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Enhancing Portfolio Performance in Global Equity Allocation with a Forward-Looking Indicator

Subhransu Sekhar Mohanty
The Journal of Investing Winter 2018, 27 (4) 81-97; DOI: https://doi.org/10.3905/joi.2018.1.073
Subhransu Sekhar Mohanty
is director and professor of finance at St. Francis Institute of Management & Research in Mumbai, India, and President Emeritus of SMART International Holdings, Inc. in Delaware. ;
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Abstract

The Black–Litterman model provides a more reasonable platform for portfolio optimization and asset allocation, as compared to the traditional CAPM approach, by presenting an equilibrium state of the markets and only deviating from that equilibrium state with forward-looking strategic views. The Index of Economic Freedom (IEF) can be used as a handy tool for forming such strategic views on global markets. Ex-post performance analysis of portfolios covering both developed and developing equity markets constructed with CAPM, Black–Litterman equilibrium implied return, and Black–Litterman absolute view approaches shows that by smoothing expected return with changes in the IEF, significantly superior portfolio performance can be achieved at a lower risk. The Index of Economic Freedom contains superior information in terms of idiosyncratic country-specific risks, which the market seems to ignore or under price. This study has particular relevance to asset allocation strategy, portfolio optimization, and risk minimization in the context of global equity markets.

TOPICS: Portfolio construction, emerging, risk management, performance measurement

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The Journal of Investing: 27 (4)
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Enhancing Portfolio Performance in Global Equity Allocation with a Forward-Looking Indicator
Subhransu Sekhar Mohanty
The Journal of Investing Nov 2018, 27 (4) 81-97; DOI: 10.3905/joi.2018.1.073

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Enhancing Portfolio Performance in Global Equity Allocation with a Forward-Looking Indicator
Subhransu Sekhar Mohanty
The Journal of Investing Nov 2018, 27 (4) 81-97; DOI: 10.3905/joi.2018.1.073
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  • Article
    • Abstract
    • RESEARCH METHODOLOGY
    • DATA, THEIR LIMITATIONS AND ASSUMPTIONS
    • WORKING WITH THE MODELS (PORTFOLIO EXPECTED RISK–RETURN, PORTFOLIO OPTIMIZATION, ASSET ALLOCATION, PROBABILITY OF TARGET RETURN, AND BENCHMARK TRACKING)
    • EX-POST PERFORMANCE EVALUATION
    • SUMMARY AND CONCLUSION
    • APPENDIX A
    • APPENDIX B
    • ENDNOTES
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