Skip to main content
SpringerLink
Log in

The Effects of Overnight Events on Daytime Return: A Market Microstructure Analysis of Market Quality

  • Original Research
  • Published:
Asia-Pacific Financial Markets Aims and scope Submit manuscript

Abstract

This paper examines the trading and non-trading returns to diagnose the impact of market microstructure changes on market quality. The daily data of ten agricultural commodities traded on the National Commodity and Derivative Exchange (NCDEX) were used for the study. The data has been divided into three categories: year-wise, pre- and post-reform, pre-ban, and post-ban period. The study employs variance ratio analysis, and the results suggest high daytime and opening variances. A first-order autocorrelation detects the return predictability in the data series. A Value at Risk (VaR) and Expected Shortfall (ES) methods were employed to get more detail about the downside risk of the series. It suggested that daytime return has more risk compared to overnight return. Overall, this study suggests that market microstructure effects are visible in the Indian agricultural commodity market and hardly observe any improvement in the market quality. Since we reveal the impact of policy changes on market quality, the results will be useful for policymakers.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Data availability

Data can be downloaded from the NCDEX website.

References

  • Akbas, F., Boehmer, E., Jiang, C., & Koch, P. D. (2022). Overnight returns, daytime reversals, and future stock returns. Journal of Financial Economics, 145(3), 850–875.

    Article  Google Scholar 

  • Alexander, C., & Sarabia, J. M. (2011). Value-at-Risk Model. Risk. https://doi.org/10.2139/ssrn.1758548

    Article  Google Scholar 

  • Amihud, Y., & Mendelson, H. (1987). Trading Mechanisms and Stock Returns : An Empirical Investigation. Journal of Finance, 42(3), 533–553.

    Article  Google Scholar 

  • Anderson, R.M., Shik, K., Buhm, S., & Park, J. (2012). Sources of stock return autocorrelation. 1–63.

  • Artzner, P., Delbaen, F., Eber, J. M., & Heath, D. (1999). Coherent measures of risk. Mathematical Finance, 9(3), 203–228.

    Article  Google Scholar 

  • Avramov, D., Chordia, T., & Goyal, A. (2006). Liquidity and autocorrelations in individual stock returns. Journal of Finance, 61(5), 2365–2394. https://doi.org/10.1111/j.1540-6261.2006.01060.x

    Article  Google Scholar 

  • Bessembinder, H., & Hertzel, M. G. (1993). Return Autocorrelations around Nontrading Days. The Review of Financial Studies, 6(1), 155–189.

    Article  Google Scholar 

  • Bhaumik, S. K., Chakrabarty, M., Kutan, A. M., et al. (2021). How Effective Are Stock Market Reforms in Emerging Market Economies? Evidence from a Panel VAR Model of the Indian Stock Market. Journal of Quantitative Economics, 19, 795–818. https://doi.org/10.1007/s40953-021-00253-z

    Article  Google Scholar 

  • Bhuyan, B., Patra, S., & Bhuian, R. K. (2020). Market adaptability and evolving predictability of stock returns: An evidence from India. Asia-Pacific Financial Markets, 27, 605–619.

    Article  Google Scholar 

  • Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31, 307–327. https://doi.org/10.1109/TNN.2007.902962

    Article  Google Scholar 

  • Boudoukha, J., Feldmanb, R., Koganc, S., & Richardsond, M. (2019). Information, trading, and volatility : Evidence from firm-specific news. The Review of Financial Studies, 32(3), 992–1033. https://doi.org/10.1093/rfs/hhy083/5061375

    Article  Google Scholar 

  • Branch, B. S., & Ma, A. (2012). Overnight return the invisible hand behind the intraday return a retrospective. Journal of Applied Finance. https://doi.org/10.2139/ssrn.3259614

    Article  Google Scholar 

  • Campbell, J. Y., Grossman, S. J., & Wang, J. (1993). Trading volume and serial correlation in stock returns. The Quarterly Journal of Economics, 108(4), 905–939.

    Article  Google Scholar 

  • Cecchetti, S. G., & Lam, P. S. (1994). Variance-ratio tests: Small-sample properties with an application to international output data. Journal of Business and Economic Statistics, 12(2), 177–186. https://doi.org/10.1080/07350015.1994.10510006

    Article  Google Scholar 

  • Cerqueria, A. (2006). Autocorrelation in Daily Stock Returns. 4th International Conference of the PFN—Portuguese Finance Network. Available at http://www.fep.up.pt/conferencias/pfn2006/Conference Papers/439.

  • Chan, K. (1993). Imperfect information and cross-autocorrelation among stock prices. Journal of Finance, 48(4), 1211–1230.

