---
title: Optimizing Portfolio Allocation
url: https://www.ml-quant.com/papers/ssrn/4534131/
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 4534131
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4534131
featured: 2023-08-09
citations: unknown
topic: Portfolio & Allocation
---


# Optimizing Portfolio Allocation

The BlackLitterman model, BLEnd2End, uses deep learning to optimize portfolio allocation, outperforming mean-variance benchmarks and other traditional strategies.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4534131
- Identifier: SSRN 4534131
- Released: 2023-08-07
- First featured: Quant Letter No. 11 (2023-08-09): https://www.ml-quant.com/issues/2023-08-09/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Portfolio & Allocation

## Related

- [Stock Movement Prediction and Portfolio Optimization](https://www.ml-quant.com/papers/ssrn/5234375/): The study introduces a deep learning framework, MR2TNet, to predict NIFTY50 index stock price movements by considering multiple information sources.
- [From Headlines to Holdings: Deep Learning for Smarter Portfolio Decisions](https://www.ml-quant.com/papers/arxiv/2509.24144/): A new deep learning framework for portfolio optimization, which combines LSTM networks, Graph Attention Networks, and financial news sentiment analysis, outperforms traditional benchmarks by directly learning portfolio weights, resulting in higher cumulative returns and Sharpe ratios.
- [Rebalanced Portfolio Optimization](https://www.ml-quant.com/papers/ssrn/4526656/): Regular intraperiod portfolio rebalancing strategies offer a unique solution to portfolio optimization problems without requiring utility or risk tradeoffs.
- [Portfolio selection via topological data analysis](https://www.ml-quant.com/papers/arxiv/2308.07944/): A two-stage method using Topological Data Analysis for building a stock investment portfolio consistently outperforms traditional methods across various time periods.
- [Memory Effects, Multiple Time Scales and Local Stability in Langevin Models of the S&P500 Market Correlation](https://www.ml-quant.com/papers/arxiv/2307.12744/): The study highlights the importance of considering the memory effect in market correlations for improving the accuracy of forecasting models and aiding in portfolio selection.
- [Transfer Learning for Portfolio Optimization](https://www.ml-quant.com/papers/arxiv/2307.13546/): The study presents the concept of transfer risk in transfer learning techniques for financial portfolio optimization, showing its potential to improve the efficiency of the transfer learning approach.