# Predict the Future from the Past? On the Temporal Data Distribution Shift in Financial Sentiment Classifications

arXiv: https://arxiv.org/abs/2310.12620 · HTML: https://mlsanity.com/paper/2310.12620/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 19 Oct 2023.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts](https://mlsanity.com/paper/1307.5336/) (2013)
- [FinBERT: A Pretrained Language Model for Financial Communications](https://mlsanity.com/paper/2006.08097/) (2020)
- [WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domain](https://mlsanity.com/paper/2211.00083/) (2022)
- [Identifying and Mitigating Spurious Correlations for Improving Robustness in NLP Models](https://mlsanity.com/paper/2110.07736/) (2021)
- [Robustness to Spurious Correlations in Text Classification via Automatically Generated Counterfactuals](https://mlsanity.com/paper/2012.10040/) (2020)

## Built on by (most cited first, 6 of 6)

- [Beyond Surface Similarity: Detecting Subtle Semantic Shifts in Financial Narratives](https://mlsanity.com/paper/2403.14341/) (2024)
- [ChronosLex: Time-aware Incremental Training for Temporal Generalization of Legal Classification Tasks](https://mlsanity.com/paper/2405.14211/) (2024)
- [Fully Test-time Adaptation for Tabular Data](https://mlsanity.com/paper/2412.10871/) (2024)
- [Language Modeling for the Future of Finance: A Survey into Metrics, Tasks, and Data Opportunities](https://mlsanity.com/paper/2504.07274/) (2025)
- [Knowledge-driven Augmentation and Retrieval for Integrative Temporal Adaptation](https://mlsanity.com/paper/2604.22098/) (2026)
- [Robust Predictive Modeling Under Unseen Data Distribution Shifts: A Methodological Commentary](https://mlsanity.com/paper/2503.03399/) (2025)

## Abstract (arXiv)

Temporal data distribution shift is prevalent in the financial text. How can a financial sentiment analysis system be trained in a volatile market environment that can accurately infer sentiment and be robust to temporal data distribution shifts? In this paper, we conduct an empirical study on the financial sentiment analysis system under temporal data distribution shifts using a real-world financial social media dataset that spans three years. We find that the fine-tuned models suffer from general performance degradation in the presence of temporal distribution shifts. Furthermore, motivated by the unique temporal nature of the financial text, we propose a novel method that combines out-of-distribution detection with time series modeling for temporal financial sentiment analysis. Experimental results show that the proposed method enhances the model's capability to adapt to evolving temporal shifts in a volatile financial market.