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Liquidity commonality beyond best prices: Indian evidence

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Abstract

This paper investigates the phenomenon of liquidity commonality in the Indian market, for the 3-year period spanning 2015–2017. Principal component analysis and canonical correlation analysis are performed on a number of liquidity proxies to provide the evidence of commonality. Following the market model of Chordia et al. (J Financ Econ 56(1):3–28, 2000), we show that the level of liquidity commonality for this order-driven emerging market is higher, as compared to that reported for quote-driven markets. There is evidence to suggest that both the inventory and information asymmetry hypotheses significantly contribute to the commonality in this important emerging market. Commonality is observed even at the deeper levels beyond the best prices, indicating a significant role of information asymmetry hypothesis. The study also provides evidence of specific intraday patterns in liquidity and ‘free-entry and free-exit’ phenomenon.

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Article Open access 05 November 2018

Clifford S. Asness, Andrea Frazzini & Lasse Heje Pedersen

Notes

  1. https://www.imf.org/en/Publications/WEO/Issues/2019/07/18/WEOupdateJuly2019.

  2. https://www.imf.org/external/datamapper/NGDP_RPCH@WEO/OEMDC/ADVEC/WEOWORLD.

  3. In LOBs, limit-orders supply liquidity, and therefore, limit order suppliers become the de-facto market makers.

  4. https://economictimes.indiatimes.com/news/economy/india-becomes-5th-largest-economy-overtakes-uk-france-report/articleshow/74181024.cms?from=mdr.

  5. https://www1.nseindia.com/content/us/ismr_full2019.pdf.

  6. https://focus.world-exchanges.org/issue/january-2020/market-statistics.

  7. For instance, in case the market-wide circuit breaker is initiated in the cash segment, the F&O segment automatically shuts down.

  8. CBOE: Chicago Board of Exchange, NYSE: New York Stock Exchange, and NASDAQ: National Association of Securities Dealer and Automated Quotations.

  9. https://www.world-exchanges.org/home/index.php/statistics/market-highlights. For the 6-month period ending June 2018, NSE was the second largest exchange in terms of the number of trades in equity shares.

  10. https://www.nseindia.com/products/content/derivatives/equities/selection_criteria.htm.

  11. Negative spread measures indicate data recording errors.

  12. We aim to select the stocks that are informationally efficient and least influenced by the information asymmetry. This ensures that the evidence in favor of information asymmetry paradigm is robust and reliable.

  13. The analysis employs deeper levels of the order book beyond the best prices. For instance, the information pertaining to the top five buy and sell limit orders is referred to as L5. Similarly, L10 captures the information pertaining to the best ten buy and sell limit orders, and so on for L50, L100, and complete order book.

  14. For robustness checks, in addition to the 30-s VWAP price, the study also employs 5-, 10-, 15-, 20-, and 25-s VWAP prices. The results remain very similar; therefore, for brevity, we report results based on 30-s window.

  15. The 60-min VWAP returns are employed in the CRS (2000) method, and the other return measures (1-, 5-, 10-, 15-, 30-min) are employed in the PCA and canonical correlations.

  16. Standard tick-test algorithm is employed to classify the trades into buy or sell transactions, following Chakrabarty et al. 2015; Ellis et al. 2000; Odders-White 2000.

  17. The results are provided for all the three normalized order imbalance measures: based on the number of trades, volume, and dollar volume.

  18. To test commonality: (1) PCA and canonical correlations are applied on liquidity measures at levels, and (2) CRS regressions are employed on the first-differenced liquidity measures.

  19. For example, if a set of 5 and 6 covariates have to be compared, simple pairwise correlation tests will yield 30 correlations and the corresponding 30 t-statistics. In this case, however, a canonical correlation analysis will provide only one comprehensive correlation measure and the corresponding Chi-square statistic. For detailed discussion on PCA and canonical correlations, please refer Hasbrouck and Seppi (2001) and Korajczyk and Sadka (2008).

  20. The window of 15-min offers a reasonable trade-off between a ‘less noisy’ and ‘contemporaneous with the state of liquidity’ measure of volume.

  21. The fixed-effect panel regression methods are widely applied to contain the vitiating effect of unobserved heterogeneity in estimation (Baltagi 2008; Hsiao 2003). For the selection of an appropriate panel regression model, we conduct the standard Hausman and poolability tests. The results from the other models (viz., the random-effects, between, Hausman–Taylor, first-differenced, and pooled), however, are similar. The intuition for the fixed-effect model is as follows. The observation set comprises diverse firms from various industries. Therefore, we expect the cross-sectional variations (firm-specific time invariant effects) to contribute significantly to the unobserved heterogeneity across observations.

  22. Hence, this model does not include the first snapshot of10 A.M.

  23. Short-term return measures for 1-, 5-, 10-, 15-, and 30-min periods are employed for the PCA and canonical correlation analyses. For CRS regressions, hourly returns are used.

  24. Short-sale restrictions require the naked short-positions to be compulsorily squared-off during the same day, and cannot be carried forward.

  25. NSE defines the impact cost as “percentage price movement caused by an order size of INR 0.1 million from the average of the best bid and offer price in the order book snapshot. The impact cost is calculated for both, the buy and the sell side in each order book snapshot”. The study computes the impact cost for the order sizes of INR 0.1 million, INR 0.2 million, INR 0.3 million, INR 0.5 million, INR 1.0 million, and complete order book. However, the trade volumes above INR 0.3 million are not as frequent, and therefore, not very pertinent from the commonality perspective.

  26. The first principal component explains nearly 40% of the total variation in relative spreads for the cross-section of securities over the study period. That broadly indicates the level of commonality in the liquidity of securities.

  27. The study assumes that each trade involves two legs- a limit order and a market order that crosses against the limit order. Therefore, the traded volume provides a good proxy of the market orders (Chordia et al. 2008).

  28. It is presumed that the limit orders beyond 100 may also include non-serious orders and may not carry much information. This is evident from the drop in \({\text{adj}}. R^{2}\) measure and the t-statistics pertaining to the industry and market liquidity coefficients.

  29. The portfolio measures of liquidity, returns, volume, etc., are computed as the equally-weighted averages of these measures for the individual stocks in that portfolio.

  30. The effect of noise is significantly reduced in portfolios, which causes the evident increase in the \({\text{adj}}. R^{2}\) measure (Chordia et al. 2000, 2008).

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  1. Finance and Accounting, Indian Institute of Management Lucknow, Lucknow, India

    Abhinava Tripathi,  Vipul & Alok Dixit

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  1. Abhinava Tripathi
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Appendix A

Appendix A

See Table 7.

Table 7 Commonality with the industry and market liquidity using market model panel regressions for impact cost
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Tripathi, A., Vipul & Dixit, A. Liquidity commonality beyond best prices: Indian evidence. J Asset Manag 21, 355–373 (2020). https://doi.org/10.1057/s41260-020-00164-3

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