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An Automated Analytics Framework for Stock Trend Analysis from Multi-Modal Data

International Journal of Electronics and Communication Engineering
© 2024 by SSRG - IJECE Journal
Volume 11 Issue 1
Year of Publication : 2024
Authors : B. Varija, Nagaratna P. Hegde
10.14445/23488549/IJECE-V11I1P109
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How to Cite?

B. Varija, Nagaratna P. Hegde, "An Automated Analytics Framework for Stock Trend Analysis from Multi-Modal Data," SSRG International Journal of Electronics and Communication Engineering, vol. 11,  no. 1, pp. 116-130, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I1P109

Abstract:

Developing comprehensive and automated analytics frameworks for stock trend research is essential due to the turbulent and dynamic financial markets. This paper introduces a novel Automated Analytics Framework for Stock Trend Analysis from Multi-Modal Data (AASAMT). The framework utilizes sentiment extraction methods and historical stock price data to predict market trends. The approach incorporates many modalities of data, merging textual sentiment data acquired from news items and social media with numerical data derived from stock price histories. This involves collecting sentiment indicators from unstructured textual data sources. The sentiment data is then integrated with previous stock price data to provide a complete dataset suitable for study. Machine learning models, such as regression and classification algorithms, forecast stock patterns, furnishing investors with vital insights to facilitate educated decision-making. The framework’s capacity to accommodate many textual data sources enables its flexibility to various markets and industries. The research study includes empirical findings highlighting the framework’s efficacy in predicting stock market movements. Moreover, it illustrates the framework’s capacity to improve decision-making processes for financial stakeholders. The Automated Analytics Framework for Stock Trend Analysis from Multi-Modal Data, which incorporates sentiment extraction and stock prices, signifies a notable advancement in utilizing various data sources and state-of-the-art technologies. This advancement aims to enhance the accuracy of predicting stock trends, consequently facilitating more informed investment strategies within dynamic financial markets.

Keywords:

Stock trends, Sentiment analysis, Multi-Modal Data, Automated analytics framework, Financial markets, Stock price prediction, Natural Language Processing.

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