Modeling the relationship between corporate strategy and wealth creation using neural networks

In this paper, we hypothesize that there is a non-linear relationship between corporate strategy, short-run financial variables, and wealth creation measured as market value added (MVA), and use neural networking to model this relationship. The neural network model accurately categorized over 90% in the training set and nearly 93% of firms in the holdout test sample. Additional analysis revealed that strategy variables were particularly effective predictors of an upward trend in wealth creation whereas short-run financial variables were more effective in predicting a downward trend, or wealth destruction. Neural networks outperformed discriminant analysis in predictive ability in all analyses, suggesting the presence of non-linear effects. This research represents a first attempt to use neural networking to model the relationship between corporate strategy and wealth creation. Scope and purpose Strategy researchers are often interested in explaining the relationship between strategy choices and firm performance. Strategy choices are generally of two types: corporate and business. Business-level strategies address issues of competitive positioning and sources of differentiation (Porter ME. Competitive strategy. New York: The Free Press, 1980). Corporate-level strategies, on the other hand, are concerned with which businesses to be in and how to allocate resources among them (Porter ME. Harvard Business Review 1987: 43-59). In this study, we investigated the relationship among (1) patterns of decisions about organization scope and resource allocations (corporate strategy), (2) short-run financial health, which influences resource availability and stock market values, and (3) market value-added (MVA), a measure of wealth creation and destruction (Stewart GB. Journal of Applied Corporate Finance 1994; 7: 71-6). (C) 2000 Elsevier Science Ltd. All rights reserved.; In this paper, we hypothesize that there is a non-linear relationship between corporate strategy, short-run financial variables, and wealth creation measured as market value added (MVA), and use neural networking to model this relationship. The neural network model accurately categorized over 90% in the training set and nearly 93% of firms in the holdout test sample. Additional analysis revealed that strategy variables were particularly effective predictors of an upward trend in wealth creation whereas short-run financial variables were more effective in predicting a downward trend, or wealth destruction. Neural networks outperformed discriminant analysis in predictive ability in all analyses, suggesting the presence of non-linear effects. This research represents a first attempt to use neural networking to model the relationship between corporate strategy and wealth creation.