Training LLM to analyze the Brazilian financial news.

This post was automatically generated using Hypotenuse AI, based on my publication FinBERT-PT-BR: Análise de Sentimentos de Textos em Português do Mercado Financeiro. The model is available for free on Hugging Face.

Introduction

Hey there, have you ever wondered if you could predict the ups and downs of the stock market?

Using FinBERT, an AI model trained on financial news in Portuguese, you absolutely can.

FinBERT analyzes thousands of news articles and social media posts to determine the overall sentiment of investors in Brazil’s stock market. If FinBERT detects an increase in positive sentiment, it’s likely stock prices will rise. A swing towards more negative sentiment suggests a downturn may be on the horizon. By tracking these sentiment shifts, FinBERT can identify trends in the market and even predict changes before they happen.

In this article, we’ll explore how FinBERT works, see examples of its accuracy, and discuss how similar AI could be applied to stock markets around the world. If you’re an investor looking for an edge, sentiment analysis with neural networks may be the insight you’ve been searching for.

Ready to take a peek inside the minds of investors? Let’s dive in.

Understanding Sentiment Analysis in Finance

Sentiment analysis allows us to gage the overall opinion or emotional tone behind words and phrases in text data. In finance, it provides a way to analyze how market participants feel about stocks, sectors or the overall market based on news articles, social media posts, and other commentary.

By tracking sentiment over time, you can identify trends that may impact stock prices or market movements. For example, increasingly positive sentiment surrounding a company could signal an upward trend in their stock price. Negative sentiment following a product launch, on the other hand, may indicate lower future earnings.

Of course, sentiment is not the only driver of the markets, but when combined with fundamental and technical analysis, it can provide additional insight into the psychology of investors and analysts. Sentiment analysis models are trained on large datasets of text that have been manually labeled as positive, negative or neutral. The models can then automatically analyze new text and determine an overall sentiment score.

More advanced models like BERT (Bidirectional Encoder Representations from Transformers) use neural networks to understand context and complex language nuances. FinBERT-PT-BR, for example, is a Portuguese sentiment analysis model for financial news articles. It was trained on 1.4 million unlabeled news articles and 500 manually labeled articles to better understand the sentiment of financial texts.

With the right tools and models, sentiment analysis can offer a useful metric for gaging market psychology and detecting shifts in investor moods and behaviors. When combined with other market indicators, it provides another layer of insight to drive smarter investment decisions.

Introducing FinBERT-PT-BR: A Novel Approach for Analyzing Brazilian Markets

So, you want to analyze the Brazilian stock market using neural networks, huh? FinBERT-PT-BR is a state-of-the-art language model tailored for this purpose.

Training the Model

To build FinBERT-PT-BR, we first collected millions of finance news articles from major Brazilian media outlets like Valor Econômico, Exame and InfoMoney. We then fine-tuned BERTimbau, a general Portuguese language model, on this dataset.

The fine-tuned model, FinBERT-PT-BR, was then trained further on a subset of 500 manually labeled news articles to generate SentFinBERT-PT-BR, a sentiment classifier. SentFinBERT-PT-BR assigns one of three sentiment labels (positive, negative or neutral) to sentences in Portuguese finance news.

Evaluating Performance

We evaluated SentFinBERT-PT-BR on various metrics and compared it to other models. SentFinBERT-PT-BR outperformed the current state-of-the-art, demonstrating its power for building sentiment indices, crafting investment strategies, and analyzing macroeconomic trends.

FinBERT-PT-BR and SentFinBERT-PT-BR show the potential of natural language processing and neural networks for quantitative finance. SentFinBERT-PT-BR in particular can help investors gain valuable insights into market sentiment and make more informed decisions.

While finance news sentiment is just one piece of the puzzle, it provides an extra data point to consider when analyzing stocks or the overall market. Paired with fundamental and technical analysis, sentiment analysis gives investors another tool to potentially achieve higher returns.

Training FinBERT-PT-BR on a Massive Dataset of Financial News

To train FinBERT-PT-BR, we gathered a massive dataset of 2.7 million financial news articles published between 2006 to 2021 from Valor Econômico, Exame and InfoMoney, totaling 130 million words.

After collecting the texts, we cleaned the data by removing HTML tags, uncommon symbols and punctuation, and performed spell checking. We then segmented the texts into sentences to obtain a total of 1.4 million sentences.

Pre-training FinBERT-PT-BR

We used BERTimbau, a BERT model pretrained on Brazilian Portuguese data, to initialize FinBERT-PT-BR. We then fine-tuned BERTimbau on our dataset of financial news articles through self-supervised tasks like masked language modeling and next sentence prediction.

