Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Informational Efficiency of National Stock Exchange (NSE), India:A Comparison with Seven Selected Markets


Affiliations
1 Vinod Gupta School of Management (VGSOM), Indian Institute of Technology Kharagpur, Kharagpur-721302, West Bengal, India
     

   Subscribe/Renew Journal


The efficiency of a stock market can be illustrated either by the condition such as investment and behavioral patterns, and bubbles, or assessed by exclusive measures, such as the Hurst exponent. The Hurst exponent methodology brings about a measure for long-term memory and probability of the future values based on past information, without making assumptions about stationarity. In the current study, the Hurst exponent has been estimated for past 10 years’ daily values of eight stock indices and the corresponding financial sector index series. The markets chosen include those from India, Brazil, China, Hong Kong, Japan, Singapore, United Kingdom (UK) and the United States of America (USA). These financial sector indices were considered for this study as they had the highest weight percentage and number of constituents in all the exchanges. Empirical analyses showed that the Hurst exponent values of the series varied for different stock exchanges and corresponding financial sector index, but were in the range of 0.5, confirming the market to be efficient. A comparison of India’s National Stock Exchange (NSE) with selected markets of the seven other countries revealed that India’s NSE had one of the efficient indices with regard to its peers. It was also found that most of the indices, though locally persistent, went through phases involving herding behaviors of market participants.

Keywords

De-Trended Fluctuation Analysis (DFA), Fractal Dimension, Hurst Exponent, National Stock Exchange (NSE), Range to Standard Deviation Ratio.
User
Subscription Login to verify subscription
Notifications
Font Size

  • Aloui, C., & Hamida Hela, B. (2011). Hurst’s exponent behaviour, weak-form stock market efficiency and financial liberalization: the Tunisian case. Economics Bulletin, 31(1), 830-843.
  • Baciu, O. (2014). Ranking capital markets efficiency: the case of twenty European stock markets. Journal of Applied Quantitative Methods, 9(3), 24-33.
  • Barunik, J., & Kristoufek, L. (2010). On Hurst exponent estimation under heavy-tailed distributions. Physica A: Statistical Mechanics and its Applications, 389(18), 3844-3855.
  • Bassler, K. E., Gunaratne, G. H., & McCauley, J. L. (2006). Markov Processes, Hurst Exponents, and Nonlinear Diffusion Equations: With application to Finance. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.627.9704 &rep= rep1&type=pdf
  • Cajueiro, D. O., & Tabak, B. M. (2004). The Hurst exponent over time: Testing the assertion that emerging markets are becoming more efficient. Physica A: Statistical Mechanics and its Applications, 336(3), 521-537.
  • Da Silva, S., Matsushita, R., Gleria, I., & Figueiredo, A. (2007). Hurst exponents, power laws, and efficiency in the Brazilian foreign exchange market. Economics Bulletin, 7(1), 1-11.
  • Danilenko, S. (2009). Hurst Analysis of Baltic Sector Indices. Applied Stochastic Models and Data Analysis (ASMDA-2009), Vilnius, 329-333.
  • Grech, D., & Mazur, Z. (2004). Can one make any crash prediction in finance using the local Hurst exponent idea? Physica A: Statistical Mechanics and its Applications, 336(1), 133-145.
  • Hurst, H. E. (1951). Long-term storage capacity of reservoirs. Transactions of the American Societyof Civil Engineers, 116, 770-808.
  • Krištoufek, L., & Vošvrda, M. (2012). Capital markets efficiency: Fractal dimension, hurst exponent and entropy. Politickáekonomie, 2012(2), 208-221.
  • Kyaw, N. A., Los, C. A., & Zong, S. (2006). Persistence characteristics of Latin American financial markets. Journal of Multinational Financial Management, 16(3), 269-290.
  • Lipka, J., & Los, C. (2002). Persistence characteristics of European stock indexes (Kent State University Working Paper). Kent, OH: Kent State University.
  • Mahalingam, G., & Selvam, M. (2013). Fractal Analysis in the Indian Stock Market with Special Reference to CNX 500 Index Returns. Available at SSRN 2325334.
  • McCauley, J. L., Gunaratne, G. H., & Bassler, K. E. (2007). Hurst exponents, Markov processes, and fractional Brownian motion. Physica A: Statistical Mechanics and its Applications, 379(1), 1-9.
  • Mitra, S. K. (2012). Is Hurst Exponent Value Useful in Forecasting Financial Time Series? Asian Social Science, 8(8), 111.
  • Peng, C. K., Buldyrev, S. V., Havlin, S., Simons, M., Stanley, H. E., & Goldberger, A. L. (1994). Mosaic organization of DNA nucleotides. Physical Review, 49(2), 1685.
  • Peters, E. E. (1996). Chaos and Order in the Capital Markets: A New View of Cycles, Prices, and Market Volatility (Vol. 1). John Wiley & Sons.
  • Selvam, M., & Jayapal, G. (2011). Fractal Structure Analysis in the Indian Stock Market.
  • Sensoy, A. (2013). Efficiency of Stock Markets and Exchange Rates: Emerging vs. Developed Countries (No. 11).
  • Singh, J. P., & Prabakaran, S. (2008). On the Distribution of Returns & Memory Effects in Indian Capital Markets, International Research Journal of Finance and Economics, 14, 165-176.

Abstract Views: 194

PDF Views: 0




  • Informational Efficiency of National Stock Exchange (NSE), India:A Comparison with Seven Selected Markets

Abstract Views: 194  |  PDF Views: 0

Authors

Manoj Kumar Choudhury
Vinod Gupta School of Management (VGSOM), Indian Institute of Technology Kharagpur, Kharagpur-721302, West Bengal, India
Prabina Rajib
Vinod Gupta School of Management (VGSOM), Indian Institute of Technology Kharagpur, Kharagpur-721302, West Bengal, India

Abstract


The efficiency of a stock market can be illustrated either by the condition such as investment and behavioral patterns, and bubbles, or assessed by exclusive measures, such as the Hurst exponent. The Hurst exponent methodology brings about a measure for long-term memory and probability of the future values based on past information, without making assumptions about stationarity. In the current study, the Hurst exponent has been estimated for past 10 years’ daily values of eight stock indices and the corresponding financial sector index series. The markets chosen include those from India, Brazil, China, Hong Kong, Japan, Singapore, United Kingdom (UK) and the United States of America (USA). These financial sector indices were considered for this study as they had the highest weight percentage and number of constituents in all the exchanges. Empirical analyses showed that the Hurst exponent values of the series varied for different stock exchanges and corresponding financial sector index, but were in the range of 0.5, confirming the market to be efficient. A comparison of India’s National Stock Exchange (NSE) with selected markets of the seven other countries revealed that India’s NSE had one of the efficient indices with regard to its peers. It was also found that most of the indices, though locally persistent, went through phases involving herding behaviors of market participants.

Keywords


De-Trended Fluctuation Analysis (DFA), Fractal Dimension, Hurst Exponent, National Stock Exchange (NSE), Range to Standard Deviation Ratio.

References