Forecasting Directional Movement of Stock Prices using Deep Learning

Deeksha Chandola (), Akshit Mehta (), Shikha Singh (), Vinay Anand Tikkiwal () and Himanshu Agrawal ()
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Deeksha Chandola: York University
Akshit Mehta: University of Florida Gainesville
Shikha Singh: Jaypee Institute of Information Technology
Vinay Anand Tikkiwal: Jaypee Institute of Information Technology
Himanshu Agrawal: Jaypee Institute of Information Technology

Annals of Data Science, 2023, vol. 10, issue 5, No 9, 1378 pages

Abstract: Abstract Stock market’s volatile and complex nature makes it difficult to predict the market situation. Deep Learning is capable of simulating and analyzing complex patterns in unstructured data. Deep learning models have applications in image recognition, speech recognition, natural language processing (NLP), and many more. Its application in stock market prediction is gaining attention because of its capacity to handle large datasets and data mapping with accurate prediction. However, most methods ignore the impact of mass media on the company’s stock and investors’ behaviours. This work proposes a hybrid deep learning model combining Word2Vec and long short-term memory (LSTM) algorithms. The main objective is to design an intelligent tool to forecast the directional movement of stock market prices based on financial time series and news headlines as inputs. The binary predicted output obtained using the proposed model would aid investors in making better decisions. The effectiveness of the proposed model is assessed in terms of accuracy of the prediction of directional movement of stock prices of five companies from different sectors of operation.

Keywords: Deep learning; Stock forecasting; Word2vec; LSTM; AI; NLP (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s40745-022-00432-6

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