Distinctive Assessment of Neural Network Models in Stock Price Estimation

Verma, Shreya; Mishra, Sushruta; Sharma, Vandana; Nandal, Manju; Garai, Sayan; Alkhayyat, Ahmed

doi:10.4108/eetsis.4643

“…2023) compared the accuracy of LSTM and RNN models in stock price estimation, finding that LSTM models were more effective in predicting stock prices closer to actual values. This study underscores the importance of selecting appropriate neural network architectures based on the specific requirements of each stock market for optimal performance (Verma et al, 2023).…”

Section: Model Performance Across Different Stock Markets In Neural N...mentioning

confidence: 99%

Predicting stock market movements using neural networks: A review and application study

Olatunji Akinrinola,

Wilhelmina Afua Addy,

Adeola Olusola Ajayi-Nifise

et al. 2024

GSC Adv. Res. Rev.

1

0

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In the rapidly evolving landscape of financial markets, the quest for accurate stock market predictions has never been more critical. This paper delves into the transformative potential of neural network models in forecasting stock market movements, offering a comprehensive examination of their effectiveness compared to traditional predictive models. With a focus on the evolution of stock market prediction methodologies, this study aims to uncover the nuanced dynamics of neural networks, their comparative analysis with other models, and the pivotal role of data preprocessing in enhancing prediction accuracy. Employing a qualitative analysis framework, the research meticulously synthesizes findings from selected studies, highlighting the superior performance of neural network models in capturing complex market patterns and adapting to volatility. The results underscore the significant impact of data quality and quantity, architectural nuances of neural networks, and the strategic implications for investors navigating the stock market's unpredictability. Despite the promising outcomes, the study acknowledges inherent challenges in the real-world application of these models, including data imperfections and the complexity of financial ecosystems. Conclusively, the paper advocates for ongoing innovation, interdisciplinary collaboration, and the strategic integration of advanced neural network architectures to overcome existing limitations. Recommendations emphasize the critical need for high-quality, diverse datasets and continuous model refinement to harness the full predictive power of neural networks in stock market forecasting. This study not only illuminates the path forward for investors and financial analysts but also sets the stage for future research in this dynamic field.

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“…Their findings indicate that deep learning techniques, particularly when combined with ensemble learning, can significantly improve prediction accuracy, demonstrating the versatility of neural network models in adapting to different market dynamics (Raipitam et al, 2023). Verma et al (2023) compared the accuracy of LSTM and RNN models in stock price estimation, finding that LSTM models were more effective in predicting stock prices closer to actual values. This study underscores the importance of selecting appropriate neural network architectures based on the specific requirements of each stock market for optimal performance (Verma et al, 2023).…”

Section: Model Performance Across Different Stock Markets In Neural N...mentioning

confidence: 99%

“…Verma et al (2023) compared the accuracy of LSTM and RNN models in stock price estimation, finding that LSTM models were more effective in predicting stock prices closer to actual values. This study underscores the importance of selecting appropriate neural network architectures based on the specific requirements of each stock market for optimal performance (Verma et al, 2023). Gao, Zhang, and Yang (2020) applied various machine learning models, including MLP, LSTM, CNN, and an attention-based neural network, to predict stock index prices in developed, less developed, and developing markets.…”

Section: Model Performance Across Different Stock Markets In Neural N...mentioning

confidence: 99%

Predicting Stock Market Movements Using Neural Networks: A Review and Application Study

Adedoyin Tolulope Oyewole,

Omotayo Bukola Adeoye,

Wilhelmina Afua Addy

et al. 2024

Comput. sci. IT res. j.

6

0

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In the rapidly evolving landscape of financial markets, the quest for accurate stock market predictions has never been more critical. This paper delves into the transformative potential of neural network models in forecasting stock market movements, offering a comprehensive examination of their effectiveness compared to traditional predictive models. With a focus on the evolution of stock market prediction methodologies, this study aims to uncover the nuanced dynamics of neural networks, their comparative analysis with other models, and the pivotal role of data preprocessing in enhancing prediction accuracy. Employing a qualitative analysis framework, the research meticulously synthesizes findings from selected studies, highlighting the superior performance of neural network models in capturing complex market patterns and adapting to volatility. The results underscore the significant impact of data quality and quantity, architectural nuances of neural networks, and the strategic implications for investors navigating the stock market's unpredictability. Despite the promising outcomes, the study acknowledges inherent challenges in the real-world application of these models, including data imperfections and the complexity of financial ecosystems. Conclusively, the paper advocates for ongoing innovation, interdisciplinary collaboration, and the strategic integration of advanced neural network architectures to overcome existing limitations. Recommendations emphasize the critical need for high-quality, diverse datasets and continuous model refinement to harness the full predictive power of neural networks in stock market forecasting. This study not only illuminates the path forward for investors and financial analysts but also sets the stage for future research in this dynamic field. Keywords: Neural Network Models, Stock Market Prediction, Data Preprocessing, Model Accuracy, Financial Markets, Innovation.

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Cybersecurity Threats Detection in Intelligent Networks using Predictive Analytics Approaches

Duary,

Choudhury,

Mishra

et al. 2024

2024 4th International Conference on Innovative Practices in Technology and Management (ICIPTM)

22

0

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Distinctive Assessment of Neural Network Models in Stock Price Estimation

Cited by 6 publications

References 20 publications

Predicting stock market movements using neural networks: A review and application study

Predicting stock market movements using neural networks: A review and application study

Predicting Stock Market Movements Using Neural Networks: A Review and Application Study

Cybersecurity Threats Detection in Intelligent Networks using Predictive Analytics Approaches

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