Comparative Analysis of Artificial Neural Networks for Predicting Nifty 50 value in The Indian Stock Market

Lamba, Vikas; Hooda, Susheela; Solanki, Vikas; Ahuja, Rakesh; Ahuja, Sachin; Kaur, Amandeep

doi:10.1109/iscon52037.2021.9702400

Cited by 5 publications

(1 citation statement)

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“…This transition to AI-based models represents a paradigm shift in how financial analysts and investors approach stock market predictions, moving towards a more data-driven, algorithmic methodology. Lamba et al (2021) provide a focused analysis on the Indian stock market, comparing various neural network models, including feedforward neural networks and radial basis neural networks, for predicting the Nifty 50 index. Their findings reveal that radial basis neural networks offer the highest accuracy, demonstrating the effectiveness of neural network models in capturing the complex patterns of stock market movements.…”

Section: Comparative Analysis Of Neural Network and Other Predictive ...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.

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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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Section: Comparative Analysis Of Neural Network and Other Predictive ...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.

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Escalate the Returns With ML-Based Technical Analysis: Next Day Closing Price Prediction Using RNN and LSTM Models

Mahajan

Anand

Sarangi

et al. 2023

2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE)

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Estimate and Predict the Foreign Currency Exchanging Rates by Using Adaptive Neuro Fuzzy Inference System and Deep Learning Algorithms

Bai,

Wang

2024

2024 4th International Conference on Information Communication and Software Engineering (ICICSE)

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Comparative Analysis of Artificial Neural Networks for Predicting Nifty 50 value in The Indian Stock Market

Cited by 5 publications

References 10 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

Escalate the Returns With ML-Based Technical Analysis: Next Day Closing Price Prediction Using RNN and LSTM Models

Estimate and Predict the Foreign Currency Exchanging Rates by Using Adaptive Neuro Fuzzy Inference System and Deep Learning Algorithms

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