The facts of increased institutional trading on the nation's securities markets are by now well known. On the New York Stock Exchange (NYSE), the six major institutional groups—insurance companies, investment companies, noninsured pension funds, nonprofit institutions, common trusts, and mutual savings banks, now own more than one-fourth of the market value of listed shares compared with less than 16 percent at the end of 1956. But, ownership is merely the tip of the perennial iceberg, since institutional trading of stock has become much more significant than institutional ownership. This fact is pointed up in the recent SEC Study of Institutional Investors. It shows that there has been a relatively slow increase in the share of outstanding stock owned by institutions in all markets, but the institutional share of trading has mushroomed.

The hypothesis that corporate directors determine dividend payments by applying target payout ratios and related parameters to current and past earnings has gained widespread acceptance in recent years as a generally applicable account of dividend policy. One reason for the acceptance of this view appears to be its success in predicting the behavior of aggregate measures of dividends in time series regression analyses. The interpretation of these findings as supporting the target payout hypothesis may well be incorrect since the behavior of aggregate magnitudes need bear no direct and close relationship to the behavior of individual firms. This paper attacks the target payout hypothesis on two grounds: it shows that the behavioral characteristics of the firm assumed by the hypothesis have no satisfactory basis in managerial motivation; and it presents data relating to the dividend policies of individual firms for which the hypothesis fails to account.

Current conditions in the money and credit markets, along with the memory of the “crunch” of late 1966, have caused both the managers of financial institutions and their regulators to reconsider their concepts of “liquidity.” Both Minsky and Ritter have argued forcefully that traditional attention paid to balance sheet proportions should be abandoned in favor of an intertemporal analysis of cash flows [6], [7], and [8]. Ritter states that: “With a multidimensional cash flow forecast extending several years into the future, probability estimates can be made regarding potential liquidity needs over time” [8]. The purpose of this paper is to suggest a forecasting method which explicitly deals with the problem of uncertainty. The method is specifically designed for a mutual savings bank, but it could easily be adapted for a savings and loan association and, with perhaps greater effort, a commercial bank.2 In Section I, components of cash inflows and outflows are examined with the general objective of classification according to: (1) degree of uncertainty and (2) degree of management control. A risk analysis simulation model which permits explicit consideration of uncertainty is presented in Section II. Problems of implementing the model are discussed in Section III, while the implications of the analysis for future research are considered in Section IV.

This paper develops a hypothesis regarding the factors which influence the average maturity of corporate debt, and the hypothesis is tested with a cross-sectional sample of large industrial firms. The sample consists of 159 companies from the Fortune 500 list of the largest industrial firms as of 1971. A linear regression model is developed with the firm's average debt maturity expressed as a function of the firm's asset maturity, size, variability of income, growth, and the proportion of debt in the firm's capital structure.

Investment literature, particularly materials made available to investors by financial magazines, brokerage houses, and investment services, places a great deal of stress upon analysis of investment opportunities by industry groups. Examples are industry analyses, such as those of Forbes and Financial Analysts Journal, as well as the industry segregations of popular services such as Value Line, Standard & Poor's and Moody's.

For as long as governments have intervened in economic affairs, private decision makers have searched for signs and indicators of ongoing or impending government actions. A century ago, anxious British bankers relied on changes in the bank rate to provide them with information on the intent of the Bank of England. Contemporary decision makers not only analyze changes in policy-tools monetary, fiscal, or debt policy actions—but they carefully scrutinize the words of ever more talkative economic policy makers. The State Department's legendary crew of “China watchers” could not analyze pronouncements by Chairman Mao any more carefully than money market bankers and security dealers analyze an economic report of the President or a speech by the Chairman of the Board of Governors of the Federal Reserve.

In my article, I have discussed the relationship between the internal rate of return, K, and Kp which denotes the reciprocal of the payoff period under several alternative assumptions.

