
Exploring the Practicality of Merger Arbitrage
The study employs advanced machine learning techniques, including logistic regression, tree-based models, and neural networks, to evaluate the factors influencing deal success.
The study employs advanced machine learning techniques, including logistic regression, tree-based models, and neural networks, to evaluate the factors influencing deal success.
In “Optimising Cross-Asset Carry,” Nick Baltas extends the concept of the carry trade beyond currency markets to include commodities, equity indices, and government bonds. Baltas investigates how systematically capturing carry across multiple asset classes can enhance portfolio diversification and improve risk-adjusted returns.
Malcolm Baker and Serkan Savasoglu’s paper, “Limited Arbitrage in Mergers and Acquisitions II,” delves into the performance of merger arbitrage strategies and the factors that influence their returns.
The paper “Carry” by Koijen, Moskowitz, Pedersen, and Vrugt provides a comprehensive examination of the carry trade across various asset classes, establishing a unified framework that extends beyond traditional applications.
By classifying ARP strategies into divergence and convergence premia, Baltas provides a framework to understand the mechanics of crowding and its implications for investors.