Artificial Intelligence and Machine Learning in Corporate Finance
This paper reviews applications, methodologies, and challenges of AI and machine learning in corporate finance research and practice.
What it examines
This chapter overviews how artificial intelligence and machine learning transform corporate finance. It outlines diverse methods like supervised, unsupervised, reinforcement, deep learning, and causal inference techniques. With focus on data analysis, processing unstructured data, and text mining, the study aims to expand finance research and practice applications.
What it concludes
The study shows artificial intelligence and machine learning can enhance M&A target selection, default and fraud prediction, and portfolio management in corporate finance. It recommends further research into human-machine collaboration, regulatory impacts, and ethical challenges. Applications include credit scoring, risk management, and automated valuation, though limitations in data biases remain.
Evidence objects
The study demonstrates how artificial intelligence transforms corporate finance by enhancing prediction accuracy, uncovering hidden patterns, and analyzing unstructured data with models like random forests, neural networks, and causal forests.
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The chapter integrates deep learning with explainable AI to demystify models, combining text, audio, video, and numerical data for insights into investor sentiment, financial distress, portfolio construction, and corporate governance.
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Employing ensemble learning, SHAP values, and double machine learning, the research boosts performance while noting challenges like data manipulation, fake citations, and quality compromises, urging human-machine collaboration and market impact.
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The paper presents an original synthesis of established corporate finance methods with innovative applications of $AI$ and $machine learning$. It introduces fresh perspectives on analyzing unstructured data, causal inference, and related issues. Though primarily building on existing ideas, its thoughtful integration offers readers valuable insights and compelling practical relevance overall.
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Raw abstract and provenance
- This chapter examines how artificial intelligence and machine learning are utilized in corporate finance research. W e provide an overview of the applications and identify …
Source row: 231 · abstract type: snippet