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Information Content of an Open Limit-Order Book: Indian Evidence

Mayank Gupta & Saurabh Kumar

American Business Review2025https://doi.org/10.37625/abr.28.1.34-64article
ABDC B
Weight
0.50

Abstract

Using the 5-year period (2014-18) limit-order book and trade book data from NSE, this paper investigates the contribution of an open limit-order book towards price discovery and market efficiency. In particular, the paper examines two interesting questions: (a) the information content of the limit-order book beyond the best prices and (b) the order submission choices of informed traders. The results indicate that the deeper levels of the order book significantly contribute to short-term return predictability, even after controlling for the return autocorrelations, volume, and trade imbalances. Furthermore, this return predictability is time-varying and relatively high around lower quantiles of the return distribution. There is evidence to suggest that informed trading is more pronounced during the environments characterized by low liquidity and high uncertainty. The information share (Hasbrouck, 1995) and component share (Gonzalo & Granger, 1995) measures show that, out of the total contribution of the limit-order book to price discovery, the deeper levels’ share is nearly 50%.

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https://doi.org/https://doi.org/10.37625/abr.28.1.34-64

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@article{mayank2025,
  title        = {{Information Content of an Open Limit-Order Book: Indian Evidence}},
  author       = {Mayank Gupta & Saurabh Kumar},
  journal      = {American Business Review},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.37625/abr.28.1.34-64},
}

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Information Content of an Open Limit-Order Book: Indian Evidence

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.