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Journal of Information and Optimization Sciences cover

Journal of Information and Optimization Sciences

(Since 1980)
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Journal of Information and Optimization Sciences
Open Access Research Article

Predicting stock prices with LSTM: A hybrid machine learning model for financial forecasting

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pp. 575–584Vol. 44Issue 3April 2023DOI: 10.47974/JIOS-1416XML
Published Online:
11 Aug 2023
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1416
Pages:
575–584

Abstract

This article discusses the challenges of accurately predicting the direction of the stock market and proposes a new approach using machine learning and manual forecasting. The article explores the use of technical analysis and machine learning to predict current stock market indices’ values by training on historical data. The authors demonstrate how these methods can be used to influence investor judgments at different levels of consideration, including unrestricted, near, medium, high, and volumic. The article also explores the use of social media platforms like Twitter and the correlation between stock prices and local weather patterns to improve forecasting accuracy. The authors present their research in three phases, demonstrating the potential of machine learning and technical analysis to provide accurate and reliable predictions for investors seeking to protect themselves from market volatility.

Keywords

Mathematical modelingOrdinary differential equationsA duopoly economyMarket share

Subject Classifications

Primary 93A30Secondary 49K15

References

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