Predicting Financial Literacy via Semi-supervised Learning
用半监督学习预测金融素养AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要研究金融素养的半监督回归预测，未涉及所列资产定价、收益预测或其他金融计量细分，故不强行归类。
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用半监督学习预测金融素养
- 研究问题
- 金融素养标签有限且分布不平衡时，如何利用客户金融记录中的未标注数据预测素养水平，并改善对少数取值区间的预测表现？
- 方法与思路
- 作者提出 SMOGN-COREG，将带高斯噪声的回归过采样方法与非参数多学习器协同回归结合，用于处理不平衡和未标注数据。
- 材料陈述的贡献
- 摘要称该方法在五个数据集上优于六种比较回归器，为利用未标注金融数据估计金融素养提供了方案，但未报告具体改进幅度。
- 阅读关注点
- 应核对金融素养的定义及标签来源、未标注数据的使用方式、过采样与验证集的隔离，以及评价指标是否反映少数区间表现。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实。摘要未提供数据细节，也不能据预测表现推断干预能够增加利润或减少政府支出。
原文摘要
Financial literacy (FL) represents a person's ability to turn assets into income, and understanding digital currencies has been added to the modern definition. FL can be predicted by exploiting unlabelled recorded data in financial networks via semi-supervised learning (SSL). Measuring and predicting FL has not been widely studied, resulting in limited understanding of customer financial engagement consequences. Previous studies have shown that low FL increases the risk of social harm. Therefore, it is important to accurately estimate FL to allocate specific intervention programs to less financially literate groups. This will not only increase company profitability, but will also reduce government spending. Some studies considered predicting FL in classification tasks, whereas others developed FL definitions and impacts. The current paper investigated mechanisms to learn customer FL level from their financial data using sampling by synthetic minority over-sampling techniques for regression with Gaussian noise (SMOGN). We propose the SMOGN-COREG model for semi-supervised regression, applying SMOGN to deal with unbalanced datasets and a nonparametric multi-learner co-regression (COREG) algorithm for labeling. We compared the SMOGN-COREG model with six well-known regressors on five datasets to evaluate the proposed models effectiveness on unbalanced and unlabelled financial data. Experimental results confirmed that the proposed method outperformed the comparator models for unbalanced and unlabelled financial data. Therefore, SMOGN-COREG is a step towards using unlabelled data to estimate FL level.