In practice, mean-variance optimization results in non-intuitive and extreme portfolio allocations, which are highly sensitive to variations in the inputs. Generally, efficient frontiers based on historical data lead to highly concentrated portfolios. The Black-Litterman approach overcomes, or at least mitigates, these problems to a large extent. The highlight of this approach is that it enables us to incorporate investment views (which are subjective in nature) and assigns confidence levels to the views at the modeler’s discretion. These aspects make the Black-Litterman model a strong quantitative tool that provides an ideal framework for strategic/tactical asset allocation. The present study is an endeavor in this direction. It considers the weak and strong aspects of both models and demonstrates how their optimization procedures are put into practice in the context of Indian equity markets. To represent the Indian equity markets, the study considered the Bombay stock exchange (BSE) published sectoral indices for tactical asset allocation. In different trials, the performance of the two approaches is compared empirically. The study found that the Black-Litterman efficient portfolios achieve a significantly better return-to-risk performance than the mean-variance optimal approach/strategy.
Key words: Black-Litterman model, reverse optimization, tactical asset allocation, implied equilibrium return, portfolio optimization, tracking error volatility.
Abbreviations: BSE, Bombay stock exchange; ER, expected returns; CAPM, capital asset pricing model; CD, consumer durable; CG, consumer good; FMCG, fast moving consumer goods; HC, healthcare; IT, information technology; GICS, global industry classification standard.
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