Numerous empirical studies document patterns in the means and variances of security returns measured over periods that are punctuated by market closures. This article develops a multiperiod model in which closures delay the resolution of uncertainty, thereby redistributing risk across time and agents. Since agents are risk averse in the model, this redistribution affects the equilibrium price, altering risk premia, liquidity costs, and the degree of informational asymmetry. As a consequence, closures alter both the means and variances of returns. The article demonstrates that closures can generate a variety of mean and variance effects, including those that mirror the empirical phenomena.

Using a database that is free of survivorship bias, this article finds that book-to-market equity, earnings yield, and cash flow yield have significant explanatory power with respect to the cross-section of realized stock returns during the period from July 1940 through June 1963. There is a strong January seasonal in the explanatory power of these variables, even though small stocks are, by construction, excluded from the sample.

Numerous empirical studies document patterns in the means and variances of security returns measured over periods that are punctuated by market closures. This article develops a multiperiod model in which closures delay the resolution of uncertainty, thereby redistributing risk across time and agents. Since agents are risk averse in the model, this redistribution affects the equilibrium price, altering risk premia, liquidity costs, and the degree of informational asymmetry. As a consequence, closures alter both the means and variances of returns. The article demonstrates that closures can generate a variety of mean and variance effects, including those that mirror the empirical phenomena.

Numerous empirical studies document patterns in the means and variances of security returns measured over periods that are punctuated by market closures. This article develops a multiperiod model in which closures delay the resolution of uncertainty, thereby redistributing risk across time and agents. Since agents are risk averse in the model, this redistribution affects the equilibrium price, altering risk premia, liquidity costs, and the degree of informational asymmetry. As a consequence, closures alter both the means and variances of returns. The article demonstrates that closures can generate a variety of mean and variance effects, including those that mirror the empirical phenomena.

We study the relation between the number of news announcements reported daily by Dow Jones & Company and aggregate measures of securities market activity including trading volume and market returns. We find that the number of Dow Jones announcements and market activity are directly related and that the results are robust to the addition of factors previously found to influence financial markets such as day-of-the-week dummy variables, news importance as proxied by large New York Times headlines and major macroeconomic announcements, and noninformation sources of market activity as measured by dividend capture and triple witching trading. However, the observed relation between news and market activity is not particularly strong and the patterns in news announcements do not explain the day-of-the-week seasonalities in market activity. Our analysis of the Dow Jones database confirms the difficulty of linking volume and volatility to observed measures of information.

We study the relation between the number of news announcements reported daily by Dow Jones & Company and aggregate measures of securities market activity including trading volume and market returns. We find that the number of Dow Jones announcements and market activity are directly related and that the results are robust to the addition of factors previously found to influence financial markets such as day-of-the-week dummy variables, news importance as proxies by large 'New York Times' headlines and major macroeconomic announcements, and non-information sources of market activity as measured by dividend capture and triple switching rating. However, the observed relation between news and market activity is not particularly strong and the patterns in news announcements do not explain the day-of-the-week seasonalities in market activity. Our analysis of the Dow Jones database confirms the difficulty of linking volume and volatility to observed measures of information.

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We study the relation between the number of news announcements reported daily by Dow Jones & Company and aggregate measures of securities market activity including trading volume and market returns. We find that the number of Dow Jones announcements and market activity are directly related and that the results are robust to the addition of factors previously found to influence financial markets such as day‐of‐the‐week dummy variables, news importance as proxied by large New York Times headlines and major macroeconomic announcements, and noninformation sources of market activity as measured by dividend capture and triple witching trading. However, the observed relation between news and market activity is not particularly strong and the patterns in news announcements do not explain the day‐of‐the‐week seasonalities in market activity. Our analysis of the Dow Jones database confirms the difficulty of linking volume and volatility to observed measures of information.

Using a database that is free of survivorship bias, this article finds that book-to-market equity, earnings yield, and cash flow yield have significant explanatory power with respect to the cross-section of realized stock returns during the period from July 1940 through June 1963. There is a strong January seasonal in the explanatory power of these variables, even though small stocks are, by construction, excluded from the sample.

Using a database that is free of survivorship bias, this article finds that book‐to‐market equity, earnings yield, and cash flow yield have significant explanatory power with respect to the cross‐section of realized stock returns during the period from July 1940 through June 1963. There is a strong January seasonal in the explanatory power of these variables, even though small stocks are, by construction, excluded from the sample.