The trading of security options is one of the fastest growing and most dynamic areas of investment concern. When the Chicago Board of Trade's proposal to develop an exchange for trading option contracts is implemented, security option trading will become an even more important aspect of the investment world.

Intelligent corporate financial planning has been necessary for as long as the corporate form of business enterprise has existed. Only in recent years, however, have computer technology and academic theorizing been harnessed to meet this practical need. Without wishing to minimize the impact and value of these efforts on the practice of corporate finance, we do think there are grounds for believing that the new finance “tools” have been less than maximally effective. In this article we contrast typical financial modeling theory in order to interpret the gap between the two. Then we describe a financial policy model whose characteristics might be expected to be more acceptable in practice. Finally, we discuss the implications of the theory/practice gap and our experience with this model for future scholarly activities in the modeling of financial policies.

The field of investment analysis provides an example of a situation in which individuals or corporations make inferences and decisions in the face of uncertainty about future events. The uncertainty concerns future security prices and related variables, and it is necessary to take account of this uncertainty when modeling inferential or decision-making problems relating to investment analysis. Since probability can be thought of as the mathematical language of uncertainty, formal models for decision making under uncertainty require probabilistic inputs. In financial decision making, this is illustrated by the models that have been developed for the portfolio selection problem; such models generally require the assessment of probability distributions (or at least some summary measures of probability distributions) for future prices or returns of the securities that are being considered for inclusion in the portfolio (e.g., see Markowitz [11] and Sharpe [19]).

The current merger movement has been characterized by the willingness of the management of some acquiring companies to pay substantial merger premiums. A merger premium exists when the common stockholders of an acquired company receive cash and/or securities possessing a value greater than the company's premerger market value. The rationalization or justification of these “premiums” is based on a merger synergy concept. Contemporary merger literature recognizes two broad forms of merger synergy — the potential for greater operating efficiencies [14] and/or potential financial benefits — with the latter containing instantaneous [12] and real elements [1, 7, 9, 10, 11, 13].

Considerable literature in the investment, growth, and financing of the corporation has developed in recent years. While theoretical studies in this area have contributed importantly to the understanding of the firm's time-optimal decision program, they have generally been limited in scope to the all-internally-funded firm and steady-state dynamics. The well-known analyses of Gordon ]7[ and Lintner ]13[ are typical of this restricted focus. Herein we relax these specializing conditions, both by permitting external equity as a financing alternative and by not a priori requiring the firm to make identical (earnings proportional) investment and financing decisions at every time instant such that it progresses only along a constant, exponentially growing earnings path.

This paper has presented a model for solving a central problem of short-term financial management — cash planning and credit-line determination. The core of the model is an algorithmic procedure for finding the best cash plan and the associated credit line for a given operating plan and long-term financial plan. Since the model requires a computation of cash balances, it must be embedded in a financial statement simulator.

The two keys to the model are:

1. the use of priority rankings in specifying the order in which assets and liabilities are used to change cash balances;

2. the separation of solution constraints into two classes—consistency conditions given by C1, C2, and C3 and feasibility conditions stated in C4, C5, and C6. The latter conditions require changes in the long-term plan (or the operating plan) to obtain feasibility of the short-term plan.

The use of priority rankings and the separation of solution constraints into these two classes makes possible the formulation of an algorithmic procedure that is computationally efficient and that avoids having to solve amathematical programming problem.

The benefits of the model are: (1) saved time; (2) increased accuracy in cash planning; (3) quick determination of infeasibility with respect to the short-term plan. For a firm already using financial statement simulation, the model is sufficiently easy to program and implement so that saved user time and system expense alone easily justify the cost of developing the system. Finally, a system that automatically handles short-term cash planning is critical for other areas of short-term planning, for meaningful sensitivity analysis, and for long-term financial planning for firms (such as General Recreation) for which a substantial part of the total financing is provided by either credit-line borrowing or commercial paper issuance.

Because of the similarity of both banking and financial practice across firms and banks, the basic approach used in this model is applicable to most nonfinancial corporations.

