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The authors demonstrate that trade informativeness is highest for institutions and lowest for the retail group of traders. Algorithmic trading generally provides advantages versus manual trading, especially in certain scenarios.


Using transactions-based calendar time (TBCT) portfolio analysis, we investigate informativeness of trades of investor categories, namely institutions, proprietary traders, and retail clients. We find that trade informativeness is positive for institutional and negative for retail-client investors. The informativeness of liquidity-demanding trades are less than the informativeness of liquidity-supplying trades for all trading groups, over both long and short horizons. We also find that institutions are benefitted by algorithmic executions compared to manual executions and this benefit is elevated on days of high volume and volatility. Proprietary algorithmic traders (high-frequency traders) generate positive alpha for their trades only from their liquidity-supplying trades.

About the Author(s)

Samarpan Nawn CFA

Samarpan Nawn, CFA, is an assistant professor in the Department of Finance and Accounting at the Indian Institute of Management Udaipur, Udaipur, Rajasthan, India.

Gaurav Raizada

Gaurav Raizada is a founder at Irage Capital and visiting faculty at the Indian Institute of Management Ahmedabad, Ahmedabad, Gujarat, India.