By Yakov Amihud, Haim Mendelson, Lasse Heje Pedersen
Liquidity and Asset costs reports the literature that reviews the connection among liquidity and asset costs. The authors assessment the theoretical literature that predicts how liquidity impacts a security's required go back and talk about the empirical connection among the 2. Liquidity and Asset costs surveys the idea of liquidity-based asset pricing by way of the empirical facts. the idea part proceeds from simple types with exogenous keeping sessions to those who include extra components of possibility and endogenous preserving classes. The empirical part experiences the facts at the liquidity top class for shares, bonds, and different monetary resources.
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Extra resources for Liquidity and Asset Prices
Example text
Gottesman and Jacoby obtain that while for dividend-paying stock, return is an increasing and concave function of the bid–ask spread, for stock repurchases the function is increasing and it may be convex or concave, depending on the mass of investors’ holding-period clientele. They test the theory using data from Nasdaq during 1993–1999, when stock repurchases were far more popular than in the 1961–1980 period studied by Amihud and Mendelson. The results show that in general, the return–spread relationship is positive and concave (controlling for other characteristics).
7. 1 27 Illiquidity deriving from private information Certain investors or corporate insiders can have superior information (or information processing ability) about the fundamental value of a security. This creates an adverse selection problem: informed traders with bad news are likely to sell, and informed traders with good news have an incentive to buy (Akerlof, 1970). Grossman and Stiglitz (1980) show that information asymmetries are fundamental to market equilibrium for, if all information were contained in prices, no one has an incentive to gather information in the first place.
S. only recently and are thus available only for a relatively short period of time. Further, studies of the effect of liquidity on expected stock returns use ex-post or realized returns, whose variance around the expected return is high. Consequently, researchers need a large amount of data – long time series – to increase the power of their tests. Given the short duration of high-frequency data, this poses a problem. Researchers need then to find substitute measures of liquidity using low-frequency data, such as daily return data and perhaps trading volume.