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A study on novel filtering and relationship between input-features and target-vectors in a deep learning model for stock price prediction
5 references·11 citing·50 second hop
A study on novel filtering and relationship between input-features and target-vectors in a deep learning model for stock price prediction (2018)A study on novel filtering and relationsh…Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets (2018)Stock Market Prediction on Hi…Deep learning for stock prediction using numerical and textual information (2016)Deep learning for stock predi…Deep learning for event-driven stock prediction (2015)Deep learning for event-drive…Market Statistics and Technical Analysis: The Role of Volume (1994)Market Statistics and Technic…Simple Technical Trading Rules and the Stochastic Properties of Stock Returns (1992)Simple Technical Trading Rule…Stock Market Prediction Using Machine Learning and Deep Learning Techniques: A Review (2025)Stock Market Prediction Using…Key technical indicators for stock market prediction (2025)Key technical indicators for …Machine learning in business and finance: a literature review and research opportunities (2024)Machine learning in business …Climate change attention and carbon futures return prediction (2023)Climate change attention and …Stock Market Forecasting Using the Random Forest and Deep Neural Network Models Before and During the COVID-19 Period (2022)Stock Market Forecasting Usin…House price prediction using hedonic pricing model and machine learning techniques (2022)House price prediction using …Forecasting stock market for an efficient portfolio by combining XGBoost and Hilbert–Huang transform (2022)Forecasting stock market for …Dynamic stock-decision ensemble strategy based on deep reinforcement learning (2022)Dynamic stock-decision ensemb…Jointly modeling transfer learning of industrial chain information and deep learning for stock prediction (2021)Jointly modeling transfer lea…Generative adversarial network (GAN) and enhanced root mean square error (ERMSE): deep learning for stock price movement prediction (2021)Generative adversarial networ…Financial quantitative investment using convolutional neural network and deep learning technology (2019)Financial quantitative invest…On the Impossibility of Informationally Efficient Markets (1980)On the Impossibility of Informationally Efficient Markets (1980)The Relation Between Price Changes and Trading Volume: A Survey (1987)Option Volume and Stock Prices: Evidence on Where Informed Traders Trade (1998)Data‐Snooping, Technical Trading Rule Performance, and the Bootstrap (1999)Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation (2000)The Behavior of Stock-Market Prices (1965)Does the Stock Market Overreact? (1985)Conditional Heteroskedasticity in Asset Returns: A New Approach (1991)Market underreaction to open market share repurchases (1995)Do Investors Trade Too Much? 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this work references cited by second hop

References

  • 1992Simple Technical Trading Rules and the Stochastic Properties of Stock Returns2,298 cited
  • 1994Market Statistics and Technical Analysis: The Role of Volume1,105 cited
  • 2015Deep learning for event-driven stock prediction675 cited
  • 2016Deep learning for stock prediction using numerical and textual information420 cited
  • 2018Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets262 cited

Cited by

  • 2024Machine learning in business and finance: a literature review and research opportunities96 cited
  • 2022Forecasting stock market for an efficient portfolio by combining XGBoost and Hilbert–Huang transform91 cited
  • 2021Generative adversarial network (GAN) and enhanced root mean square error (ERMSE): deep learning for stock price movement prediction68 cited
  • 2022House price prediction using hedonic pricing model and machine learning techniques67 cited
  • 2019Financial quantitative investment using convolutional neural network and deep learning technology66 cited
  • 2023Climate change attention and carbon futures return prediction55 cited
  • 2021Jointly modeling transfer learning of industrial chain information and deep learning for stock prediction54 cited
  • 2022Stock Market Forecasting Using the Random Forest and Deep Neural Network Models Before and During the COVID-19 Period49 cited

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2025
Stock Market Prediction Using Machine Learning and Deep Learning Techniques: A Review
45 cited
  • 2022Dynamic stock-decision ensemble strategy based on deep reinforcement learning36 cited
  • 2025Key technical indicators for stock market prediction35 cited