How can FinBERT help you get a jump on your competitors?
“If stock market experts were so expert, they would be buying stock, not selling advice – Norman Ralph Augustine”
BERT was able to make significant breakthrough in NLP. However, the financial sector is also faced with large volumes of financial text that needs to be analysed and BERT is just not doing it. Sentiment analysis has many takers be it movie or product reviews. But there do not exist substantial models which can learn the context of positive or negative in financial sentiment analysis because of the limitations in domain specific language. It is very difficult for NLP to extract positive or negative messages in the financial context as the rest of the words in the sentence may send out a different message.
‘Gold prices plummet at the news of a vaccine discovery’, a news item like this can be interpreted in many ways. Financial text/news is difficult to decipher unlike regular texts.
FinBERT text analytics was created as a result of this. It is a language model based on BERT to tackle NLP in financial domain. It has shown considerable improvements on every metric in reading financial sentiment analysis. It has a deeper understanding of the financial language and can be fine-tuned for sentiment classification in the financial world. BERT was already trained on how to read text but here on it was important to read financial text and interpret the sentiment in the text. FinBERT is the pre-existing BERT trained on financial jargon and then finetuned for it with labelled data for financial sentiment classification.
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Advantages of FinBERT Text Analytics
1. In order to utilise BERT, a much less training time with decrease in in-model performance will do justice unlike the expensive method of training the whole model
2. A text editor that processes text and highlights sentences according to their predicted sentiment.
3. A dashboard that gets tweets from financial news outlets and gives their perspective
4. Any data scientist can develop their own application with FinBERT without a new model.
5. Large data sets are not required for finetuning the model as the model learns about the language during the training phase. BERT took it a step ahead with its transformer architecture
Limitation of FinBERT Text Analytics
1. It is a large model, so fine-tuning the whole model requires significant time and computing power.
2. The model fails to do math in which the figure is higher where there are no indicative words like higher
3. It fails to distinguish neutral statements about a given situation
4. It fails to identify if the situation is a positive outlook or an objective observation
The FinBERT text analytics model is trained on a large financial corpora that are a part of English financial communication and they outperform generic BERT on three sentiment classifications. FinBERT can be utilised for wider range of application beyond sentiment such as corporate fraud, stock volatilities, making interpretations to financial reports etc.
Authors: – Benila Jacob, Mark John and Sunil Kumar