---
title: Corporate Financial Asset Allocation Impact
url: https://www.ml-quant.com/papers/ssrn/5134335/
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 5134335
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5134335
featured: 2025-02-26
citations: unknown
topic: Portfolio & Allocation
---


# Corporate Financial Asset Allocation Impact

The research reveals a U-shaped relationship between corporate financial asset allocation and audit fees, influenced by financing constraints and moderated by ownership concentration.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5134335
- Identifier: SSRN 5134335
- Released: 2025-02-12
- First featured: Quant Letter No. 86 (2025-02-26): https://www.ml-quant.com/issues/2025-02-26/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Portfolio & Allocation

## Related

- [A Cholesky decomposition-based asset selection heuristic for sparse tangent portfolio optimization](https://www.ml-quant.com/papers/arxiv/2502.11701/): A new asset selection method for mean-variance portfolios has been proposed, allowing for quicker optimization and construction of portfolios with fewer assets.
- [The Role of Deep Learning in Financial Asset Management: A Systematic Review](https://www.ml-quant.com/papers/arxiv/2503.01591/): A review of deep learning in financial asset management identifies trends like explainable AI and deep reinforcement learning, suggesting deep learning can enhance portfolio performance and price forecasting.
- [Dynamically Optimal Portfolios for Monotone Mean-Variance Preferences](https://www.ml-quant.com/papers/arxiv/2503.08272/): The paper describes the optimal dynamic portfolio choice for the Monotone Mean-Variance utility in asset price models with independent returns, with minimal assumptions.
- [Tactical asset allocation with macroeconomic regime detection](https://www.ml-quant.com/papers/arxiv/2503.11499/): The study introduces a new model that applies machine learning for regime modeling in tactical asset allocation, showing significant enhancements in portfolio performance.
- [Bayesian Optimization for CVaR-based portfolio optimization](https://www.ml-quant.com/papers/arxiv/2503.17737/): The paper presents new Bayesian Optimization algorithms for portfolio allocation, reducing risk and meeting performance goals, proven through practical examples.
- [Clearing Sections of Lattice Liability Networks](https://www.ml-quant.com/papers/arxiv/2503.17836/): A novel mathematical framework has been created to improve comprehension of complex financial networks, offering a unified basis for analyzing systemic risk, resource allocation, and network stability.