Fintech Empowers Prediction of Stock Market Index Using Artificial Neural Network

Paul, Taraknath

doi:10.1109/icaicst53116.2021.9497817

“…Paul (2021) discusses the complexity of predicting stock market indices due to the influence of diverse factors such as market trends, economic indicators, and investor behavior. The study highlights the difficulty in developing models that can accurately predict daily stock market movements, pointing out the limitations of artificial neural networks in capturing the multifaceted nature of financial markets (Paul, 2021). Liu et al (2021) propose a deep neural network model for stock market prediction, acknowledging the challenges posed by the uncertain noise in stock index prices.…”

Section: Limitations Observed In Neural Network Predictions For Stock...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.

2

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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“…This section synthesizes findings from selected studies to provide insights into the challenges and limitations observed in neural network predictions for stock market forecasting. Paul (2021) discusses the complexity of predicting stock market indices due to the influence of diverse factors such as market trends, economic indicators, and investor behavior. The study highlights the difficulty in developing models that can accurately predict daily stock market movements, pointing out the limitations of artificial neural networks in capturing the multifaceted nature of financial markets (Paul, 2021).…”

Section: Limitations Observed In Neural Network Predictions For Stock...mentioning

confidence: 99%

“…Paul (2021) discusses the complexity of predicting stock market indices due to the influence of diverse factors such as market trends, economic indicators, and investor behavior. The study highlights the difficulty in developing models that can accurately predict daily stock market movements, pointing out the limitations of artificial neural networks in capturing the multifaceted nature of financial markets (Paul, 2021). While neural network models hold significant potential for stock market prediction, the limitations observed in current research underscore the need for ongoing innovation and refinement in model development.…”

Section: Limitations Observed In Neural Network Predictions For Stock...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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Measuring Factors Affecting Non-performing Assets Using Neuro Fuzzy Model

Paul¹

2022

Lecture Notes in Networks and Systems

0

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Fintech Empowers Prediction of Stock Market Index Using Artificial Neural Network

Cited by 12 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

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

Measuring Factors Affecting Non-performing Assets Using Neuro Fuzzy Model

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