This paper tests for seasonal patterns in corporate bond returns using the Dow Jones Composite Bond Average. Each seasonal pattern documented for equities is investigated. For the period 1963–1986, corporate bond returns exhibit January, turn-of-the-year, and weekof-the-month effects, but no significant day-of-the-week or turn-of-the-month effects. In contrast, for the S&P 500 stock index, the turn-of-the-month and day-of-the-week effects are highly significant, but the week-of-the-month effect is less significant, and the January and turn-of-the-year effects are insignificant. The behavior of an equity index constructed using companies in the bond index is similar to that of the S&P, except the turn-of-the-year effect is significant.

This article shows that correlated errors in news about fundamentals are an important, rational determinant of excess comovement. Individual analysts’ forecast errors tend to be correlated across stocks. Using a proxy for correlated forecast errors based on analyst coverage, I find that stocks with similar sets of analysts exhibit more excess comovement, controlling for industry and other variables. Exogenous changes in commonality in analyst coverage around i) brokerage firm mergers and ii) additions to an index lead to changes in excess comovement. This information channel explains 10% to 25% of the increase in comovement around additions to the S&P 500 index.

The Capital Asset Pricing Model predicts that investors will hold diversified portfolios, but many households actually hold very few assets. The paper examines the asset pricing implications of one possible explanation for this phenomenon, fixed costs of holding assets. While earlier authors found the exact asset pricing effects of such costs in single-period models under restrictive assumptions, I derive a general upper bound on these effects that is also valid in continuous time. Illustrative calculations reveal that large holding costs must be postulated to generate significant asset pricing effects.

Empirical studies of the behavior of stock returns are important for several reasons. First, the nature of stock return behavior is fundamental to the formulation of the concept of “risk” (or “uncertainty”) in various financial theories and models. Second, the measurement of risk depends heavily on properties (such as the stationarity, long-tailedness, finiteness of the second and higher moments, etc.) of empirical stock return distributions. Third, various tests for the empirical validity of financial models [28] and the applications of these models (e.g., to the evaluation of investment performances [21], [22]) rely to a considerable extent on the steadiness over time of stock return distributions and the constancy of systematic risk. Fourth, several important pricing models for stock options, warrants, convertible debentures, and other similar financial instruments usually require explicit estimates of stock return variances [5]; the usefulness of such models depends largely on the adequacy (e.g., the finiteness, accuracy, etc.) and the stationarity of the variance measurements.

To sum up, Emery and Cogger [5] have raised several interesting questions concerning the derivation of the safety index (as well as the related risk of ruin) and the interpretation of that index which needed to be addressed. While the potential limitations discussed are theoretically possible, closer examination reveals that most of the concerns raised are unlikely to occur in practical applications, although certain of the procedures utilized were in need of further explanation. Several of these issues also provide extensions of the present work to make the estimation of the risk of ruin an even more robust measure of the potential for corporate failure.

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.

This paper develops an exact theoretical test of the presence or absence of a filter effect for a portfolio of securities and a general number of different filter sizes. It is a natural development from Praetz [8], which obtained exact expressions for the mean and variance of rates of return of the investment strategies under filter tests assuming the underlying stochastic process is a random walk. These expressions showed that expected returns from filter strategies are, in fact, less than the return from a buy-andhold alternative with which filter returns are usually compared.

The certainty-equivalent method of evaluating risky investments has been widely discussed in the literature ([2], [5], [14, p. 356], [19], [20]) and consists of applying a multiplicative factor, αt, to each period's expected cash flow, μt, to produce a certainty-equivalent flow, αtμt. The certainty-equivalent flow is then discounted with the riskless rate of interest, αtμt/(l + i)t. Although there has been much discussion of αt, researchers have not derived explicit expressions for αt, relying instead on ad hoc graphs [24, p. 328] or arguments involving mean-variance indifference curves [2] which may not even exist ([4], [12], [22], [23]). In this paper, I will (1) provide a rigorous definition of αt, (2) derive formal expressions for a for αt three special cases, (3) discuss relationships between αt and σt, the standard deviation of the period t cash flow, (4) formally derive the period t risk-adjusted discount rate, kt, from assumptions concerning the decision maker's (d. m.'s) risk preferences and cash flow distribution, and (5) apply the preceding results to a specific problem involving calculation of the risk-adjusted present value of an uncertain cash flow stream.

I was interested to read Meyer and Kim [5], where I learned a little about sugar futures, but regret to say that I found the attempted Box-Jenkins analysis singularly lacking in expertise. It is my intention, here, to discuss a few of its most obvious shortcomings. My list will not be exhaustive, but will include just five points.