---
title: Portfolio Diversification Including Art as an Alternative Asset
url: https://www.ml-quant.com/papers/ssrn/4617318/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 4617318
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4617318
featured: 2023-11-02
citations: 1
topic: Portfolio & Allocation
---


# Portfolio Diversification Including Art as an Alternative Asset

Art and collectibles can act as alternative assets for portfolio diversification, with art performing well compared to standard investments and showing a unique seasonal pattern in returns.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4617318
- Identifier: SSRN 4617318
- Released: 2023-10-30
- First featured: Quant Letter No. 24 (2023-11-02): https://www.ml-quant.com/issues/2023-11-02/
- Citations (Semantic Scholar): 1
- Published in: not yet
- Topic: Portfolio & Allocation

## Related

- [A General Framework on Enhancing Portfolio Management with Reinforcement Learning](https://www.ml-quant.com/papers/arxiv/1911.11880/): A reinforcement learning framework for portfolio management is introduced, allowing for continuous asset weights, short selling, and decision-making, with three reinforcement learning algorithms compared for effectiveness.
- [Maximizing Portfolio Predictability with Machine Learning](https://www.ml-quant.com/papers/arxiv/2311.01985/): Portfolio Predictability Maximization using ML: A stock portfolio called the maximally predictable portfolio (MPP), created using machine learning and a Kelly criterion strategy, consistently performs better than the benchmark.
- [Topological Portfolio Selection and Optimization](https://www.ml-quant.com/papers/arxiv/2310.14881/): The paper suggests the use of Statistically Robust Information Filtering Network (SR-IFN) to minimize noise in empirical covariance estimation, improving portfolio optimization by aiding in the selection of diversified, high-performing portfolios.
- [A Comparative Study of Portfolio Optimization Methods for the Indian Stock Market](https://www.ml-quant.com/papers/arxiv/2310.14748/): The chapter evaluates and compares the MVP, HRP, and HERC portfolio optimization methods using data from 15 sectors of the Indian stock market.
- [Dynamic Realized Minimum Variance Portfolio Models](https://www.ml-quant.com/papers/arxiv/2310.13511/): The article introduces a new model for predicting future portfolios using high-frequency financial data, which minimizes variance using the least absolute shrinkage and selection operator.
- [Withdrawal success optimization](https://www.ml-quant.com/papers/arxiv/2311.06665/): The likelihood of completing a specific investment and withdrawal schedule is maximized using adjustable portfolio weight functions, showing significant improvements when optimal weights are used instead of constant ones.