There exists a relatively large body of evidence that is consistent with the proposition that the market for securities (in particular, the New York Stock Exchange) is an efficient market in the sense that market prices react instantaneously and unbiasedly to new information and, therefore, market prices fully reflect all publicly available information. To what extent do accounting numbers reflect the kinds of information reflected in market prices? One might not, of course, expect accounting numbers to reflect all events reflected in current market prices. For example, if an economically significant piece of legislation is under discussion in, say, the United States Senate, then the expected effects (if any) of this legislation may be impounded in current market prices. One should not, however, expect these effects (if any) to be reflected in currently issued accounting numbers because of the nature of accepted accounting procedures. Yet, in general, over a period of time, there may be a systematic correspondence between some types of events reflected in market prices and accounting numbers. That is, over time, there may be a correlation between the information impounded in market prices and that impounded in accounting numbers.

The purpose of this article is to explore the relevance of the theory of Markov processes to the analysis of stock price movements.

The present study was prompted by the work of Dryden [6], in which aggregate data on United Kingdom share prices were analyzed within a Markovian framework, and which indicated that it might be fruitful to apply the Markov model to more disaggregated data, specifically to individual stock price data.

The FTC reported 22,517 corporate acquisitions during the 1960s compared with 7200 for the period, 1940–1959. The increased employment of this method of corporate growth has generated a number of studies explaining certain segments of the merger movement. Attempts have been made to explain why firms merge, how firms merge, and how mergers have affected subsequent performance of firms. Mergers have been described as consummated to avoid bankruptcy (for the acquired firm), to capitalize upon managerial inefficiencies, to gain from valuation discrepancies, to achieve portfolio diversification, and for synergistic purposes and many other reasons.

Despite apparent implications of normative portfolio theory for portfolios that incorporate a wide variety of marketable security forms, most of the literature concerned with application or empirical testing of the theory has considered portfolios composed only of common stocks, cash, and the proverbial riskless bond. However, nonequity securities constitute a significant component of investors' total financial wealth, and broadly diversified securities portfolios are commonplace. The objectives of this paper are to examine the risk characteristics of 19 classes of long-term marketable securities, ranging from U.S. government bonds to speculative common stocks, and to explore some implications of these characteristics for diversification of actual securities portfolios. The first section presents some risk measures for these security classes which are derived from ex post holding period return data for the 18 years, 1951–1968. This section includes an appraisal of the efficacy of alternative approaches to the generation of the matrix of interrelationships among the returns of broad types of securities. The second section utilizes the ex post risk measures to explore the composition of minimum risk portfolios consisting of two types of marketable securities, and the final section considers the question of appropriate media for the efficient diversification of common stock portfolios.

An important issue in the financial literature concerns the conflict between the stochastic dominance (SD) and the mean-variance (EV) methods of choosing optimal portfolios of risky assets. Much of the recent theoretical and empirical work in portfolio analysis has been devoted to the extension and testing of the Markowitz two-moment model, in which it is assumed that either (a) decision makers have quadratic utility functions with negative second derivatives or (b) the probability functions are from some appropriate two-parameter family and the investor is risk averse.

The earliest successes in developing asset management theory focusing predominantly on short-term optimization of physical stock flow systems are due to Masse [13]; Arrow, Harris, and Marschak [1]; and Whitin [22]. This led to the development of burgeoning literature on what has come to be known as “inventory theory” followed by its application to cash management problems by Baumol [2]. In contrast to the conventional static analysis of Tobin [19] and Markowitz [12], Baumol's model incorporates what Hicks [11] referred to as “frictions” or the adjustment costs. More recently the pioneering works by Miller and Orr [14] Eppen and Fama [3], Weitzman [20], and Sethi [4] have sought to extend this basic model by incorporating different cash flow and operating cost assumptions.

The recent wave of revaluations has again brought to the fore the foreign exchange risks with which most multinational corporations have to live constantly. While these latest parity changes have tended to increase the value of foreign currencies relative to the dollar, most exchange risks arise from pending devaluations.

