Stock Market Trading Volume
Andrew W. Lo
Massachusetts Institute of Technology (MIT) - Sloan School of Management; Massachusetts Institute of Technology (MIT) - Computer Science and Artificial Intelligence Laboratory (CSAIL); National Bureau of Economic Research (NBER)
Massachusetts Institute of Technology (MIT) - Sloan School of Management; China Academy of Financial Research (CAFR); National Bureau of Economic Research (NBER)
THE HANDBOOK OF FINANCIAL ECONOMETRICS, Y. A¨ıt-Sahalia and L. Hansen, eds., New York: North-Holland, 2009
If price and quantity are the fundamental building blocks of any theory of market interactions, the importance of trading volume in understanding the behavior of financial markets is clear. However, while many economic models of financial markets have been developed to explain the behavior of prices - predictability, variability, and information content - far less attention has been devoted to explaining the behavior of trading volume. In this chapter, we hope to expand our understanding of trading volume by developing well-articulated economic models of asset prices and volume and empirically estimating them using recently available daily volume data for individual securities from the University of Chicago's Center for Research in Securities Prices. Our theoretical contributions include: (1) an economic definition of volume that is most consistent with theoretical models of trading activity; (2) the derivation of volume implications of basic portfolio theory; and (3) the development of an intertemporal equilibrium model of asset market in which the trading process is determined endogenously by liquidity needs and risk-sharing motives. Our empirical contributions include: (1) the construction of a volume/returns database extract of the CRSP volume data; (2) comprehensive exploratory data analysis of both the time-series and cross-sectional properties of trading volume; (3) estimation and inference for price/volume relations implied by asset-pricing models; and (4) a new approach for empirically identifying factors to be included in a linear-factor model of asset returns using volume data.
Number of Pages in PDF File: 110
Keywords: Trading Volume, Asset Pricing, Portfolio Theory, Mean-Variance Optimization
JEL Classification: G11, G12, G14Accepted Paper Series
Date posted: May 16, 2009
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