摘要(英) |
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. |
參考文獻 |
[1] Allaj, E. (2013). The Black–Litterman model: A consistent estimation of the parameter
tau. Financial Markets and Portfolio Management, 27, 217-251.
[2] Black, F., & Litterman, R. (1991). Global asset allocation with equities, bonds, and currencies. Fixed Income Research, 2(15-28), 1-44.
[3] Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross-section of stock returns.
The journal of Finance, 61(4), 1645-1680.
[4] Baker, M., & Wurgler, J. (2007). Investor sentiment in the stock market. Journal of economic
perspectives, 21(2), 129-151.
[5] Bauder, D., Bodnar, T., Parolya, N., & Schmid, W. (2021). Bayesian mean–variance analysis: Optimal portfolio selection under parameter uncertainty. Quantitative Finance, 21(2),
221-242.
[6] Chan, L. K., Jegadeesh, N., & Lakonishok, J. (1996). Momentum strategies. The journal of
Finance, 51(5), 1681-1713.
[7] Chen, S. D., & Lim, A. E. (2020). A Generalized Black–Litterman Model. Operations
Research, 68(2), 381-410.
[8] Chou, P. (2024). The Black-Litterman Model: Some Unsolved Puzzles and Treatments.
Unpublished manuscript, April 16, 2024.
[9] Fama, E. F., & French, K. R. (2004). The capital asset pricing model: Theory and evidence.
Journal of economic perspectives, 18(3), 25-46.
[10] Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and
bonds. Journal of Financial Economics.
[11] Fama, E. F., & French, K. R. (1995). Size and book-to-market factors in earnings and
returns. The journal of finance, 50(1), 131-155.
[12] He, G., & Litterman, R. (1999). The intuition behind the Black-Litterman model portfolios. Goldman Sachs Investment Management Series.
[13] Meyer-Bullerdiek, F. (2021). Out-of-sample performance of the Black-Litterman model.
Journal of Finance and Investment Analysis, 10(2), 1-2.
[14] Kolm, P. N., Ritter, G., & Simonian, J. (2021). Black-Litterman and beyond: The Bayesian
paradigm in investment management. The Journal of Portfolio Management, to appear.
[15] Kolm, P. N., & Ritter, G. (2021). Factor investing with Black–Litterman–Bayes: Incorporating factor views and priors in portfolio construction. Journal of Portfolio Management,
47(2), 113-126.
[16] Tu, J., & Zhou, G. (2010). Incorporating economic objectives into Bayesian priors: Portfolio choice under parameter uncertainty. Journal of Financial and Quantitative Analysis,
45(4), 959-986.
[17] Zhou, G. (2009). Beyond Black-Litterman: letting the data speak. Journal of Portfolio
Management, 36(1), 36. |