This paper shows that the results of variance-bound tests depend on how cash distributions to shareholders are measured. As in prior studies, we find apparent evidence of excess volatility when a narrow definition of cash flow (dividends only) is applied. However, we are unable to reject the hypothesis of market efficiency when the cash flow measure also includes share repurchases and takeover distributions in addition to ordinary cash dividends.
Most current empirical work finds no evidence that money shocks lower interest rates. We show that these nonresults are mainly due to a failure to model the conditional heteroskedasticity of interest rates. Autoregressive conditional heteroskedasticity (ARCH) models find a significant liquidity effect where ordinary least squares (OLS) models do not. The existence of a liquidity effect is found using different models and sample periods when ARCH models are used in estimation, but never when OLS is employed.
This paper developes a semiautoregression (SAR) approach to estimate factors of the arbitrage pricing theory (APT) that has the advantage of providing a simple asymptotic variance‐covariance matrix for the factor estimates, which makes it easy to adjust for measurement errors. Using the extracted factors, I confirm the finding that the APT describes asset returns slightly better than the CAPM, although there is still some mispricing in the APT model. I find that not only are the factors “priced” by the market, but the factor premiums move over time in relation to business cycle variables.
This paper analyzes the role of capital structure in the presence of intrafirm influence activities. The hierarchical structure of large organizations inevitably generates attempts by members to influence the distributive consequences of organizational decisions. In corporations, for example, top management can reallocate or eliminate quasi rents earned by their employees, while at the same time, they must rely on these employees to provide them with information vital to their decision making. This creates the opportunity for lower level managers to influence top management's discretionary decisions. As a result, divisional managers may attempt to inflate the corporate perception of their relative contributions to the firm, or to take actions that make the elimination of their rents more costly for the firm. This incentive to influence is especially acute when managers fear losing their jobs, for example in the event of a divestiture. Since the firm's capital structure can affect future divestiture decisions, it can be chosen to reduce or increase the divisional managers' incentives to influence top management's decisions. The control of influence activities arises at the expense of restrictions on future divestiture decisions. Hence, there emerges an optimal capital structure that trades off the costs of influence activities against the costs of making poor divestiture decisions. The findings suggest that capital structure can also be chosen to control influence activities that arise under less extreme motivations. We identify several key factors that determine the optimal capital structure: the top management's prior assessment of the likelihood that it will be optimal to divest a specific division; the costs of influence activities to the firm and to the divisional managers; and the difference in the valuation of the division's assets in the current firm and under alternative uses.
Forward and spot exchange rates between major currencies imply large standard deviations of both predictable returns from currency speculation and of the equilibrium price measure (the intertemporal marginal rate of substitution). Representative agent theory with time-additive preferences cannot account for either of these properties. We show that the theory does considerably better along these dimensions when the representative agent's preferences exhibit habit persistence, but that the theory fails to reproduce some of the other properties of the data—in particular, the strong autocorrelation of forward premiums.
This paper reexamines the ability of dividend yields to predict long-horizon stock returns. The authors use the bootstrap methodology, as well as simulations, to examine the distribution of test statistics under the null hypothesis of no forecasting ability. These experiments are constructed so as to maintain the dynamics of regressions with lagged dependent variables over long horizons. They find that the empirically observed statistics are well within the 95 percent bounds of their simulated distributions. Overall there is no strong statistical evidence indicating that dividend yields can be used to forecast stock returns. Copyright 1993 by American Finance Association.(This abstract was borrowed from another version of this item.)(This abstract was borrowed from another version of this item.)