arXiv 4 Feb 2019 · Econometrics · publishedJournal of Econometrics (2021) · 1 citations (OpenAlex)
arXiv:1902.01015 · PDF · DOI · OpenAlex · Extracted main text
This paper investigates asset allocation problems when returns are predictable. We introduce a market-timing Bayesian hierarchical (BH) approach that adopts heterogeneous time-varying coefficients driven by lagged fundamental characteristics. Our approach includes a joint estimation of conditional expected returns and covariance matrix and considers estimation risk for portfolio analysis. The hierarchical prior allows modeling different assets separately while sharing information across assets. We demonstrate the performance of the U.S. equity market. Though the Bayesian forecast is slightly biased, our BH approach outperforms most alternative methods in point and interval prediction. Our BH approach in sector investment for the recent twenty years delivers a 0.92% average monthly returns and a 0.32% significant Jensen`s alpha. We also find technology, energy, and manufacturing are important sectors in the past decade, and size, investment, and short-term reversal factors are heavily weighted. Finally, the stochastic discount factor constructed by our BH approach explains most anomalies.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Welch, I. and A. Goyal (2008) A comprehensive look at the empirical performance of equity premium prediction | 1.000 | 8 | 4 | 100% |
| 2 | Avramov, D. and T. Chordia (2006) Predicting stock returns | 1.000 | 8 | 3 | 100% |
| 3 | Gu, S., B. Kelly, and D. Xiu (2020) Empirical asset pricing via machine learning | 0.811 | 4 | 2 | 100% |
| 4 | Feng, G., J. He, X. He, and N. Polson (2020) Deep learning for predicting asset returns self | 0.737 | 3 | 2 | 100% |
| 5 | Kandel, S. and R. F. Stambaugh (1996) On the predictability of stock returns: an asset-allocation perspective | 0.737 | 3 | 2 | 100% |
| 6 | Polson, N. G. and B. V. Tew (2000) Bayesian portfolio selection: An empirical analysis of the S&P 500 index 1970–1996 | 0.737 | 3 | 2 | 100% |
| 7 | Amihud, Y (2002) Illiquidity and stock returns: cross-section and time-series effects | 0.644 | 2 | 2 | 100% |
| 8 | Avramov, D (2004) Stock return predictability and asset pricing models | 0.644 | 2 | 2 | 100% |
| 9 | Feng, G., S. Giglio, and D. Xiu (2020) Taming the factor zoo: A test of new factors self | 0.644 | 2 | 2 | 100% |
| 10 | Hou, K., C. Xue, and L. Zhang (2020) Replicating anomalies | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 33 scored citations.