# Statistical Learning for Individualized Asset Allocation

arXiv: https://arxiv.org/abs/2201.07998 · HTML: https://mlsanity.com/paper/2201.07998/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 55 references that are in the map.
- First posted on arXiv 20 Jan 2022.

## Builds on (most cited first, 10 of 10)

- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [Double/Debiased/Neyman Machine Learning of Treatment Effects](https://mlsanity.com/paper/1701.08687/) (2017)
- [Estimating Dynamic Treatment Regimes in Mobile Health Using V-learning](https://mlsanity.com/paper/1611.03531/) (2016)
- [Nonparametric methods for doubly robust estimation of continuous treatment effects](https://mlsanity.com/paper/1507.00747/) (2015)
- [Off-Policy Estimation of Long-Term Average Outcomes with Applications to Mobile Health](https://mlsanity.com/paper/1912.13088/) (2019)
- [Policy Learning with Observational Data](https://mlsanity.com/paper/1702.02896/) (2017)
- [Efficient Implementations of the Generalized Lasso Dual Path Algorithm](https://mlsanity.com/paper/1405.3222/) (2014)
- [A Parsimonious Personalized Dose Finding Model via Dimension Reduction](https://mlsanity.com/paper/1802.06156/) (2018)
- [Robust Learning for Optimal Treatment Decision with NP-Dimensionality](https://mlsanity.com/paper/1510.04378/) (2015)
- [Kernel Assisted Learning for Personalized Dose Finding](https://mlsanity.com/paper/2007.09811/) (2020)

## Abstract (arXiv)

We establish a high-dimensional statistical learning framework for individualized asset allocation. Our proposed methodology addresses continuous-action decision-making with a large number of characteristics. We develop a discretization approach to model the effect of continuous actions and allow the discretization frequency to be large and diverge with the number of observations. The value function of continuous-action is estimated using penalized regression with our proposed generalized penalties that are imposed on linear transformations of the model coefficients. We show that our proposed Discretization and Regression with generalized fOlded concaVe penalty on Effect discontinuity (DROVE) approach enjoys desirable theoretical properties and allows for statistical inference of the optimal value associated with optimal decision-making. Empirically, the proposed framework is exercised with the Health and Retirement Study data in finding individualized optimal asset allocation. The results show that our individualized optimal strategy improves the population financial well-being.