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Stochastic Frontier Model Approach for Measuring Stock Market Efficiency with Different Distributions

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  • Md Zobaer Hasan
  • Anton Abdulbasah Kamil
  • Adli Mustafa
  • Md Azizul Baten

Abstract

The stock market is considered essential for economic growth and expected to contribute to improved productivity. An efficient pricing mechanism of the stock market can be a driving force for channeling savings into profitable investments and thus facilitating optimal allocation of capital. This study investigated the technical efficiency of selected groups of companies of Bangladesh Stock Market that is the Dhaka Stock Exchange (DSE) market, using the stochastic frontier production function approach. For this, the authors considered the Cobb-Douglas Stochastic frontier in which the technical inefficiency effects are defined by a model with two distributional assumptions. Truncated normal and half-normal distributions were used in the model and both time-variant and time-invariant inefficiency effects were estimated. The results reveal that technical efficiency decreased gradually over the reference period and that truncated normal distribution is preferable to half-normal distribution for technical inefficiency effects. The value of technical efficiency was high for the investment group and low for the bank group, as compared with other groups in the DSE market for both distributions in time- varying environment whereas it was high for the investment group but low for the ceramic group as compared with other groups in the DSE market for both distributions in time-invariant situation.

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  • Md Zobaer Hasan & Anton Abdulbasah Kamil & Adli Mustafa & Md Azizul Baten, 2012. "Stochastic Frontier Model Approach for Measuring Stock Market Efficiency with Different Distributions," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-9, May.
    • Handle: RePEc:plo:pone00:0037047
    DOI: 10.1371/journal.pone.0037047
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    2. Nuno Ferreira & Francisca Mendonça Souza & Adriano Mendonça Souza, 2014. "PSI-20 Portfolio Efficiency Analysis with SFA," International Journal of Finance, Insurance and Risk Management, International Journal of Finance, Insurance and Risk Management, vol. 4(3), pages 785-785.
    3. Zhujia Yin & Yantuan Yu & Jianhuan Huang, 2018. "Evaluation and evolution of bank efficiency considering heterogeneity technology: An empirical study from China," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-19, October.
    4. Ioana-Andreea Boboc & Mihai-Cristian Dinică, 2013. "An Algorithm for Testing the Efficient Market Hypothesis," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-11, October.
    5. Naimat U Khan & Sajjad Khan, 2016. "Weak Form of Efficient Market Hypothesis: Evidence from Pakistan," Business & Economic Review, Institute of Management Sciences, Peshawar, Pakistan, vol. 8(SE), pages 1-18, March.
    6. Viorica CHIRILA & Ciprian CHIRILA, 2015. "The Steel European Stock Market Efficiency," CES Working Papers, Centre for European Studies, Alexandru Ioan Cuza University, vol. 7(4), pages 873-880, December.
    7. Okotori, Tonprebofa & Ayunku, Peter, 2019. "An empirical investigation on efficient market test for the Nigerian stock exchange (NSE)," MPRA Paper 110516, University Library of Munich, Germany.
    8. Wasi Ul Hassan Shah & Gang Hao & Nan Zhu & Rizwana Yasmeen & Ihtsham Ul Haq Padda & Muhammad Abdul Kamal, 2022. "A cross-country efficiency and productivity evaluation of commercial banks in South Asia: A meta-frontier and Malmquist productivity index approach," PLOS ONE, Public Library of Science, vol. 17(4), pages 1-17, April.
    9. Roslah Arsad & Zaidi Isa, 2024. "Evaluating Company Efficiency in Malaysian Stock Markets: Insights from DEA and Super Efficiency Models," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 8(8), pages 4707-4718, August.

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