Firm-level studies of sustainable investment performance are typically limited by an errors-in-variables bias (i.e., a distortion of estimated regression coefficients caused by measurement error in explanatory variables). Using recent advances in statistical methodology, we present the first cross-sectional analysis of sustainable stock selection which adequately corrects for this bias and additionally answers the question of whether betas with respect to sustainable risk factors or sustainable characteristics (i.e., environmental, social, and governance ratings) are more relevant in portfolio selection. Within the universe of S&P 500 stocks, which is highly relevant from the investor attention and liquidity perspectives, we find that, after accounting for errors-in-variables bias, both types of variables become insignificant. Consequently, they do not add value to investment portfolios and are not vital in models explaining stock returns. Among classic predictors with a long history of use in the investment fund industry, only the market-to-book ratio provides independent investment and pricing information.

中文翻译：

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可持续投资策略的实施和盈利能力：变量误差视角

可持续投资绩效的公司层面研究通常受到变量误差偏差的限制（即，由解释变量的测量误差引起的估计回归系数的失真）。利用统计方法的最新进展，我们首次对可持续股票选择进行横断面分析，该分析充分纠正了这种偏差，并另外回答了关于可持续风险因素或可持续特征（即环境、社会和治理评级）在投资组合选择中更相关。在与投资者关注度和流动性角度高度相关的标准普尔 500 指数股票中，我们发现，在考虑了变量误差偏差后，这两种类型的变量都变得无关紧要。最后，它们不会为投资组合增加价值，并且在解释股票回报的模型中也不是至关重要的。在投资基金行业使用历史悠久的经典预测指标中，只有市净率可以提供独立的投资和定价信息。