Ebook Earnings Announcement Clustering, Systematic Liquidity Shocks, and Expected Returns
In this study, we argue that earnings announcement clustering during earnings seasons can have significant effects on stock market liquidity, liquidity betas, and liquidity risk premia. Since the information flow is highly concentrated during a very short interval, this creates a potential for market-wide liquidity shocks.
These shocks result in time-varying liquidity risk which in turn leads to changes in the liquidity risk premium that investors require to hold the stocks. We estimate that the marginal liquidity risk premium lies in the range between 0.5% and 2.4% per year.
Earnings season is arguably one of the most important time periods on Wall Street. During these periods very important economic information floods the market keeping busy thousands of analysts, professional and amateur traders, and the business press. In spite of its significance for the Wall Street and practitioners in general, this unique period of the year has received scant (if any) attention from academia. This is surprising since as soon as one deviates from the world of perfect and complete information, the importance of information arrival (how, when, and how fast) on asset prices becomes significant (see O'Hara (2003) for a general discussion).
Even though the clustering effect of earnings seasons has been ignored, there is a vast stream of literature in finance and accounting that studies the impact of individual firms earnings announcements on financial markets. Numerous studies have shown that bid-ask spreads widen and depths on the limit books substantially decrease around individual firms earnings announcements. In addition, trading volumes sharply decrease just before announcements and surge right after the announcements (Chae (2005). Most authors conclude that the observed patterns are best explained by the changing information environment around these events. There are, however, several ways in which the information environment can affect asset prices.
When a company announces its quarterly earnings, the opportunity for informed trading (whether by insiders or better information processors) is arguably at its highest. Even though none of the previous studies looks at how the changing information environment impacts asset prices, there are many studies that argue that the proportion of informed to uninformed (noise or liquidity) traders matters enough to affect required rates of return.
For example, Easley et al. (2002) develop a multi-asset rational expectations equilibrium model in which stocks have differing levels of public and private information. They show that in equilibrium, uninformed traders require compensation to hold stocks with greater private information, resulting in cross-sectional differences in returns. Admati (1985) generalize Grossman and Stiglitz's (1980) analysis of partially revealing rational expectations equilibrium to multiple assets and show how individuals face differing risk-return trade-offs when differential information is not fully revealed in equilibrium. Wang (1993) provides an intertemporal asset-pricing model in which traders can invest in a riskless asset and a risky asset. In his model, the presence of traders with superior information induces an adverse selection problem, as uninformed traders demand a premium for the risk of trading with informed traders.
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