This pre-training allows the model to learn the language used in financial news and capture relationships between words, phrases, and concepts. The pre-trained FinBERT-PT-BR model can then be used for various downstream tasks like sentiment analysis, named entity recognition, question answering, and more.

Sentiment Labeling

To train the sentiment classifier SentFinBERT-PT-BR, we manually labeled a subset of 500 sentences from our dataset with sentiment polarity: positive, negative or neutral. The neutral class represents sentences with no clear sentiment or mixed sentiments.

These labeled sentences were then used to fine-tune the pre-trained FinBERT-PT-BR model for sentiment classification. The fine-tuned model, SentFinBERT-PT-BR, can classify the sentiment of new sentences with a high degree of accuracy.

In summary, the massive dataset, pre-training, and sentiment labeling were key to developing an advanced sentiment analysis model for financial news in Brazilian Portuguese. FinBERT-PT-BR and SentFinBERT-PT-BR achieve state-of-the-art performance on this task and can enable new applications like sentiment trading strategies, macroeconomic analysis, and stock market prediction.

Evaluating Model Performance Against Other State-of-the-Art Methods

Evaluating how well your model performs is key to understanding its effectiveness and limitations. There are a few ways to measure this for sentiment analysis models.

Accuracy and F1 score are two of the most common metrics used. Accuracy measures how often the model correctly predicts the sentiment label, while the F1 score considers both precision (how often the model is correct when it predicts a certain label) and recall (how often the model predicts the correct label). For sentiment analysis, F1 scores for each label (positive, negative and neutral) are often reported.

The FinBERT-PT-BR model showed strong performance on these metrics compared to other state-of-the-art models. It achieved an accuracy of 0.76 and F1 scores of 0.73. These scores demonstrate the model is quite adept at predicting sentiment for financial news articles.

Model	Accuracy	F1-Score
FinBERT-PT-BR	0.76	0.73
BERTimbau	0.67	0.63

Another way to evaluate the model is to look at specific examples it analyzed to get a qualitative sense of its performance. For example, the model correctly predicted positive sentiment for news articles announcing interest rate cuts, new investments in the economy and rising stock prices. It identified negative sentiment for news covering increasing unemployment, declining GDP and trade disputes.

The model did struggle at times with complex articles and with specific words like “abertura” (opening) and “fechamento” (closing) of interest rates.

Overall though, the FinBERT-PT-BR model shows a lot of promise for analyzing sentiment in Portuguese financial news articles. With strong scores on key metrics and examples demonstrating logical predictions, this model could be useful for building sentiment indices, investment strategies and gaining insights from macroeconomic news. Continued development of the model and application to new data sets will help unlock even more potential.

Applications of FinBERT-PT-BR: Sentiment Indices and Trading Strategies

FinBERT-PT-BR can be used to build sentiment indices and trading strategies based on sentiment data. By analyzing sentiment expressed in financial news articles, FinBERT-PT-BR provides insights into overall market sentiment and stock-specific sentiment.

Sentiment Indices

FinBERT-PT-BR can be used to build broad market sentiment indices based on sentiment scores aggregated from many financial news articles. For example, a “bullish sentiment index” could track the percentage of positively-scored sentences in articles over time. If sentiment is largely optimistic, the index would increase, signaling a good time to invest. If pessimism reigns, the index may predict a market downturn.

Stock-Specific Sentiment

FinBERT-PT-BR also enables analysis of sentiment targeting specific stocks or companies. By extracting sentences mentioning a stock ticker or company name, FinBERT-PT-BR can gage sentiment surrounding that asset. Bullish sentiment could indicate a good buying opportunity or price increase, while bearish sentiment may foreshadow a price drop.

Trading Strategies

Using sentiment indices and stock-specific sentiment, you can build basic trading strategies. For example, you might buy stocks with bullish sentiment and sell stocks with bearish sentiment. You could also wait for sentiment reversals, buying when pessimism spikes and selling into optimism. More advanced strategies could combine sentiment with fundamental and technical analysis.

While sentiment alone should not drive investment decisions, FinBERT-PT-BR provides an additional data point to incorporate into a holistic trading strategy. Used properly, natural language processing and sentiment analysis can give investors an “edge” by identifying sentiment shifts early. However, sentiment is often short-lived and volatile, so strategies should account for this variability. Overall, FinBERT-PT-BR opens up many possibilities for quantitative and algorithmic trading based on insights from financial news.

Conclusion

So there you have it, a promising new tool for analyzing the ups and downs of the Brazilian stock market. FinBERT-PT-BR isn’t magic - it requires training data and tuning to perform at its best. But with some time and effort, it can provide insights into how investors are feeling about companies and sectors that traditional metrics alone may miss. The future of AI in finance looks bright.