Develops design-based causal inference methods for spatial treatments using counterfactual candidate locations, extends double ML for spatial correlations, and applies to grocery store effects on foot traffic.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
News embeddings from financial text improve out-of-sample realized volatility forecasts for stocks, with stronger effects for stock-specific news and high-volatility periods, and yield gains when combined with benchmarks.
citing papers explorer
-
Causal Inference for Spatial Treatments
Develops design-based causal inference methods for spatial treatments using counterfactual candidate locations, extends double ML for spatial correlations, and applies to grocery store effects on foot traffic.
-
Realised Volatility Forecasting: Machine Learning via Financial Word Embedding
News embeddings from financial text improve out-of-sample realized volatility forecasts for stocks, with stronger effects for stock-specific news and high-volatility periods, and yield gains when combined with benchmarks.