    Article  Google Scholar 

  • Chordia, T., Roll, R., & Subrahmanyam, A. (2008). Liquidity and market efficiency. Journal of Financial Economics, 87(2), 249–268.

    Article  Google Scholar 

  • Fama, E. F. (1965a). Random walks in stock market prices. Financial Analysts Journal, 21, 55–59.

    Article  Google Scholar 

  • Fama, E. F. (1970). Session topic: Stock market price behavior session: A review of theory and empirical work. Journal of Finance, 25(2), 383–417.

    Article  Google Scholar 

  • Fama, E. F. (1991). Efficient capital markets: reply. Journal of Finance, 46(5), 1575–1617.

    Article  Google Scholar 

  • Fama, F. E. (1965b). The behavior of stock-market prices. The Journal of Business, 38(1), 34–105.

    Article  Google Scholar 

  • French, K. R., & Roll, R. (1986). Stock return variances. Journal of Financial Economics, 17(1), 5–26.

    Article  Google Scholar 

  • Frey, R., & McNeil, A. J. (2002). VaR and expected shortfall in portfolios of dependent credit risks: Conceptual and practical insights. Journal of Banking and Finance, 26(7), 1317–1334.

    Article  Google Scholar 

  • Gao, R., Zhang, X., Zhang, H., Zhao, Q., & Wang, Y. (2022). Forecasting the overnight return direction of stock market index combining global market indices: A multiple-branch deep learning approach. Expert Systems with Applications, 194(1), 1–18.

    Google Scholar 

  • Ham, H., Ryu, D., & Webb, R. I. (2022). The effects of overnight events on daytime trading sessions. International Review of Financial Analysis, 83, 102228.

    Article  Google Scholar 

  • Inani, S. K. (2018). Price discovery and efficiency of Indian agricultural commodity futures market: An empirical investigation. Journal of Quantitative Economics, 16(1), 129–154.

    Article  Google Scholar 

  • Jayawardena, N. I., Todorova, N., Li, B., & Su, J. J. (2016). Forecasting stock volatility using after-hour information: Evidence from the Australian stock exchange. Economic Modelling, 52, 592–608.

    Article  Google Scholar 

  • Jian, Z., Li, X., & Zhu, Z. (2020). Sequential forecasting of downside extreme risk during overnight and Daytime: Evidence from the Chinese Stock Market. Pacific Basin Finance Journal, 64(19), 101454. https://doi.org/10.1016/j.pacfin.2020.101454

    Article  Google Scholar 

  • Jiang, Y., Kellard, N., & Liu, X. (2020). Night trading and market quality: Evidence from Chinese and US precious metal futures markets. Journal of Futures Markets, 40(10), 1486–1507.

    Article  Google Scholar 

  • Krishna, K. L., & Meenakshi, J. V. (2022). Agricultural productivity growth and structural transformation in Rural India: Some recent evidence. Journal of Quantitative. Economics, 20(1), 277–302.

    Article  Google Scholar 

  • Kumar, D. (2020). Value-at-risk in the presence of structural breaks using unbiased extreme value volatility estimator. Journal of Quantitative Economics, 18, 587–610.

    Article  Google Scholar 

  • Kumar, R. (2021). Predicting wheat futures prices in India. Asia-Pacific Financial Markets, 28(1), 121–140.

    Article  Google Scholar 

  • Kupiec, P. H. (1995). Techniques for verifying the accuracy of risk measurement models. The Journal of Derivatives, 3(2), 73–84.

    Article  Google Scholar 

  • Liu, Q., Guo, H., & Wei, X. (2015). Negative overnight returns: China’s security markets. Procedia Computer Science, 55(12), 980–989.

    Article  Google Scholar 

  • Liu, Q., & Tse, Y. (2017). Overnight returns of stock indexes: Evidence from ETFs and futures. International Review of Economics and Finance, 48(11), 440–451.

    Article  Google Scholar 

  • Lockwood, L. J., & Linn, S. C. (1990). An examination of stock market return volatility during overnight and intraday periods, 1964–1989. Journal of Finance, 45(2), 591–601.

    Article  Google Scholar 

  • Lou, D., Polk, C., & Skouras, S. (2019). A tug of war: Overnight versus intraday expected returns. Journal of Financial Economics, 134(1), 192–213.

    Article  Google Scholar 

  • Madhavan, A. (2000). Market microstructure: A survey. Journal of Financial Markets. https://doi.org/10.1016/S1386-4181(00)00007-0

    Article  Google Scholar 

  • Masulis, R. W., & Ng, V. K. (1995). Overnight and daytime stock return-Dynamics on the London stock exchange: The impacts of “Big Bank” and the 1987 stock—market crash. Journal of Business & Economic Statistics, 13(4), 365–378.

