Philippe Goulet Coulombe, Maximilian Goebel
arXiv 8 Jun 2023 · Econometrics · 3 citations (OpenAlex)
arXiv:2306.05568 · PDF · DOI · OpenAlex · Extracted main text
When it comes to stock returns, any form of predictability can bolster risk-adjusted profitability. We develop a collaborative machine learning algorithm that optimizes portfolio weights so that the resulting synthetic security is maximally predictable. Precisely, we introduce MACE, a multivariate extension of Alternating Conditional Expectations that achieves the aforementioned goal by wielding a Random Forest on one side of the equation, and a constrained Ridge Regression on the other. There are two key improvements with respect to Lo and MacKinlay's original maximally predictable portfolio approach. First, it accommodates for any (nonlinear) forecasting algorithm and predictor set. Second, it handles large portfolios. We conduct exercises at the daily and monthly frequency and report significant increases in predictability and profitability using very little conditioning information. Interestingly, predictability is found in bad as well as good times, and MACE successfully navigates the debacle of 2022.
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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 Goyal, A (2007) A Comprehensive Look at The Empirical Performance of Equity Premium Prediction | 1.000 | 6 | 3 | 100% |
| 2 | Gu, S., Kelly, B., and Xiu, D (2020) Empirical Asset Pricing via Machine Learning | 0.974 | 13 | 4 | 92% |
| 3 | Filippou, I., Rapach, D. E., and Thimsen, C (2021) Cryptocurrency Return Predictability: A Machine-Learning Analysis | 0.843 | 3 | 3 | 100% |
| 4 | Krauss, C., Do, X. A., and Huck, N (2017) Deep Neural Networks, Gradient-Boosted Trees, Random Forests: Statistical Arbitrage on the S&P 500 | 0.843 | 3 | 3 | 100% |
| 5 | Fama, E. F. and French, K. R (2015) A Five-Factor Asset Pricing Model | 0.737 | 4 | 3 | 50% |
| 6 | Cong, L., Tang, K., Wang, J., and Zhang, Y (2021) AlphaPortfolio: Direct Construction Through Deep Reinforcement Learning and Interpretable AI | 0.737 | 4 | 2 | 75% |
| 7 | d'Aspremont, A (2011) Identifying small mean-reverting portfolios | 0.737 | 3 | 2 | 100% |
| 8 | Breiman, L. and Friedman, J. H (1985) Estimating Optimal Transformations for Multiple Regression and Correlation | 0.737 | 3 | 2 | 100% |
| 9 | Goulet Coulombe, P (2022) A neural phillips curve and a deep output gap | 0.737 | 3 | 2 | 100% |
| 10 | Goulet Coulombe, P., Leroux, M., Stevanovic, D., and Surprenant, S (2021) Macroeconomic data transformations matter | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 92 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Non-linear dimension reduction in factor-augmented vector autoregressions | 0.000 | 1 | 1 |