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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.

Suggested Citation

  • John Y. Campbell & Tarun Ramadorai & Tuomo O. Vuolteenaho, 2004. "Caught on Tape: Predicting Institutional Ownership With Order Flow," Harvard Institute of Economic Research Working Papers 2046, Harvard - Institute of Economic Research.
    • Handle: RePEc:fth:harver:2046
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    References listed on IDEAS

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    Cited by:

    1. Asani Sarkar & Robert A. Schwartz, 2009. "Market Sidedness: Insights into Motives for Trade Initiation," Journal of Finance, American Finance Association, vol. 64(1), pages 375-423, February.
    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.
    3. Natashekara, Karthik & Sampath, Aravind, 2024. "Informed trading and cryptocurrencies. New evidence using tick-by-tick data," Finance Research Letters, Elsevier, vol. 61(C).
    4. Tan, Xiaoyu & Zhang, Zili & Zhao, Xuejun & Wang, Chengxiang, 2021. "Investor sentiment and limits of arbitrage: Evidence from Chinese stock market," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 577-595.
    5. Tsai, Shih-Chuan, 2013. "Investors' information advantage and order choices in an order-driven market," Pacific-Basin Finance Journal, Elsevier, vol. 21(1), pages 932-951.
    6. Ascioglu, Asli & Comerton-Forde, Carole & McInish, Thomas H., 2011. "Stealth trading: The case of the Tokyo Stock Exchange," Pacific-Basin Finance Journal, Elsevier, vol. 19(2), pages 194-207, April.
    7. Charlie Charoenwong & David K. Ding & Tiong Yang Thong, 2016. "Decimalization, IPO aftermath, and liquidity," Review of Quantitative Finance and Accounting, Springer, vol. 47(4), pages 1303-1344, November.
    8. Brian J. Bushee & Theodore H. Goodman, 2007. "Which Institutional Investors Trade Based on Private Information About Earnings and Returns?," Journal of Accounting Research, Wiley Blackwell, vol. 45(2), pages 289-321, May.

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    More about this item

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G20 - Financial Economics - - Financial Institutions and Services - - - General

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