---
title: Neural Networks in Merger Arbitrage
url: https://www.ml-quant.com/papers/ssrn/4802998/
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 4802998
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4802998
featured: 2024-04-24
citations: unknown
topic: Trading, Microstructure & Execution
---


# Neural Networks in Merger Arbitrage

The use of feed forward neural networks (FFNNs) in making merger arbitrage investment decisions proves effective, outperforming other models and increasing risk-standardized deal returns on average.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4802998
- Identifier: SSRN 4802998
- Released: 2024-04-22
- First featured: Quant Letter No. 46 (2024-04-24): https://www.ml-quant.com/issues/2024-04-24/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Trading, Microstructure & Execution

## Related

- [Advancing Algorithmic Trading: A Multi-Technique Enhancement of Deep Q-Network Models](https://www.ml-quant.com/papers/arxiv/2311.05743/): The research enhances a Deep Q-Network trading model using advanced methods, showing improved performance in automated trading and the potential of convolutional neural networks in trading systems.
- [Statistical Arbitrage in Rank Space](https://www.ml-quant.com/papers/arxiv/2410.06568/): Research indicates that ranking stocks by capitalization rather than company names improves statistical arbitrage performance, particularly when used with neural networks.
- [Multivariate Probabilistic Forecasting of Electricity Prices With Trading Applications](https://www.ml-quant.com/papers/ssrn/4527675/): Research improves probabilistic electricity price forecasting using artificial neural networks, achieving similar results to benchmarks but with lower computational cost.
- [Deep Learning Meets Queue-Reactive: A Framework for Realistic Limit Order Book Simulation](https://www.ml-quant.com/papers/arxiv/2501.08822/): The MDQR model, an advanced Queue-Reactive model, uses neural networks to understand complex market dependencies, making it useful for practical applications like strategy creation.
- [Neural Networks Can Detect Model-Free Static Arbitrage Strategies](https://www.ml-quant.com/papers/arxiv/2306.16422/): Neural networks can identify model-free static arbitrage opportunities in financial markets with many traded securities, offering tractability and effectiveness.
- [VWAP Execution with Signature-Enhanced Transformers: A Multi-Asset Learning Approach](https://www.ml-quant.com/papers/arxiv/2503.02680/): A new method for Volume Weighted Average Price (VWAP) execution has been suggested, using a single neural network across multiple assets, providing a more scalable and effective solution than traditional asset-specific models.