Information spillover and cross-predictability of currency returns: An analysis via Machine Learning

B-Tier
Journal: Journal of Banking & Finance
Year: 2024
Volume: 169
Issue: C

Authors (4)

Score contribution per author:

0.503 = (α=2.01 / 4 authors) × 1.0x B-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

Abstract

This paper documents significant cross-return predictability of news variables, derived from textual analysis of news articles, for a broad cross-section of currencies. By employing forecasts based on the Least Absolute Shrinkage and Selection Operator (LASSO) that incorporate both news variables and forward discounts, we develop a notably profitable trading strategy. This strategy proves robust against transaction costs, risk adjustments, and controls for currency characteristics. Further analyses indicate that both risks and market frictions contribute to the profitability of the trading strategy, highlighting the crucial role of news in financial markets.

Technical Details

RePEc Handle
repec:eee:jbfina:v:169:y:2024:i:c:s0378426624002279
Journal Field
Finance
Author Count
4
Added to Database
2026-01-25