Stock Market prediction from Financial and News Data a Deep Learning approach

Abstract

Stock market prediction is a challenging and complex problem that has received the attention of researchers due to the high returns resulting from an newlineimproved prediction. Even though machine learning models are popular newlinein this domain, the dynamic and volatile nature of the stock markets limits newlinethe accuracy of stock prediction. An extensive literature review was conducted, and the scope for improving the performance of existing models newlinewas observed. newlineThere is a need to develop an architecture that facilitates noise removal from stock data and ensures prediction to a reasonable degree of newlineaccuracy. Our approach to utilizing deep learning for stock forecasting newlinecomprises thoroughly examining existing literature, gathering and preparing data, creating and training models, assessing its performance, analyzing the outcomes, interpreting the findings, and presenting the results. newlineThe performance of different deep learning techniques in stock market prediction was compared. The experiments show that these models newlineoutperform traditional machine learning methods in performance metrics. newlineThis research designs efficient deep learning architectures for stock prediction. We have developed an architecture combining a deep autoencoder and newlineLong Short Term Memory (LSTM)/Gated Recurrent Unit (GRU) to give a novel deep learning framework to forecast the stock price. Applying a newlinedeep autoencoder that extracts deep features is a new concept in stock price newlineforecasting. The autoencoder denoises the stock data, and the LSTM/GRU newlinemodels store past information to predict the future stock price. newlineAn effort is made to investigate the influence of market sentiment newlineanalysis on stock forecasting, and we created a deep learning framework newlinethat incorporates this factor. A cooperative deep learning architecture for newlinestock market prediction using the deep autoencoder, LSTM/GRU, and sentiment analysis with news headlines is proposed. newline