Knowledge that Transforms

Donald C. Aucamp, Walter L. Eckardt, Jr., A Sufficient Condition for a Unique Nonnegative Internal Rate of Return-- Comment, The Journal of Financial and Quantitative Analysis, Vol. 11, No. 2 (Jun., 1976), pp. 329-332

In this paper some effects of nonstationary parameters upon inferences and decisions in portfolio analysis are investigated. A Bayesian inferential model with nonstationary parameters is presented and is applied to the problem of portfolio choice. For this model, nonstationarity 1) implies greater uncertainty about future returns; 2) implies that in forecasting future returns, recent returns should receive more weight than not-so-recent returns; 3) restricts the amount of information that can be obtained about future values of the parameters of interest; 4) shifts investment among risky securities and from risky securities to risk-free securities; and 5) yields optimal portfolios with smaller expected returns than corresponding optimal portfolios in the stationary case.

Whenever the firm must borrow funds, it must also decide maturity of the new debt. Yet, the decision models which have dealt with the debt maturity decision have done so almost incidentally, as an extension of the decision to exercise the call provision on outstanding bonds ([6], [10], [23]). There has been little direct examination of the corporate debt maturity decision. In an attempt to fill this gap, this paper is an exploration of the debt maturity decision for a firm which is concerned with minimizing the present value of the expected costs of borrowing. This paper develops a discrete dynamic programming model of the debt maturity decision, in a world where interest rates follow a finite Markov process, and where the yield curve is formed from expectations regarding the future course of interest rates. With this optimization model, the influence on the debt maturity strategy of variables such as flotation costs and liquidity premiums will be explored. There will be no consideration of the risks associated with alternative borrowing strategies.

This study provides, as a result of comprehensive search, a better description of the intertemporal behaviors of corporate debt policy, comparable to those that exist for dividend policy. Although leverage policy may vary a great deal from firm to firm, we found that: (1) The rather simple partial adjustment model with constant payout ratio to have the best predictive performance and other superior models include the first-order markov process and the historical average leverage ratio; (2) in general, firms seem to operate with a concept of “target leverage ratio, ” e.g., target ratio computed from the partial adjustment models, or from historical or industry averages; (3) there is some weak evidence of the presence of unused debt capacity for the total sample; (4) the average speed of adjustment to close the gap between the desired and actual leverage ratio is a respectable 67 percent in the first year (due to the lumpiness of debt issue, individual firms tend to be either under or overadjusted); (5) there are some indications that firms also adjust debt behavior to anticipated future increases or decreases in assets.There are several areas for future research, for instance, the best debt model could serve as the first stage of a possible two-stage equation in the empirical verification of the MSM's assumption of the independence of the investment decision to the financing decision (e.g., [7]), on a further exploration of how firms' expectations affect debt behavior. Finally, the existence of a rational target leverage ratio should encourage research interest concerning the existence of an empirically testable optimal leverage ratio.

The purpose of this study was to test empirically the risk and return relationships for a mean-variance (E-V) and a mean-semivariance (E-S) capital asset pricing model (CAPM). To date, virtually all empirical work has focused on the Sharpe-Lintner [28, 17] E-V model. In the E-V model, the risk of an efficient portfolio is measured by the standard deviation of return, σp. For individual securities, the appropriate measure of risk is the covariance of return on the security and the market portfolio. The E-V model states that the expected return of any security or portfolio equals the risk-free rate of return plus a risk premium that is η times the difference between the expected return on the market portfolio and the risk-free rate of return, i.e., where the tildes denote random variables, and = expected rate of return on security i, Rf = risk-free rate of return, = expected rate of return on the market portfolio, and.

