Enhancing Q-Learning for Optimal Asset Allocation

NeurIPS 1997 pp. 936-942

/neurips/1997/neuneier1997neurips-enhancing/

Abstract

This paper enhances the Q-Iearning algorithm for optimal asset alloca(cid:173) tion proposed in (Neuneier, 1996 [6]). The new formulation simplifies the approach by using only one value-function for many assets and al(cid:173) lows model-free policy-iteration. After testing the new algorithm on real data, the possibility of risk management within the framework of Markov decision problems is analyzed. The proposed methods allows the construction of a multi-period portfolio management system which takes into account transaction costs, the risk preferences of the investor, and several constraints on the allocation.

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Text

Neuneier. "Enhancing Q-Learning for Optimal Asset Allocation." Neural Information Processing Systems, 1997.

Markdown

[Neuneier. "Enhancing Q-Learning for Optimal Asset Allocation." Neural Information Processing Systems, 1997.](https://mlanthology.org/neurips/1997/neuneier1997neurips-enhancing/)

BibTeX

@inproceedings{neuneier1997neurips-enhancing,
  title     = {{Enhancing Q-Learning for Optimal Asset Allocation}},
  author    = {Neuneier, Ralph},
  booktitle = {Neural Information Processing Systems},
  year      = {1997},
  pages     = {936-942},
  url       = {https://mlanthology.org/neurips/1997/neuneier1997neurips-enhancing/}
}