    Google Scholar 

  • Meng, X., & Taylor, J. W. (2020). Estimating value-at-risk and expected shortfall using the intraday low and range data. European Journal of Operational Research, 280(1), 191–202.

    Article  Google Scholar 

  • Mighri, Z., & Jaziri, R. (2022). Long-memory, asymmetry and fat-tailed GARCH models in value-at-risk estimation: Empirical evidence from the global real estate markets. Journal of Quantitative Economics, 21, 41–97.

    Article  Google Scholar 

  • Oldfield, G. S., & Rogalski, R. J. (1980). A theory of common stock returns over trading and non-trading periods. Journal of Finance, 35(3), 729–736.

    Article  Google Scholar 

  • Oskay, B., Linssen, T., van Vliet, T., Bouman, B., & Pasma, N. (2017). Advancing financial risk estimates by incorporating GARCH into value at risk. Available at SSRN 3137587.

  • Patton, A. J., Ziegel, J. F., & Chen, R. (2019). Dynamic semiparametric models for expected shortfall (and Value-at-Risk). Journal of Econometrics, 211(2), 388–413.

    Article  Google Scholar 

  • Puffer, M. K. (1991). Private information and weekend volatility in the Tokyo and New York stock markets. Journal of Banking and Finance, 15(2), 407–423. https://doi.org/10.1016/0378-4266(91)90075-W

    Article  Google Scholar 

  • Sentana, E., & Wadhwani, S. (1992). Feedback traders and stock return autocorrelations : Evidence from a century of daily data. The Economic Journal, 102(411), 415–425.

    Article  Google Scholar 

  • Steeley, P. S. (2005). Explaining volatility and serial correlation in opening and closing returns a study of the FT-30 components. Global Finance Journal, 16(1), 1–15.

    Article  Google Scholar 

  • Stoll, H. R., & Whaley, R. E. (1990). Stock market structure and volatility. The Review of Financial Studies, 3(1), 37–71.

    Article  Google Scholar 

  • Tsiakas, I. (2008). Overnight information and stochastic volatility: A study of European and US stock exchanges. Journal of Banking and Finance, 32(2), 251–268.

    Article  Google Scholar 

  • Van Ness, B., Van Ness, R., & Yildiz, S. (2021). Private information in trades, R2, and large stock price movements. Journal of Banking and Finance, 131, 1–57.

    Google Scholar 

  • Zhu, X., Zhang, H., & Zhong, M. (2017). Volatility forecasting using high-frequency data: The role of after-hours information and leverage effects. Resources Policy, 54(2), 58–70.

    Article  Google Scholar 

Download references

Funding

The authors did not receive support from any organization for the submitted work.

Author information

Authors and Affiliations

  1. School of Humanities,Social Sciences and Management, National Institute of Technology Karnataka, Surathkal, Mangalore, Karnataka, India

    Sreekha Pullaykkodi & Rajesh H. Acharya

Authors
  1. Sreekha Pullaykkodi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rajesh H. Acharya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Sreekha Pullaykkodi or Rajesh H. Acharya.

Ethics declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

1.1 Year-wise Backtest Results for All Commodities

See Tables 20, 21, 22, 23, 24, 25, 26, 27, 28, 29.

Table 20 Backtest results for Jeera
Full size table
Table 21 Backtest results for Chana
Full size table
Table 22 Backtest results for Castor seed
Full size table
Table 23 Backtest results for Guar gum
Full size table
Table 24 Backtest results for Gur seed
Full size table
Table 25 Backtest results for Coriander
Full size table
Table 26 Backtest results for Pepper
Full size table
Table 27 Backtest results for RM seed
Full size table
Table 28 Backtest results for Soy oil
Full size table
Table 29 Backtest results for Soybean
Full size table

1.2 Backtest Results for Commodities in the Pre and Post-reform Periods

See Tables 30, 31.

Table 30 Back test result for the pre-reform period
Full size table
Table 31 Back test result for the post-reform period
Full size table

1.3 Back test Results for Commodities in the Pre-ban and Post-ban Periods

See Tables 32, 33.

Table 32 Back test result for the pre-ban period
Full size table
Table 33 Back test result for the post-ban period
Full size table

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pullaykkodi, S., Acharya, R.H. The Effects of Overnight Events on Daytime Return: A Market Microstructure Analysis of Market Quality. Asia-Pac Financ Markets (2023). https://doi.org/10.1007/s10690-023-09424-9

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10690-023-09424-9

Keywords

JEL Classification

Search

Navigation