Trade Informativeness in Modern Markets

Abstract

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.

Keywords:

• algorithmic trading
• HFT
• informativeness
• institutional traders
• limit orders
• market orders

PL Credits: 2.0:

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Notes

1 World Federation of Exchanges 2020 report.

2 Alphas are risk adjusted (Fama-French-Carhart four factor).

3 The direct market access was provided in 2010.

4 https://www.world-exchanges.org/our-work/articles/2020-annual-statistics-guide.

5 Colocation services amount to renting rack space in the premises of the exchange itself to reduce latency, i.e., to reduce the time gap between an order being placed and it actually reaching the exchange.

6 The results are available on request from the authors.

7 Odean (1998), Odean (1999), and Barber and Odean (2000) use trading account data from a discount brokerage house.

8 SMB is the Fama-French size factor constructed by longing a portfolio of small firms and shorting a portfolio of big firms. HML is the Fama-French value factor constructed by longing a portfolio of high-value firms and shorting a portfolio of low-value firms. UMD is the momentum factor constructed by subtracting the returns of a portfolio of losing stocks from the returns of a portfolio of winning stocks.

9 As an alternative, we also create a sample of stocks with fewer than three analysts covering in all the 3 years. The results are qualitatively similar.

10 Available from the authors.

11 Finally, for the group of the rest of stocks, we do not see any major information flow from this method. Table 2 suggests that the retail client contributes almost 75% of the turnover in this group of smallest stocks. Such a skew in the distribution perhaps makes this analysis on the group of the rest of stocks incomprehensible.

12 Thus, the trades of the form (₹ X.05, ₹ X.10, ₹ X.15,…, ₹ X.45, ₹ X.55, ₹ X.60,…., ₹ X.90, ₹ X.95) are termed as non-clustered.

13 Unreported, available from the authors.

14 Unreported, available from the authors.

Additional information

Notes on contributors

Samarpan Nawn

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.