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Social and Behavioral Sciences Commons™
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Full-Text Articles in Social and Behavioral Sciences
The Machines Aren’T Taking Over (Yet): An Empirical Comparison Of Traditional, Profiling, And Machine Learning Approaches To Criterion-Related Validation, Kristin S. Allen, Mathijs Affourtit, Craig M. Reddock
The Machines Aren’T Taking Over (Yet): An Empirical Comparison Of Traditional, Profiling, And Machine Learning Approaches To Criterion-Related Validation, Kristin S. Allen, Mathijs Affourtit, Craig M. Reddock
Personnel Assessment and Decisions
Criterion-related validation (CRV) studies are used to demonstrate the effectiveness of selection procedures. However, traditional CRV studies require significant investment of time and resources, as well as large sample sizes, which often create practical challenges. New techniques, which use machine learning to develop classification models from limited amounts of data, have emerged as a more efficient alternative. This study empirically investigates the effectiveness of traditional CRV with a variety of profiling approaches and machine learning techniques using repeated cross-validation. Results show that the traditional approach generally performs best both in terms of predicting performance and larger group differences between candidates …
Deploying Machine Learning For A Sustainable Future, Cary Coglianese
Deploying Machine Learning For A Sustainable Future, Cary Coglianese
All Faculty Scholarship
To meet the environmental challenges of a warming planet and an increasingly complex, high tech economy, government must become smarter about how it makes policies and deploys its limited resources. It specifically needs to build a robust capacity to analyze large volumes of environmental and economic data by using machine-learning algorithms to improve regulatory oversight, monitoring, and decision-making. Three challenges can be expected to drive the need for algorithmic environmental governance: more problems, less funding, and growing public demands. This paper explains why algorithmic governance will prove pivotal in meeting these challenges, but it also presents four likely obstacles that …
Three Essays On Financial Economics, Jiangyuan Li
Three Essays On Financial Economics, Jiangyuan Li
Dissertations and Theses Collection (Open Access)
Disagreement measures are known to predict cross-sectional stock returns but fail to predict market returns. This paper proposes a partial least squares disagreement index by aggregating information across individual disagreement measures and shows that this index significantly predicts market returns both in- and out-ofsample. Consistent with the theory in Atmaz and Basak (2018), the disagreement index asymmetrically predicts market returns with greater power in high sentiment periods, is positively associated with investor expectations of market returns, predicts market returns through a cash flow channel, and can explain the positive volume-volatility relationship.