Restructuring performance prediction with a rebalanced and clustered support vector machine

This paper discusses whether asset restructuring can improve firm performance over decades. Variation in the stock price or the financial ratio is used as the dependent variable of either short‐ or long‐term effectiveness to evaluate the variance both before and after asset restructuring. The result is varied. It is necessary to develop a foresight approach for the mixed situation. This work pioneers to forecast effectiveness of asset restructuring with a rebalanced and clustered support vector machine (RCS). The profitability variation 1 year before and after asset restructuring is used as the dependent variable. The current financial indicators of the year of asset restructuring are used as independent variables. Specially treated listed companies are used as research samples, as they frequently adopt asset restructuring. In modeling, the skew distribution of samples achieving and failing to achieve performance improvement with asset restructuring is handled with rebalancing. The similar experienced knowledge of asset restructuring to the current asset restructuring is filtered out with clustering. With the help from rebalancing and clustering, a support vector machine is constructed for prediction, together with other forecasting models of multivariate discriminant analysis, logistic regression, probit regression, and case‐based reasoning. These models' standalone modes are used as benchmarks. The empirical results demonstrate the applicability of the RCS for forecasting effectiveness of asset restructuring.