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How Effective are Stock Market Reforms in Emerging Market Economies? Evidence from a Panel VAR Model of the Indian Stock Market

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Abstract

The paper uses a panel VAR framework to estimate the impact of a series of reforms aimed at reducing transactions cost and information cost in India’s secondary market for equity, on trading cost and trading volume. In particular, we focus on the reforms that were introduced after the creation of the National Stock Exchange (NSE) and screen-based trading that have been much discussed in the literature. Our results suggest that only the creation of the clearing corporation that reduced or eliminated counterparty risk had an economically meaningful/significant impact on trading cost and volume. We also find that the impact was much greater for mid-cap firms and, to a lesser extent, for small-cap firms than for large-cap firms. Further, while trading costs and trading volumes Granger cause each other for mid-cap firms, there is only one-way causality for large-cap firms—trading cost Granger causes volume but the reverse is not true, and for small-cap firms there is no causal relationship between the two. The policy implications of these findings are discussed in the paper.

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Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Notes

  1. In addition, there is a fairly large literature on informational efficiency of emerging equity markets (e.g., Kawakatsu and Morey 1999; Lagoarde-Segot and Lucey 2008).

  2. A related issue is whether policy measures should be introduced in a specific sequence, with certain policies introduced early to speed up the process of equity market development (Bekaert et al. 2003; Karacadag et al. 2003). Since the sequence of policy initiatives in a particular context cannot be changed ex post, it is difficult to have more than a conjecture about it.

  3. This practice, which was known as badla trading, is much discussed in the literature and in policy circles. See, for example, Shah (1995) and Berkman and Eleswarapu (1998).

  4. In related research, Chelley-Steeley (2008) demonstrates that opening and closing market quality of participating stocks was improved by introduction of closing call auction at the London Stock Exchange. There is, in other words, prima facie evidence to suggest that the beneficial impact of such auctions may be generalizable across countries and across the specific nature of the auctions.

  5. For a rationale for the choice of NSE as the context of analysis, see Bhaumik et al. (2016).

  6. As the policy initiatives were undertaken in the 1990s and early 2000s and the last reform was initiated in 2010, we believe that such that a monthly sample period up till 2014:11 is sufficient to pick up the impact of these policies. Moreover, the reforms during this period were institutionalized in India with a focus on capital market development whereas later reforms were focused on mostly governance issues. In other words, our interest lies less in the VAR process itself and more on the marginal impact of the reforms.

  7. The model structure is, of course, compatible with a VAR as well as a VEC model. As we discuss later in the paper, we use appropriate tests to ascertain that variables are I(0). For details on panel VAR models, see Canova and Ciccarelli (2013).

  8. It is generally argued in the literature that since time-invariant individual-specific intercepts are correlated with the error term in dynamic panels (Nickell 1981), it is prudent to estimate panel VAR models using GMM (e.g., Goes 2016; Liu and Zhang 2016; Ouyang and Li 2018). While the magnitude of this bias is inversely related to T (Abrigo and Love 2015), it is unclear as to whether it is sufficiently small to warrant OLS estimation for our sample period, especially because in our unbalanced panel T may not be large for a number of stocks/companied included in our sample.

  9. As demonstrated by George et al. (1991), the adverse selection component of the bid-ask spread, while significant, accounts for only 8–13% of the quoted spread.

  10. More recent use of the CS spread can be found in Liu et al. (2016) and Rosati et al. (2017).

  11. In our base model, the dummy variable for each of these policy initiatives has the value 1 for all periods starting from the month during which policy was introduced. However, since investors may have adapted to these policy initiatives in advance, in anticipation of these initiatives, we have examined the robustness of the results by (1- and 2-month) lagged values of these dummy variables as well. The insights provided by our results were not affected by these changes, and the results themselves are available from the authors upon request.

