Tree-Based Conditional Portfolio Sorts: The Relation between Past and Future Stock Returns
By Benjamin Moritz, Tom Zimmermann. 2016
103 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Finance, Management Science and Operations Research
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Markus Pelger
- D. Xiu
- Luyang Chen
- Jason Zhu
- Shihao Gu
- Bryan T. Kelly
- Svetlana Bryzgalova
- Yacine Aït-Sahalia
In Finance
In Management Science and Operations Research
- Jason Zhu
- Markus Pelger
- Luyang Chen
- Svetlana Bryzgalova
- Yu-Long Zhou
- Qian Xu
- Wei-Ke Zhang
- Bryan T. Kelly
Citations in the corpus
- Long short‐term memory networks for CSI300 volatility prediction with Baidu search volume (2018, background): “with the notion of market eciency. In the past years, initial evidence has been established that machine learning techniques are capable of identifying (non-linear) structures in nancial market data [10, 13, 2]. Petersen, A et.al(2017) applied LSTM networks to all S&P 500 constituents from 1992 ”
- Shrinking the Cross Section∗ (2017, unknown): “Many papers (e.g., Freyberger et al. (2017); Huerta et al. (2013); Moritz and Zimmermann (2016); Tsai et al. (2011), with the exception of Feng et al. (2017)) focus on estimating risk premia, i.e., the extent to which a stock characteristic is associated with variation in expected returns.”
- Deep Learning in Asset Pricing (2019, background): “…work includes Sirignano, Sadhwani, and Giesecke (2020) who estimate mortgage prepayments, delinquencies, and foreclosures with deep neural networks, Moritz and Zimmerman (2016) who apply tree-based models to portfolio sorting and Heaton, Polson, and Witte (2017) who automate portfolio selection…”
- Forest Through the Trees: Building Cross-Sections of Stock Returns (2020, background): “Moritz and Zimmerman (2016), Gu, Kelly, and Xiu (2020b), and Rossi (2018) use decision trees for estimating conditional moments of stock returns in the context of a prediction problem.”
- Empirical Asset Pricing via Machine Learning (2018, background): “Moritz and Zimmermann (2016) apply tree-based models to portfolio sorting.”