Decision-making, risk and corporate governance: New dynamic models/algorithms and optimization for bankruptcy decisions

Bankruptcy and recovery prediction models are often used in auditing, large corporate transactions (M&A, strategic alliances, etc.), investment decision-making, and in the judicial setting where judges are the final arbiters in the bankruptcy process. However, all existing bankruptcy and recovery models are deficient primarily because: (1) the research methods were flawed; (2) the authors merely imposed rigid mathematical models on the bankruptcy framework; (3) the models do not follow an inter-disciplinary approach and do not consider many of the legal, behavioral/psychological and economic issues in financial distress; and (4) the models do not allow for optimization and simulation in order to derive the best conditions for minimizing financial distress – such optimization-based financial distress models can be particularly useful in negotiating debt restructurings, and in bankruptcy courts where judges have to decide the timing and structure of the bankruptcy process and the final result. This article introduces various dynamic models for bankruptcy and recovery decision-making, and develops a framework and foundation for further research in the use of dynamical systems and artificial intelligence in modeling bankruptcy decision-making and legal reasoning. The paper also illustrates the need for more collaboration among researchers in law, business and computer science.