Stock and Bond Liquidity and its Effect on Prices and Financial Policies

An asset is liquid if it can be traded at the prevailing market price quickly and at low cost. We show that in addition to risk, liquidity affects asset prices and returns. Theories of asset pricing suggest that the expected return of an asset is increasing in its risk, because risk-averse investors require compensation for bearing more risk. Because investors are also averse to the costs of illiquidity and want to be compensated for bearing them, asset returns are increasing in illiquidity. Thus, asset prices should depend on two asset characteristics: risk and liquidity. This paper surveys research on the effects of liquidity on asset prices and returns, showing that liquidity is an important factor in capital asset pricing.     

Unanticipated inflation and economic activity

This paper presents a new empirical test of the expectations-adjusted supply fucntion, obtaining the market's inflationary expectations from the market interest rate. The results support the existence of a positive relationship between unanticipated inflation and the level of economic activity, while anticipated inflation seems to have no effect.     

THE LIQUIDITY ROUTE TO A LOWER COST OF CAPITAL

The managements of many public companies do not pay much attention to the liquidity of their securities. Many if not most CEOs and CFOs feel powerless to affect what goes on in financial markets, and a common attitude among top executives is that maintaining liquidity is the concern of the securities exchanges and the Securities and Exchange Commission. This approach may work for those companies whose stocks are already highly liquid—a group made up mainly of large‐cap companies, as well as a number of smaller high‐flying, high‐tech firms. But, for the vast majority of public companies—especially smaller and mid‐sized firms—this is likely to be the wrong policy. As the authors of this article demonstrated in their pioneering study (published in the Journal of Financial Economics in 1986), liquidity appears to be a major determinant of a company's cost of capital. As their theory suggests and their empirical tests confirmed, the more liquid a company's securities, the lower its cost of capital and the higher its stock price. And, as discussed in this article, academic research since then has produced a large and impressive body of evidence linking greater liquidity to higher stock prices. Although recent technological innovations such as Internet‐based trading have increased liquidity generally, not all companies appear to have benefited equally. The authors offer a number of suggestions for companies intent on increasing the liquidity of their stock. Specifically, they propose that managers do the following: (1) consider measures, such as stock splits, designed to increase their investor base by attracting small investors; (2) seek trading venues for their securities that promise to increase liquidity; and (3) take advantage of the new Internet technology to provide more and better information to investors. Moreover, for smaller companies with little or no analyst coverage, the authors offer the radical suggestion that such companies actually pay analysts to cover their stock, much as companies pay Moody's or Standard & Poors to rate their bonds. This, in the authors' view, would be a more efficient alternative to the current practice of using stock splits to encourage intermediaries to make markets in the firm's shares.     

Multiple-Predictor Regressions: Hypothesis Testing

We propose a new hypothesis-testing method for multipredictorregressions in small samples, where the dependent variable isregressed on lagged variables that are autoregressive. The newtest is based on the augmented regression method (Amihud andHurvich, 2004), which produces reduced-bias coefficients andis easy to implement. The method's usefulness is demonstratedby simulations and by testing a model where stock returns arepredicted by two variables, income-to-consumption and dividendyield.     

The Foundations of Freezeout Laws in Takeovers

We provide an economic basis for permitting freezeouts of nontendering shareholders following successful takeovers. We describe a specific freezeout mechanism based on easily verifiable information that induces desirable efficiency and welfare properties in models of both corporations with widely dispersed shareholdings and corporations with large pivotal shareholders. The mechanism dominates previous proposals along some important dimensions. We also examine takeover premia that arise in the presence of competition among raiders. Our mechanism is closely related to the practice of takeover law in the United States; thus, our analysis may be thought of as analyzing the economic foundations of current regulations.     

Market microstructure and price discovery on the Tokyo Stock Exchange

This paper compares the price discovery processes at the opening and closing transactions for the fifty largest stocks trading on the Tokyo Stock Exchange. Open-to-open returns are found to have a greater volatility and a more negative autocorrelation pattern than close-to-close returns, similar to the pattern we found on the New York Stock Exchange. The results are consistent with pricing over-reaction at the opening and partial price-adjustment at the close. These patterns persist over time and prevail when estimated for returns conditional on the contemporaneous market effect. Our analysis of daytime and overnight returns suggest that pricing errors at the opening are corrected over the trading day. We present a new measure of volatility — the relative dispersion of stock returns around the market return — and find that it is greater at the opening, consistent with a more noisy price discovery process.     

Predictive regression with order-p autoregressive predictors

Studies of predictive regressions analyze the case where y_t is predicted by x_t ? 1 with x_t being first-order autoregressive, AR(1). Under some conditions, the OLS-estimated predictive coefficient is known to be biased. We analyze a predictive model where y_t is predicted by x_t ? 1, x_t ? 2,… x_t ? p with x_t being autoregressive of order p, AR(p) with p > 1. We develop a generalized augmented regression method that produces a reduced-bias point estimate of the predictive coefficients and derive an appropriate hypothesis testing procedure. We apply our method to the prediction of quarterly stock returns by dividend yield, which is apparently AR(2). Using our method results in the AR(2) predictor series having insignificant effect, although under OLS, or the commonly assumed AR(1) structure, the predictive model is significant. We also generalize our method to the case of multiple AR(p) predictors.