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Exploring new frontiers in indexing strategies: an optimization-based risk-efficient solution

Author

Listed:
  • Reema Monga

    (Delhi Technological University)

  • Deepti Aggrawal

    (Delhi Technological University)

  • Jagvinder Singh

    (Delhi Technological University)

Abstract

This paper examines various risk-efficient solutions that enable investors to optimize their risk-return profile in comparison to the traditional market-capitalization index. Investment practitioners and other equity analysts considered cap-weighted market indices as neither efficient nor diversified; due to some notable drawbacks such as overconcentration of large-cap, price noise, massive drawdown, absence of an auto-corrective mechanism of mean reversion; and others. Therefore, the purpose of the study is to explore the diverse range of risk-efficient strategies that improve the quality of the index constitution. Using the extensive sample of the S&P BSE 500 index, the study examines the performance, consistency, and robustness of optimization-based risk-efficient solutions, including; Efficient minimum variance, Diversified risk-weighted, Maximum decorrelation, Maximum deconcentration, Efficient maximum Sharpe ratio, and Diversified Multi-Strategy for the period April 1, 2004, to March 31, 2020. The result of the study presents evidence of diversification enhancement and extensive outperformance for these risk-efficient solutions.

Suggested Citation

  • Reema Monga & Deepti Aggrawal & Jagvinder Singh, 2022. "Exploring new frontiers in indexing strategies: an optimization-based risk-efficient solution," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(2), pages 853-865, June.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:2:d:10.1007_s13198-021-01138-3
    DOI: 10.1007/s13198-021-01138-3
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    References listed on IDEAS

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