  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.
