| dc.description.abstract | This paper examines the performance of the traditional Black-Litterman (BL) model and its extensions, with a particular focus on Zhou (2009) modification, under different asset classifications and market conditions. The Black-Litterman model, which integrates investor views with market equilibrium, has constituted a fundamental element in the field of portfolio management. Zhou’s enhancement incorporates a Bayesian approach to address the limitations of the original model, thereby providing a more comprehensive framework for asset allocation. As demonstrated by Baker and Wurgler (2006), investor sentiment provides insight into the market. By incorporating investor sentiment, the model is refined through the use of sentimentadjusted tau values, allowing for a dynamic adjustment of portfolio weights based on market sentiment. Moreover, we put forth the suggestion of incorporating a historical average market capitalisation, which serves to enhance the reflection of market states. This helps to reduce the impact of biases resulting from short-term market fluctuations. Our contributions include a demonstration of the practical benefits of sentiment-adjusted models and an illustration of the effectiveness of the historical average market capitalisation weights approach. The empirical analysis assesses the out-of-sample performance of a range of portfolios, classified according to their momentum, size, book-to-market ratio and industry. This comprehensive evaluation offers new insights into the applicability and benefits of these enhanced models, demonstrating their potential for improving asset allocation strategies in different market environments. | en_US |