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Caught on Tape: Predicting Institutional Ownership With Order Flow

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  • John Y. Campbell
  • Tarun Ramadorai
  • Tuomo O. Vuolteenaho

Abstract

Many questions about institutional trading can only be answered if one can track institutional equity ownership continuously. However, these data are only available on quarterly reporting dates. We infer institutional trading behavior from the "tape," the Transactions and Quotes database of the New York Stock Exchange, by regress- ing quarterly changes in reported institutional ownership on quarterly buy and sell volume in different trade size categories. Our regression method predicts institutional ownership signifcantly better than the simple cutoff rules used in previous research. We also find that total buy (sell) volume predicts increasing (decreasing) institutional ownership, consistent with institutions demanding liquidity in aggregate. Furthermore, institutions tend to trade in large or very small sizes: buy (sell) volume at these sizes predicts increasing (decreasing) institutional ownership, while the pattern reverses at intermediate trade sizes that appear favored by individuals. We then explore changes in institutional trading strategies. Institutions appear to prefer medium size trades on high volume days and large size trades on high volatility days.

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Bibliographic Info

Paper provided by Harvard - Institute of Economic Research in its series Harvard Institute of Economic Research Working Papers with number 2046.

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Date of creation: 2004
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Handle: RePEc:fth:harver:2046

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  1. Tuomo Vuolteenaho, 2002. "What Drives Firm-Level Stock Returns?," Journal of Finance, American Finance Association, vol. 57(1), pages 233-264, 02.
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Cited by:
  1. Asani Sarkar & Robert A. Schwartz, 2007. "Market sidedness: insights into motives for trade initiation," Staff Reports 292, Federal Reserve Bank of New York.
  2. Asani Sarkar & Robert A. Schwartz, 2006. "Two-sided markets and intertemporal trade clustering: insights into trading motives," Staff Reports 246, Federal Reserve Bank of New York.

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