# Enhancing Financial Market Predictions: Causality-Driven Feature Selection

arXiv: https://arxiv.org/abs/2408.01005 · HTML: https://mlsanity.com/paper/2408.01005/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 54 references that are in the map.
- First posted on arXiv 2 Aug 2024.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts](https://mlsanity.com/paper/1307.5336/) (2013)
- [Dual Focal Loss for Calibration](https://mlsanity.com/paper/2305.13665/) (2023)
- [AdaFocal: Calibration-aware Adaptive Focal Loss](https://mlsanity.com/paper/2211.11838/) (2022)
- [A neural network-based framework for financial model calibration](https://mlsanity.com/paper/1904.10523/) (2019)
- [Text Mining of Stocktwits Data for Predicting Stock Prices](https://mlsanity.com/paper/2103.16388/) (2021)
- [Incorporating Fine-grained Events in Stock Movement Prediction](https://mlsanity.com/paper/1910.05078/) (2019)

## Built on by (most cited first, 2 of 2)

- [Temporal Relational Reasoning of Large Language Models for Detecting Stock Portfolio Crashes](https://mlsanity.com/paper/2410.17266/) (2024)
- [Calibration Attention: Learning Reliability-Aware Representations for Vision Transformers](https://mlsanity.com/paper/2508.08547/) (2025)

## Abstract (arXiv)

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).