# Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis

arXiv: https://arxiv.org/abs/2212.11765 · HTML: https://mlsanity.com/paper/2212.11765/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 31 references that are in the map.
- First posted on arXiv 13 Nov 2022.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [On the Variance of the Adaptive Learning Rate and Beyond](https://mlsanity.com/paper/1908.03265/) (2019)
- [Prediction of ESG Compliance using a Heterogeneous Information Network](https://mlsanity.com/paper/1811.12166/) (2018)
- [Heterogeneous Ensemble for ESG Ratings Prediction](https://mlsanity.com/paper/2109.10085/) (2021)

## Built on by (most cited first, 1 of 1)

- [Leveraging Natural Language and Item Response Theory Models for ESG Scoring](https://mlsanity.com/paper/2407.20377/) (2024)

## Abstract (arXiv)

Environmental, social and governance (ESG) engagement of companies moved into the focus of public attention over recent years. With the requirements of compulsory reporting being implemented and investors incorporating sustainability in their investment decisions, the demand for transparent and reliable ESG ratings is increasing. However, automatic approaches for forecasting ESG ratings have been quite scarce despite the increasing importance of the topic. In this paper, we build a model to predict ESG ratings from news articles using the combination of multivariate timeseries construction and deep learning techniques. A news dataset for about 3,000 US companies together with their ratings is also created and released for training. Through the experimental evaluation we find out that our approach provides accurate results outperforming the state-of-the-art, and can be used in practice to support a manual determination or analysis of ESG ratings.