Forecasting Trends in the Stock Market by using Deep Learning Approaches

Volume: 10 | Issue: 02 | Year 2024 | Subscription
International Journal of Software Computing and Testing
Received Date: 06/27/2024
Acceptance Date: 08/13/2024
Published On: 2024-10-07
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By: Lukesh Kadu, Manoj Deshpande, and Vijaykumar Pawar

Abstract

Accurately predicting stock market trends remains a significant challenge in the field of finance. Deep learning, a subset of machine learning, has emerged as a powerful tool for capturing complex relationships within data. What sets deep learning apart is its ability to handle vast amounts of data, recognize intricate features, and adapt to evolving patterns, all while uncovering hidden insights. At its core, deep learning uses artificial neural networks that mimic the learning and pattern recognition capabilities of the human brain, making them highly effective for analyzing financial data. By leveraging advanced deep neural networks, the proposed approach integrates a diverse array of market data, including historical stock prices, trading volumes, and sentiments extracted from news sources.

         With the introduction of social media, the financial markets have changed, with Twitter emerging as a significant platform for real-time information dissemination and sentiment expression. It predicts the sentiment in stock market tweets using advanced machine learning techniques. We present a curated dataset comprising tweets related to various stocks, encompassing a diverse range of sentiments expressed by users. Through natural language processing techniques, we preprocess the textual data to extract relevant features and mitigate noise. Subsequently, we employ state-of-the-art machine learning algorithms, including but not limited to Support Vector Machines (SVM), Random Forest, and Recurrent Neural Networks (RNNs), to model the relationship between tweet content and corresponding sentiment labels. Additionally, we investigate the impact of sentiment lexicons, word embeddings, and domain-specific features on prediction accuracy in terms of investors and financial analysts.

Keywords: profound learning, Deep learning, BERT, financial sentiment analysis, Vader Lexicon

Natural Language Processing

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How to cite this article: Lukesh Kadu, Manoj Deshpande, and Vijaykumar Pawar, Forecasting Trends in the Stock Market by using Deep Learning Approaches. International Journal of Software Computing and Testing. 2024; 10(02): -p.

How to cite this URL: Lukesh Kadu, Manoj Deshpande, and Vijaykumar Pawar, Forecasting Trends in the Stock Market by using Deep Learning Approaches. International Journal of Software Computing and Testing. 2024; 10(02): -p. Available from:https://journalspub.com/publication/ijsct-v10i02-11078/
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