可解释性
美德
线性判别分析
预处理器
计算机科学
二元分析
人工智能
判别式
回归
机器学习
股权溢价之谜
衡平法
计量经济学
统计
数学
风险溢价
认识论
哲学
法学
政治学
出处
期刊:The journal of financial data science
[Pageant Media US]
日期:2023-06-05
卷期号:5 (3): 80-87
标识
DOI:10.3905/jfds.2023.1.126
摘要
When forecasting the equity risk premium, simple techniques generate results that are easier to interpret than results from more complex techniques. If complex techniques have better performance, does the virtue of superior performance trump the vice of lack of interpretability? This presumes simpler techniques underperform. Complex does not equate to superior performance. Old and simple techniques like discriminant analysis combine the virtue of performance with the virtue of intelligibility. This article performs a horse race among stepwise quadratic discriminant analysis, classification trees, regression trees, and ridgeless regression. Sometimes, accuracy can be sacrificed in favor of better out-of-sample Sharpe ratios. This article also shows that preprocessing data using rolling percentage ranks can be better than using either an expanding window or Z-scores.
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