Abstract: In binary classification prediction,there are two types of errors that inevitably occur in the prediction of stocks,but the misclassification costs of these two types of errors are often different in practical applications, and this article focuses on this issue. This paper introduces an asymmetric LINEX (Linear-exponential) loss function, which uses LINEX loss to achieve cost sensitive learning by penalizing the low cost of misclassification class linearly and punishing the high cost of misclassification class exponentially.The model uses SVM as the base classifier for AdaBoost, embedding LINEX loss function into AdaBoost-SVM weight update equation and updating the samples weights according to the different misclassification costs of positive and negative samples and whether the samples are misclassified or not. This paper takes the components of HS300 from January 2011 to December 2020 as samples for empirical research, and the proposed model is used to predict the rise and fall. The result shows that the ADASVM-CSLINEX model can obtain higher investment performance.

Key words: quantitative stock selection, misclassification cost, LINEX loss function, support vector machine, AdaBoost

摘要： 在二分类预测中存在对两类样本的两类分类错误，在现实应用中这两类错误的代价往往是不同的，该文考虑了两类错误的错分代价问题。通过引入一种具有非对称性的LINEX损失函数，可以实现对低错分代价的样本进行线性级惩罚，对高错分代价的样本进行指数级惩罚。模型以SVM作为AdaBoost的基分类器，再将LINEX损失函数嵌入到AdaBoost-SVM的权重更新方程中，根据对正负类样本错分代价的不同以及样本是否错分重新更新样本权重。对2011年1月至2020年12月的沪深300指数成分股进行了实证研究，利用所提出的模型方法进行涨跌预测，研究发现所构建的ADASVM-CSLINEX模型可以获得更高的投资绩效。

关键词: 量化选股, 错分代价, LINEX损失函数, 支持向量机, AdaBoost