---
title: Limited Partners versus Unlimited Machines; Artificial Intelligence and the Performance of Private Equity Funds
url: https://www.ml-quant.com/papers/ssrn/4490991/
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 4490991
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4490991
featured: 2023-06-28
citations: 1
topic: ML & AI Methods
---


# Limited Partners versus Unlimited Machines; Artificial Intelligence and the Performance of Private Equity Funds

Private equity fund performance is not influenced by quantitative information, but machine learning can predict future performance using qualitative information.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4490991
- Identifier: SSRN 4490991
- Released: 2023-06-26
- First featured: Quant Letter No. 5 (2023-06-28): https://www.ml-quant.com/issues/2023-06-28/
- Citations (Semantic Scholar): 1
- Published in: not yet
- Topic: ML & AI Methods

## Related

- [Artificial Intelligence & Private Equity Fund Perf.](https://www.ml-quant.com/papers/ssrn/4684754/): Private equity fund performance doesn't correlate with quantitative data like past performance, but machine learning can predict future performance using qualitative data.
- [Breaking Network Barriers VC](https://www.ml-quant.com/papers/ssrn/4941953/): The article notes that US Venture Capital activity is increasingly using digital data and machine learning to guide investment decisions, leading to more investments outside major hubs.
- [AIPowered Replication of PE Funds](https://www.ml-quant.com/papers/ssrn/5199100/): A new framework is introduced for replicating private equity performance using liquid AI-enhanced strategies, offering a liquid, scalable solution that aligns closely with traditional quarterly PE benchmarks.
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://www.ml-quant.com/papers/arxiv/2312.00752/): Sequence Modeling: Mamba, a neural network architecture that doesn't use attention or MLP blocks, provides faster inference and better performance in language, audio, and genomics than Transformers.
- [Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality](https://www.ml-quant.com/papers/arxiv/2405.21060/): The research identifies a link between state-space models and Transformers in deep learning, leading to the creation of a faster language modeling architecture, Mamba-2.
- [Octo: An Open-Source Generalist Robot Policy](https://www.ml-quant.com/papers/arxiv/2405.12213/): Octo is a large transformer-based policy for robotic manipulation, trained on a vast dataset, that can be instructed via language or images and adapted to new domains.