套利
对冲基金
交易策略
人工神经网络
投资策略
投资(军事)
计量经济学
预测能力
经济
计算机科学
金融经济学
机器学习
财务
市场流动性
哲学
认识论
政治
政治学
法学
作者
Daniel A. Braun,Yue Han,Heng Wang
标识
DOI:10.1016/j.frl.2023.104391
摘要
This study examines the effectiveness and applicability of a trending machine learning algorithm, the feed forward neural networks (FFNNs) in making merger arbitrage investment decisions. Using a sample of attempted takeovers, 24 deal-specific, target-specific, and macroeconomic factors serve as input variables for the proposed FFNNs model. The resulting failure probabilities are utilized by a simulated hedge fund in evaluating merger arbitrage opportunities. By comparing other funds employing simplistic or commonplace predictive models and investment decision rules, our findings reveal the power of machine learning in takeover failure prediction and the use of FFNN can increase risk-standardized deal returns on average.
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