Advanced Stock Market Prediction Using Long Short-Term Memory Networks: A Comprehensive Deep Learning Framework
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866910933091090432 |
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| author | Chaudhary, Rajneesh |
| author_facet | Chaudhary, Rajneesh |
| contents | Predicting stock market movements remains a persistent challenge due to the inherently volatile, non-linear, and stochastic nature of financial time series data. This paper introduces a deep learning-based framework employing Long Short-Term Memory (LSTM) networks to forecast the closing stock prices of major technology firms: Apple, Google, Microsoft, and Amazon, listed on NASDAQ. Historical data was sourced from Yahoo Finance and processed using normalization and feature engineering techniques. The proposed model achieves a Mean Absolute Percentage Error (MAPE) of 2.72 on unseen test data, significantly outperforming traditional models like ARIMA. To further enhance predictive accuracy, sentiment scores were integrated using real-time news articles and social media data, analyzed through the VADER sentiment analysis tool. A web application was also developed to provide real-time visualizations of stock price forecasts, offering practical utility for both individual and institutional investors. This research demonstrates the strength of LSTM networks in modeling complex financial sequences and presents a novel hybrid approach combining time series modeling with sentiment analysis. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_05325 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Advanced Stock Market Prediction Using Long Short-Term Memory Networks: A Comprehensive Deep Learning Framework Chaudhary, Rajneesh Computational Engineering, Finance, and Science 91G10, 68T07 I.2.7; I.5.1; J.4 Predicting stock market movements remains a persistent challenge due to the inherently volatile, non-linear, and stochastic nature of financial time series data. This paper introduces a deep learning-based framework employing Long Short-Term Memory (LSTM) networks to forecast the closing stock prices of major technology firms: Apple, Google, Microsoft, and Amazon, listed on NASDAQ. Historical data was sourced from Yahoo Finance and processed using normalization and feature engineering techniques. The proposed model achieves a Mean Absolute Percentage Error (MAPE) of 2.72 on unseen test data, significantly outperforming traditional models like ARIMA. To further enhance predictive accuracy, sentiment scores were integrated using real-time news articles and social media data, analyzed through the VADER sentiment analysis tool. A web application was also developed to provide real-time visualizations of stock price forecasts, offering practical utility for both individual and institutional investors. This research demonstrates the strength of LSTM networks in modeling complex financial sequences and presents a novel hybrid approach combining time series modeling with sentiment analysis. |
| title | Advanced Stock Market Prediction Using Long Short-Term Memory Networks: A Comprehensive Deep Learning Framework |
| topic | Computational Engineering, Finance, and Science 91G10, 68T07 I.2.7; I.5.1; J.4 |
| url | https://arxiv.org/abs/2505.05325 |