An Evaluation of the Corporate Takeover Model Using Neural Networks

Neural networks have been found to be promising in financial prediction tasks like bankruptcy and loan defaults. Their use in the capital markets is relatively new, although they have been used with some success in picking undervalued stocks. Accurate prediction of corporate takeover targets results in high financial payoffs. Researchers have used statistical procedures like logistic regression with little success in predicting corporate takeover targets. We use neural networks that are capable of producing complex mapping functions to predict mergers. We develop several neural network models carefully controlling for overfitting. Our results indicate that although neural networks map the data very well, they do not predict merger targets significantly better than logistic regression. This strongly suggests that the financial models used to predict mergers are inadequate. Firms should approach the development of merger prediction models cautiously and identify other factors that are more likely to predict mergers. Attempts to apply better analysis techniques to existing models will most likely produce similar results.