, Fabozzi and Francis (FF) concluded the coefficients of the single-index market model were not significantly different in the two types of markets.

An algorithm to estimate the composition of efficient sets by the stochastic dominance (SD) rules was developed and made available to the public by Porter, Wart, and Ferguson (PWF) [1]. Up to that time the computer central processing unit (CPU) time required for the determination of the SD efficient set was prohibitive for a large number of portfolios. The Porter, Wart, and Ferguson algorithm made possible studies that examined the performance of mutual funds, common stocks, and portfolios using the SD rules. Unfortunately, the FORTRAN program developed by PWF and titled D0MIN2 still requires a large amount of CPU time when the number of portfolios exceeds 100.

I have two comments to make. Neither is really addressed to the details of the paper. Rather they are prompted by the general line of analysis of information revealed in a rational expectations equilibrium.

There appears to be some confusion in the literature on asset-pricing models about the role of default-risk in short-selling and borrowing by consumers. For example, in the Sharpe-Lintner ([16] [9]) model it is usual to assume that all consumers are able to borrow or lend without restriction, at the riskless rate of interest. This assumption has been recognized to involve an inconsistency in the theory, because with personal borrowing there is always a positive probability of default with the S-L assumptions, so that the consumer's bond (as seen by any lender) is not a perfect substitute for the safe asset.

Since the publication of the work of Modigliani and Miller (MM) in the late 1950s there has been a recurrent controversy in the finance and economics literature about the interdependence of investment and financial variables. The arguments are too well known to recount at any length here. Basically MM would argue that in perfect capital markets, investment is, and should be independent of financing (which we will identify, as they would, with financial variables like dividends and new debt). The opposing view would argue that capital markets are sufficiently imperfect that the firm must consider financing in its investment decision. At least some of the proponents of this other view would argue that the firm must raise funds and allocate these scarce funds between investment and dividends. This view, then, holds that the firm's investment, dividend, and financing decisions are interdependent and must be studied in the context of a simultaneous equation model. There have been many articles discussing the MM position and many attempts to test it empirically. The first to focus directly on the question of interest here was done by Dhrymes and Kurz [1] in 1967. We will attempt to show that, despite several later studies, Dhrymes and Kurz were correct in their assertion that the investment and financing decisions are made simultaneously and must be studied in the context of a simultaneous equation model. To set the stage for our study we shall review the Dhrymes-Kurz study and subsequent related studies and show that each contained some error that affected their results.

In a recent issue of this journal Barnea [1] presented an empirical study of the impact of a specialist (market-maker) on the variability of the price of a stock. He concludes with others that “the chief cost of dealing with a market maker is the difference between the theoretical but unobservable equilibrium price and the transaction price, rather than the bid-ask spread.” This paper presents a rigorous dynamic model in which the specialist, who is uncertain about the future arrival of tenders, determines transaction prices periodically over the trading day. The structure of the model permits a direct comparison of the specialist's prices to the “equilibrium price,” to be defined below, and also to what prices would be in the absence of a specialist.

I'd like to begin by thanking the Western Finance Association for the lunch I just consumed …

It is only fair that I inform you at the outset that the views you are about to hear can only be described as biased. They are biased because I'll be limiting my remarks to those parts of finance that I think I know something about; secondly, my comments will contain a disproportionate reflection of my own work. The more generous among you might argue that this puts me in good company. A better explanation would recognize that I am really in a monopoly position for the next half hour or so: there are no contemporaneous sessions within commuting distance, your lunch was paid in advance and is not refundable, and for some of you at least there is a certain cost associated with getting up and leaving in full view of the organizers.

It has long been recognized in the literature of finance that the robustness and analytical potential of mathematical programming procedures can be utilized to structure highly complex decision environments and to ascertain quickly and efficiently the dominant set(s) of actions for achieving an explicit objective(s). Although some formulations involve nonlinear relationships (for instance [13] [15]), the vast majority of the models appearing in the finance literature are variants of linear programming, including such identifiable methodologies as linear programming, goal programming, networks, integer programming, mixed integer programming, and chance-constrained programming. The decision processes for capital budgeting ([25] [1] [2] [4] [14] [16] [24]), working capital management ([20] [18] [21] [6]), cash management ([17] [23]), and portfolio selection ([22] [24]), have been structured as linear programs and have contributed significantly to understanding the dynamics of financial systems. Given the potential of these mathematical approaches, the limited industrial use of financial optimization models is disturbing.

There is strong theoretical support for the notion that prices in a perfect capital market will vary randomly ([22], [16]). However, the existence of some nonrandomness in stock prices is well documented, see ([10], [11], [13], [14], [20]). Of special importance for this study is the research by Young [24] who finds predominantly negative autocorrelation for a sample of securities using a monthly differencing interval. Autocorrelation coefficients are often used as a measure of nonrandom price behavior; negative autocorrelation is an indication of price reversals.

Information may be the key to differential rewards from investment in capital markets. Corporate insiders are thought to have information not available to others which provides them an advantage in investment activities. This has piqued the interest of regulatory officials and investors. For example, regulators in both Canada and the United States have required insiders to report their trading activities and have placed restrictions on the nature of these activities. Additionally, investors have attempted to use information on insider trading activities as a foundation for their own investment strategies.