  12. Even though compulsory rolling settlement (CRS) was introduced in a phased manner, we choose July 2001 as this was the first large scale implementation of CRS, with clear expectations about wider roll out of CRS over the foreseeable future.

  13. Note that the only major change not included in our list of policy initiatives is the ban on badla trading. Badla trading was initially banned in December 1994 and this decision was reversed in October 1995. It was banned again, for good, when rolling settlement on all BSE200 stocks and derivatives trading were introduced in July 2001. Given our sample period, and in the interest of model parsimony, we felt that we did not have to separately control for changes in the regulations regarding badla trading.

  14. The exact timing of the start of the financial crisis is not easy to determine; see https://www.stlouisfed.org/financial-crisis/full-timeline. However, based on the movement of the S&P 500 index, we chose to view September 2008 as the start of the financial crisis. This is also consistent with the first substantive government intervention in the form of the decision by the Federal Housing Finance Agency (FHEA) to put Fannie Mae and Freddie Mac under conservatorship, on September 7, significant regulatory intervention by the regulators in Washington Mutual Fund and Wachovia Corporation, and the now infamous decision by Lehman Brothers Holdings to file for Chapter 11 bankruptcy on September 15.

  15. The NSE was opened in November 1994, providing Indian investors with an order-driven electronic limit order book, reduced tick sizes, satellite technology with links to sites all over India, and improved settlement and clearing standards (see Shah and Thomas (2000)). By October 1995, NSE had surpassed the BSE, becoming the dominant equities market in India.

  16. Our large-cap sub-sample includes 81 firms that were part of the CNX100 index for the duration of the sample period. Our mid-cap sub-sample includes firms whose average market capitalization over the sample period remained between the 25th and 75th percentiles of the distribution of market capitalization of the 849 firms in the full sample. Finally, our sub-sample of small-cap firms includes 85 firms whose average market capitalization over the sample period was in the bottom decile of the aforementioned distribution. While these classifications are admittedly ad hoc, we feel that they serve the purpose of examining how the impact of the equity market reforms vary across large-cap, mid-cap and small-cap firms.

  17. The positive impact of RTGS on the spread for large-cap firms and similar impact of CRS and call auctions on the spread for small-cap firms are counterintuitive. However, the economic significance of these impacts is very small.

  18. This is broadly consistent with the literature in which it has been argued that the centralized clearing being an optimal arrangement for OTC trades especially when liquidity costs are high (e.g., Koeppl et al. (2012)).

  19. The confidence intervals shown on Fig. 2 are constructed using 100 Monte Carlo simulations.

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Acknowledgements

The authors would like to thank the UK-India Education Research Initiative (UKIERI) for financial support for the research, Samarjit Das for his insights into VAR models, and participants at various seminars and conferences. The authors gratefully acknowledge the insightful comments from the Managing Editor - Professor P.G. Babu and anonymous referee that helped to improve the revised version and remain responsible for all remaining errors.

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Authors and Affiliations

  1. Sheffield University Management School, University of Sheffield, Sheffield, UK

    Sumon Kumar Bhaumik

  2. IZA – Institute of Labor Economics, Bonn, Germany

    Sumon Kumar Bhaumik

  3. Global Labor Organization, Geneva, Switzerland

    Sumon Kumar Bhaumik

  4. Indian Institute of Management Calcutta, Calcutta, India

    Manisha Chakrabarty

  5. Southern Illinois University Edwardsville, Edwardsville, USA

    Ali M. Kutan

  6. Madras School of Economics, Gandhi Mandapam Road, Behind Anna Centenary Library, Chennai, 600025, India

    Ekta Selarka

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  1. Sumon Kumar Bhaumik
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Bhaumik, S.K., Chakrabarty, M., Kutan, A.M. et al. How Effective are Stock Market Reforms in Emerging Market Economies? Evidence from a Panel VAR Model of the Indian Stock Market. J. Quant. Econ. 19, 795–818 (2021). https://doi.org/10.1007/s40953-021-00253-z

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