Automatic Domain Adaptation Outperforms Manual Domain Adaptation for Predicting Financial Outcomes

TL;DR

This paper demonstrates that automatically adapting sentiment dictionaries for financial outcome prediction outperforms manual methods, emphasizing the importance of context-based annotation over expert assumptions.

Contribution

It introduces an automatic domain adaptation method for sentiment dictionaries that surpasses manual adaptation in financial prediction tasks.

Findings

Automatic adaptation outperforms manual adaptation in predicting financial outcomes.

Context-based annotation improves sentiment dictionary accuracy.

Automatic methods achieve better results than previous state-of-the-art approaches.

Abstract

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.