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Description
Economists studying asset pricing have begun to grapple seriously with the extraordinary diversity of financial market participants. Investors, including both households and financial institutions, differ in their overall resources, current and future labor income, housing and other assets that are expensive to trade, tax treatment, access to credit, attitudes towards risk, time horizons, and sophistication about financial markets. My recent research measures and models this heterogeneity, with a particular focus on time horizons and financial sophistication.
Behavioral finance emphasizes that some investors are likely to be more sophisticated about financial markets than others. Early behavioral models emphasized a distinction between "noise traders" and sophisticated arbitrageurs, the former trading randomly and creating profits for the latter. (1) This of course raises the question of who can be described as a noise trader. Discussions at conferences are sometimes reminiscent of the old verse "It isn't you, it isn't me, it must be that fellow behind the tree." Recent literature has argued that institutional investors act as arbitrageurs, while the household sector as a whole may play the role of noise traders.
Identifying Institutional Trading Activity
To test this idea, one would like to be able to measure institutional trading at relatively high frequency to see if institutions arbitrage well-known anomalies in asset returns. In the United States, large institutional investors are required to report their equity positions to the Securities and Exchange Commission quarterly in 13-F filings. Numerous papers have aggregated these reports and have looked at the implied quarterly positions of institutional investors. (2) Household positions can then be treated as the complement, if one interprets households broadly to include small institutions and certain foreign investors.
An alternative approach is to look at trades of different sizes. It is often assumed that large trades are carried out by institutions while small trades more likely reflect individual buying or selling. The Trade and Quotes (TAQ) database allows researchers to measure each trade in each stock, and to classify trades as buys and sells based on their relation to previous quotes. Several researchers have found that large trades appear to exploit phenomena such as price and earnings momentum. (3)
It is natural to ask whether these two approaches are consistent. In quarters where a stock has been subject to a high volume of large buy orders, does the stock end up with higher institutional ownership at the end of the quarter? In joint work with Tarun Ramadorai and Allie Schwartz, I have studied this question in data from the late 1990s and have found that both unusually large and unusually small trades appear to indicate institutional activity. (4) This does not necessarily mean that small trades are more likely to be institutional;... |
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