Knowledge that Transforms

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.

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 a mathematical 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.

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 problem of monitoring the ongoing receivables collection experience of an enterprise which sells on credit is, in essence, the problem of identification. The concern is an accurate appraisal of customer account payment patterns — in particular, a determination of whether and to what extent those patterns vary over time. Successful execution by the credit manager of his responsibilities for policy formulation, collection enforcement, and forecasting necessarily depends heavily on the availability to him of a reliable reporting mechanism.

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.

Almost two decades ago, Markowitz [12] formulated the portfolio selection problem as a parametric quadratic programming problem. The crux of his formulation was the mean-variance assumption which asserted that a portfolio is efficient if (and only if): (1) it has less variance than any other feasible portfolio with the same return and (2) it has more return than any other feasible portfolio with the same variance.

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.

Ex ante predictions of the riskiness of returns on common stocks — or, in more general terms, predictions of the probability distribution of returns — can be based on fundamental (accounting) data for the firm and also on the previous history of stock prices. In this article, we attempt to combine both sources of information to provide efficient predictions of the probability distribution of returns. We predict two parameters of the distribution of returns for each security in each year: the response to the overall market return (β), and the variance of the part of risk, specific to the security, that is uncorrelated with the market return. A cross section of time series data on returns and accounting variables, taken primarily from the Compustat tape, is used. Several recent developments in statistical methodology are applied.

Recent research focused on the market for first public offerings of common stock has indicated that investors who purchase new issues at the offering price will quickly achieve relatively large systematic profits. This is attributable to either the inability or the reluctance of investment bankers to reoffer the shares in which they deal at market-clearing prices. This paper examines factors that influence investment bankers in their pricing decisions and subsequently determine the short-run performance of new issues.

Under the assumption that individuals are single-period maximizers of the expected utility of their future wealth, this essay extends the mean-variance security valuation model developed by Sharpe [10], Lintner [4, 5, and 6], and Mossin [7 and 8] to a general parameter-preference model, with and without the simplifications of homogeneous subjective probabilities and the existence of a risk-free security. Results with quadratic and cubic utility are developed as special cases.