Financial characteristics and prediction on targets of M&A based on SOM- Hopfield neural network

In this paper, we apply self-organized mapping (SOM) and Hopfield neural network to cluster and predict the target of mergers and acquisitions. Financial characteristics of six sorts of targets are shown with low profitability, bad operation and good solvency very evidently by clustering of SOM. After calculating the means of variables of every sort, we build Hopfield network to predict the sort of targets and non-targets according to the means. Demonstration indicates Hopfield network can be used as prediction although accuracy of target selection is 80.69%, and non-target is 61.33 on the average. The reason is that financial data is not the only influence factors, many un-financial factor also have effect on the prediction.

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