# Automatic Domain Adaptation Outperforms Manual Domain Adaptation for Predicting Financial Outcomes

arXiv: https://arxiv.org/abs/2006.14209 · HTML: https://mlsanity.com/paper/2006.14209/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2020.

## Built on by (most cited first, 3 of 3)

- [Stock Movement Prediction Based on Bi-typed Hybrid-relational Market Knowledge Graph via Dual Attention Networks](https://mlsanity.com/paper/2201.04965/) (2022)
- [Modular Domain Adaptation](https://mlsanity.com/paper/2204.14213/) (2022)
- [FETILDA: An Effective Framework For Fin-tuned Embeddings For Long Financial Text Documents](https://mlsanity.com/paper/2206.06952/) (2022)

## Abstract (arXiv)

In this paper, we automatically create sentiment dictionaries for predicting financial outcomes. We compare three approaches: (I) manual adaptation of the domain-general dictionary H4N, (ii) automatic adaptation of H4N and (iii) a combination consisting of first manual, then automatic adaptation. In our experiments, we demonstrate that the automatically adapted sentiment dictionary outperforms the previous state of the art in predicting the financial outcomes excess return and volatility. In particular, automatic adaptation performs better than manual adaptation. In our analysis, we find that annotation based on an expert's a priori belief about a word's meaning can be incorrect -- annotation should be performed based on the word's contexts in the target domain instead.