Some recent research into the short-term behavior of speculative prices has combined the methodology of information theory with the analysis of aggregate market data on the daily proportions of securities advancing, declining, and remaining unchanged in price. For example, Theil and Leenders [8] employed the measure of average information inaccuracy to analyze aggregate data on the Amsterdam Stock Exchange and concluded that there was a strong dependence between the proportions of prices advancing, declining, and remaining unchanged today, and the respective changes tomorrow. The evidence of dependence on the Amsterdam Exchange prompted Fama [4] to apply similar methodology on the NYSE data, for the period 6/2/52 to 10/29/62, in search of similar patterns. Professor Fama, after proper adjustment for nonstationarity in the time series, concluded that the evidence of dependence on the NYSE was meager and unreliable for forecasting short-term security prices. However, Dryden [3], utilizing the same methodology on data from the London Stock Exchange, found significant dependence between the proportions advancing, declining, and remaining unchanged today, and the respective changes tomorrow. Dryden's results yielded a higher dependence for the London Exchange than for the Amsterdam and New York Exchanges, and were later verified by the straightforward application of a Markovian approach.

Professors Nielsen and Melicher (N-M) have conducted well an interesting study of merger premiums as related to various measures of synergy connected with those mergers. Their study is another in a growing body of literature concerned with the merger phenomenon which increased substantially during the sixties and has continued into this decade. In order to provide an evaluation of their study, I shall consider their choice of research design and their analysis of research findings.

One of the major problems in finance is that of combining the separate costs of debt and equity into an appropriate cutoff rate for new investment; this problem is particularly acute when the firm is changing its capital structure. Solutions to this problem which have been proposed include various types of both marginal costing and average costing.

Professor Stevens has attempted to determine whether or not a consistent financial basis for merger exists as measured by premerger financial characteristics of the acquired firms. He suggests that results of his study are useful in identifying merger motives and in relating such motives to a general framework for analysis of merger movements.

The attempt to incorporate securities market imperfections other than proportional taxes within a mean-variance security valuation context has met with modest success. Lintner [5], however, has recently considered imperfections by the device of segmented markets. His paper has motivated the following taxonomy. Securities markets are defined as weakly segmented if some of the securities in at least one market are available to some investors but not to others, partially segmented if the sets containing both investors and available securities in each market are disjoint, and completely segmented if additionally the sets of firms in each market are disjoint. Segmented markets effectively relax the separation property of mean-variance equilibrium models (i.e., all investors, irrespective of differences in present wealth or preferences, divide their wealth between the same two mutual funds; one is risk-free and the other is the market portfolio of risky securities). This property unfortunately implies that each investor must hold a portion of every available risky security. This is empirically unrealistic, primarily due to restrictions on borrowing and shorting and scale economies in security analysis and brokerage. Moreover, even in the absence of these complications, ownership of nonmarketable assets, nonhomogeneous beliefs, or breakdown of the separation property due to tastes or nonnormality will motivate individuals to hold different risky portfolios. The device of segmented markets embodies in extreme form these obstacles to diversification and portfolio similarity.

The current assets and current liabilities of a firm are the stock reflections of closely interrelated operational and financial cash flows. The net effect of these combined flows must be recognized in searching for the optimal credit, inventory, or short-term borrowing policies. Yet, the vast majority of models for short-term investment and borrowing decisions do not allow for the interrelationships of this system.

The use of residuals, both mean and mean absolute, was found to be useful in capturing the impact of new quarterly earnings information on share prices. Our results seem to indicate that the market evaluates third quarter and annual earnings reports differentially from the first and second reports. This can perhaps be explained in part because the annual report, which is audited, contains year-end accounting adjustments. Furthermore, the third quarter report may be viewed as a harbinger of the annual report. Our results also indicate that the share prices of high growth companies adjust to earnings information differently than do the shares of medium and low growth firms. In total our results seem to be consistent with the “loose” form of the efficient markets hypothesis. This is because of the difficulty in predicting the signs of the mean residuals. Or alternatively, while we could observe significant changes in the absolute means, it was considerably more difficult to predict the direction of these changes as seen in the mean residuals. While the difficulty of predicting the direction of the residuals and the loss of statistical significance between trading days when days −1 and 0 in the group tests over all reports indicate support of market efficiency, it should be reemphasized that the market appears to take very long to react to the annual report — that is, the reaction seems to start even before the third quarter report.