  This paper introduces the FinSen dataset that revolutionizes financial market
analysis by integrating economic and financial news articles from 197 countries
with stock market data. The dataset's extensive coverage spans 15 years from
2007 to 2023 with temporal information, offering a rich, global perspective
with 160,000 records on financial market news. Our study leverages causally
validated sentiment scores and LSTM models to enhance market forecast accuracy
and reliability. Utilizing the FinSen dataset, we introduce an innovative Focal
Calibration Loss, reducing Expected Calibration Error (ECE) to 3.34 percent
with the DAN 3 model. This not only improves prediction accuracy but also
aligns probabilistic forecasts closely with real outcomes, crucial for the
financial sector where predicted probability is paramount. Our approach
demonstrates the effectiveness of combining sentiment analysis with precise
calibration techniques for trustworthy financial forecasting where the cost of
misinterpretation can be high. Finsen Data can be found at [this github
URL](https://github.com/EagleAdelaide/FinSen_Dataset.git).
