Text Mining of Stocktwits Data for Predicting Stock Prices
This work addresses stock price prediction for financial analysts by providing a domain-specific model, but it is incremental as it builds on existing ALBERT and labeling techniques.
The paper tackled stock price prediction by developing FinALBERT, an ALBERT-based model fine-tuned on Stocktwits data labeled with stock price changes, achieving competitive results compared to traditional machine learning, BERT, and FinBERT models.
Stock price prediction can be made more efficient by considering the price fluctuations and understanding the sentiments of people. A limited number of models understand financial jargon or have labelled datasets concerning stock price change. To overcome this challenge, we introduced FinALBERT, an ALBERT based model trained to handle financial domain text classification tasks by labelling Stocktwits text data based on stock price change. We collected Stocktwits data for over ten years for 25 different companies, including the major five FAANG (Facebook, Amazon, Apple, Netflix, Google). These datasets were labelled with three labelling techniques based on stock price changes. Our proposed model FinALBERT is fine-tuned with these labels to achieve optimal results. We experimented with the labelled dataset by training it on traditional machine learning, BERT, and FinBERT models, which helped us understand how these labels behaved with different model architectures. Our labelling method competitive advantage is that it can help analyse the historical data effectively, and the mathematical function can be easily customised to predict stock movement.