FinEAS: Financial Embedding Analysis of Sentiment šŸ“ˆ

Related tags

Deep Learning finance-news-analysis-bert
Overview

FinEAS: Financial Embedding Analysis of Sentiment šŸ“ˆ

(SentenceBERT for Financial News Sentiment Regression)

This repository contains the code for generating three models for Finance News Sentiment Analysis.

The models implemented are:

  • A SentenceBERT with simple classifier.
  • A BERT with simple classifier.
  • A simple Bi-directional Long-Short Term Memory (LSTM) network.
  • FinBERT from HuggingFace.
  • SentenceBERT from HuggingFace

Models šŸ¤–

Results āœ…

We used three partitions of the datasets from the February 11th, 2021. 6 months previous to that date, 1 year previous to that date and 2 years previous to the date mentioned.

We also evaluated the models 2 weeks later that date; that is to say, we evaluated from February 12th, 2021 to February 26th, 2021.

The table below shows the results:

FinEAS BERT BiLSTM
6 months 0.0556 0.2124 0.2108
6 months
Next 2 weeks		 0.1061 0.2190 0.2194
1 year 0.0654 0.2137 0.2140
1 year
Next 2 weeks		 0.1058 0.2191 0.2194
2 years 0.0671 0.2087 0.2086
2 years
Next 2 weeks		 0.1065 0.2188 0.2185

The table below shows the results for the HuggingFace models

Dates FinEAS FinBERT
6 months 0.0044 0.0050
12 months 0.0036 0.0034
24 months 0.0033 0.0040

Citing šŸ“£ 

@misc{gutierrezfandino2021fineas,
      title={FinEAS: Financial Embedding Analysis of Sentiment}, 
      author={Asier GutiƩrrez-FandiƱo and Miquel Noguer i Alonso and Petter Kolm and Jordi Armengol-EstapƩ},
      year={2021},
      eprint={2111.00526},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

License šŸ¤

MIT License.

Copyright 2021 Asier GutiƩrrez-FandiƱo & Jordi Armengol-EstapƩ.

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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Comments
  • Download and prepare data

    Download and prepare data

    1. Use Ravenpack keys to access the dataset.
    2. Download dataset.
    3. Filter by companies (column COMP). We only want rows with COMP in [LIST OF COMPANIES*]. "Company+US" (by default 50 companies).
    4. For the model we will use the free text from EVENT_TEXT as input and EVENT_SENTIMENT_SCORE (-1 to ++1). Free text should be left as is. SENTIMENT_SCORE should be scaled to [0, 1]. Column COMP should be kept. If there is an "id" column, keep it as well.
    5. Sampling. For the moment, don't sample any subset. If there are too many rows for the model, we will take a subset.
    6. Do a train-valid-test split. For the moment, random but reproducible. Validation and test should be like 4000 sentences each, ideally with at least 1 company of the ones we selected.

    *this list should be public companies: Apple, Google...

    opened by jordiae 2
  • Train SentenceBERT model

    Train SentenceBERT model

    Use https://github.com/UKPLab/sentence-transformers

    It's very easy to use. Use it as feature extractor for the moment, attach a simple linear classifier for the moment.

    enhancement 
    opened by jordiae 1
  • Train baseline(s)

    Train baseline(s)

    First train SentenceBERT. At some point, train one or two baselines. Like: An LSTM (embeddings + LSTM + linear classifier), or the original BERT (to show that SentenceBERT is more sensible choice).

    enhancement 
    opened by jordiae 0
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