We illustrate how a comparatively new technique, a Tabu search variable selection model [Drezner, Marcoulides and Salhi (1999)], can be applied efficiently within finance when the researcher must select a subset of variables from among the whole set of explanatory variables under consideration. Several types of problems in finance, including corporate and personal bankruptcy prediction, mortgage and credit scoring, and the selection of variables for the Arbitrage Pricing Model, require the researcher to select a subset of variables from a larger set. In order to demonstrate the usefulness of the Tabu search variable selection model, we: (1) illustrate its efficiency in comparison to the main alternative search procedures, such as stepwise regression and the Maximum R2procedure, and (2) show how a version of the Tabu search procedure may be implemented when attempting to predict corporate bankruptcy. We accomplish (2) by indicating that a Tabu Search procedure increases the predictability of corporate bankruptcy by up to 10 percentage points in comparison to Altman's (1968) Z-Score model.
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机译：我们说明了一种比较新的技术，即禁忌搜索变量选择模型[Drezner，Marcoulides and Salhi（1999）]，如何在研究人员必须从所考虑的整个解释变量集中选择一个变量子集的情况下，才能在金融领域有效地应用它。 。财务方面的几种类型的问题，包括公司和个人破产预测，抵押和信用评分以及套利定价模型的变量选择，要求研究人员从较大的变量集中选择变量的子集。为了证明禁忌搜索变量选择模型的有用性，我们：（1）与主要的替代搜索程序（例如逐步回归和最大R2程序）相比，说明其效率;以及（2）显示如何使用当试图预测公司破产时，可以实施禁忌搜索程序。我们通过表明禁忌搜索程序将公司破产的可预测性与Altman（1968）的Z-Score模型相比提高了10个百分点，从而完成了（2）。
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