An approach for predicting the price of a stock using deep neural network

For the prediction of any stock price and its fluctuations in prices, researchers have suggested several versions of machine learning techniques. Machine learning-based techniques fail to achieve good prediction and in turn, their accuracy is not adequate to predict the stock price. For sentiment analysis related to the financial domain BERT model is quite useful. The score generated by BERT is useful to get more insight. Few research works which have incorporated financial news, have not used financial corpus for training and testing. FinBERT is quite useful to solve stock pricing fluctuations as it is specially trained on corpus related to the financial domain. The stock market usually gets fluctuated during any impactful news either positive or negative sentiments. In this work, highly fluctuating stock price movement is predicted efficiently which is validated by experiment analysis. Further, in existing research works, stock prices are predicted for a specific company only. In this paper, A hybrid method to predict fluctuations in stock prices has been suggested using FinBERT and Long Short-term Memory (LSTM) along with news that impacted the market. The proposed method using news score and hybrid approach outperforms existing approaches significantly.