News Sentiment and Stock Market Dynamics: A Machine Learning Investigation
Milivoje Davidović; Jacqueline McCleary · 2025 · Journal of risk and financial management
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
The study relies on an extensive dataset (≈1.86 million news headlines) to investigate the heterogeneity and predictive power of explicit sentiment signals (TextBlob, VADER, and FinBERT) and implied sentiment (VIX) for stock market trends. We find that news content predominantly consists of objective or neutral information, with only a small portion carrying subjective or emotive weight. There is a structural market bias toward upswings (bullish market states). Market behavior appears anticipatory rather than reactive: forward-looking implied sentiment captures a substantial share (≈45–50%) of
Abstract by Milivoje Davidović; Jacqueline McCleary, Journal of risk and financial management (2025) — licensed CC BY 4.0.
About to run something similar?
Run an AI Precheck on your own design to catch failure modes like this one before you spend the time. Your first desk check is free.
Related failures
Estimating the reproducibility of psychological science
Negative / Null Result ReportWillingness to Share Research Data Is Related to the Strength of the Evidence and the Quality of Reporting of Statistical Results
Methods Dead-EndComparing multiple comparisons: practical guidance for choosing the best multiple comparisons test
Replication FailureInferential Statistics as Descriptive Statistics: There Is No Replication Crisis if We Don’t Expect Replication
Negative / Null Result ReportMOBILE BANKING ADOPTION: APPLICATION OF DIFFUSION OF INNOVATION THEORY
Negative / Null Result ReportEnjoyment and social influence: predicting mobile payment adoption
WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: OpenAlex · DOI 10.3390/jrfm18080412