Multiasset portfolio selection models stated in terms of the expected utility criterion generally require the evaluation of multiple integrals. This reality has severely hindered attempts towards the development of computation methods to determine optimal portfolio allocations when there are a large number of assets. Aside from special cases, expected utility is not convergent into a simple closed form; the complexity from the point of view of computation is then perhaps most easily appreciated if one realizes that every iteration in a nonlinear program demands the estimation of several integrals (see Ziemba [23] for details). Such calculations are extremely costly when the number of assets is large. It is, consequently, of interest to approximate the expected utility function by a function which is easier to optimize over the set of feasible portfolios.

1. This paper presents and tests a model of dealer inventory response. The estimated inventory responsiveness coefficient is statistically significant and its magnitude is consistent with reasonable values of underlying variables which, it is hypothesized, determine the coefficient.2. The sign of the inventory responsiveness coefficient indicates that dealers tend to be passive and acquire shares when prices fall and sell shares when prices rise. This type of behavior is sometimes termed “stabilizing.”3. Dealer inventories tend to increase on days prior to price declines and tend to decrease on days prior to price increases; that is, inventory changes tend to be “destabilizing” with respect to future price changes. This implies that a fraction of the public trades on superior information and that dealers tend to lose money to such information traders.4. There is a strong tendency for dealer inventory levels to return to normal, presumably zero. The implied typical inventory holding period is about 8 to 10 trading days.5. Comparison of NASDAQ dealers and NYSE specialists shows that the pattern of inventory responsiveness is very much the same for the two. This suggests that both act in accordance with the underlying economic model and that differential regulation has little effect on typical inventory responsiveness.6. This finding does not obviate the possibility that individual dealers or specialists behave in atypical or undersirable ways, and that the extent of such atypical behavior might depend on the degree of public regulation of dealer activities. An exhaustive comparative study of deviations from normal behavior was not possible. However, it was possible to compare the frequency of nonstabilizing transactions in which price change and inventory change on a given day are in the same, rather than opposite, direction. One could not conclude that NASDAQ dealers had more nonstabilizing activity than NYSE specialists.

The Securities and Exchange Commission (SEC) and the New York Stock Exchange are concerned with the full disclosure of information insiders normally would be expected to possess about their company, including any facts that would materially affect the market's valuation of the firm's worth if they were publicly known. At present, the regulatory agencies have limited their activities to the collection and dissemination of historical information and facts. The motives of insiders, based in large part, presumably, on their knowledge regarding future operating results are hidden from the public eye. The SEC in compiling the Official Summary of Stock Transactions does not require insider to reveal his motivation for trading.

In a general sense this analysis has been concerned with the extent of a market and the effect of limiting the extent on the prices of assets in that market. One example of the type of limited market extent with which we have been concerned is provided by nonmarketable assets and another is provided by market segmentation. Unambiguous statements of the effect of nonmarketable assets and market segmentation on the level of prices require that and be oppossite in sign (unless one or both are zero). Only two cases have been identified where unambiguous statements can be made. The first, the case of constant absolute risk aversion, implies there is no effect of nonmarketable assets or market segmentation on the level of asset prices. The second case, constant relative risk aversion, where the coefficient of relative risk aversion is equal to or less than one, implies that prices are lower in the presence of these imperfections. Arrow [1] argues that the coefficient of relative risk aversion must “hover around 1.” Thus, if constant relative risk aversion is a reasonable approximation to reality we should accept the implication of the latter case. Certainly, if a choice had to be made, the latter case would be the more palatable of the two. That is, constant relative risk aversion does imply decreasing absolute risk aversion, which appears more acceptable than the hypothesis of constant absolute risk aversion. The constant relative risk aversion case has implications for such things as the organization and operation of markets and corporate merger decisions. For example, higher margin requirements that inhibit diversification would be expected to lower asset values. Also, as a matter of corporate policy, it would appear that, ceteris paribus, mergers that increase the extent of a market would be preferable to within-market mergers.

Don B. Panton, V. Parker Lessig, O. Maurice Joy, Comovement of International Equity Markets: A Taxonomic Approach, The Journal of Financial and Quantitative Analysis, Vol. 11, No. 3 (Sep., 1976), pp. 415-432