We study the link between the individual propensity to violate moral principles and the demand for finance based on two data sets: the World Values Survey and a data set with the legal records of U.S. chief executive officers (CEOs). We find that individuals who are more tolerant of moral principle violations are more likely to borrow. Corporate executives with legal records are also associated with larger mortgages. Reverse causality and attitudes toward risk are unlikely explanations for our findings. We contend that noncompliance relaxes participation constraints in capital markets by lowering the psychological costs of entering and breaking a contract.

Conventional collateral requirements for derivatives are conservative, but not explicitly designed to buffer systemic risk. I explore collateral adequacy against systemic risk in the Canadian futures market during the 2008 crisis. I find that conventional collateral levels adequately absorb systemic risk, even allowing for an implausibly high level of stress, and that systemic risk spillovers do not exceed the effect of an approximately 1% downward stock price move. I also document that the largest systemic risk contributors are buffered relatively less than the rest, and that there is a large cross-country difference in the behavior of U.S. and Canadian institutions.

CEO trustworthiness is positively related to long-term excess returns after buyback announcements. When the Chief Executive Officer (CEO) is trustworthy, statements that the stock is undervalued are more credible. CEO trustworthiness is initially measured by the extent to which people in the county where the company headquarters is located trust each other. Further, the positive impact of trustworthiness on excess returns is higher when the CEO has been a long-term resident of a high-trust county, and correspondingly, trustworthy CEOs are less likely to be accused of financial misreporting. Our conclusions are confirmed when we use alternative measures of trustworthiness such as employee trust and CEO integrity.

We document that firms that expect their CEOs’ compensation to exceed the median CEO compensation of their Institutional Shareholder Services (ISS) peers influence ISS to revise these peer sets. Controlling for changes in firm characteristics that ISS uses to select peers, we find that ISS applies an abnormally high turnover rate in the members of these peer sets and increases the representation of focal firms’ chosen peers. This turnover results in increases in the medians of the ISS peers’ CEO compensation and size. We find that these firms underperform and conclude that they attempt to camouflage high CEO pay to mitigate outrage costs.

We document a new empirical phenomenon in which the aggregate positions of money managers, who are sophisticated speculators in the commodity futures market, as disclosed by the Disaggregated Commitments of Traders reports, can predict the cross section of commodity producers’ stock returns in the subsequent week. We employ a number of cross-sectional methods, including calendar-time regression analysis, single-sort, double-sort, and Fama–MacBeth regressions, to confirm the predictability results. The results are more pronounced in firms with higher information asymmetry. We thus add more empirical evidence to the literature on costly information processing, which leads to gradual information diffusion across asset markets.

This article proposes a novel way to estimate consumer switching costs and uses Lithuanian credit register data and two bank closures to provide this estimate for the loan market. I show that when a distressed bank’s closure forced firms to switch, they started borrowing at lower interest rates instantly and permanently and that this drop revealed the lower bound of firms’ ex ante switching costs. A healthy bank’s closure showed no such effect. The article’s findings suggest that distressed banks hold up and overcharge firms and that by closing and resolving such banks with good-bank/bad-bank separation regulators can improve firm financing.

The coefficient of variation (CV) in investment returns is often presented in introductory finance texts as a measure of project risk [7, 9, 13, 15]. Curiously, the resulting mean-coefficient of variation (MCV) efficiency criterion is usually casually proposed as an alternative to the more widely recommended mean-standard deviation (MSD) definition of efficiency, as if MCV possessed an obvious intuitive appeal for some investors or some investment situations. The pervasiveness of references to CV as a risk measure is perplexing in light of the absence of utility theoretic underpinnings, especially by contrast with the substantial theoretical effort underlying the MSD notion of efficiency [3, 6, 8, 10, 11, 12].

Professor Wu, in his recent article [8], investigated the trading activities of odd-lotters and their market impact. The purpose of this note is to update the data examined by Wu since recent trends are extremely significant and to question the use and interpretation of some of Wu's original data. In addition, some errors in Wu's article will be discussed.