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Predicting the daily return direction of the stock market using hybrid machine learning algorithms
11 references·16 citing·44 second hop
Predicting the daily return direction of the stock market using hybrid machine learning algorithms (2019)Predicting the daily return direction of …Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies (2017)Deep learning networks for st…Forecasting daily stock market return using dimensionality reduction (2016)Forecasting daily stock marke…Surveying stock market forecasting techniques – Part II: Soft computing methods (2008)Surveying stock market foreca…The use of data mining and neural networks for forecasting stock market returns (2005)The use of data mining and ne…Time series forecasting using a hybrid ARIMA and neural network model (2003)Time series forecasting using…Neural network techniques for financial performance prediction: integrating fundamental and technical analysis (2003)Neural network techniques for…A hybrid genetic-neural architecture for stock indexes forecasting (2003)A hybrid genetic-neural archi…Application of neural networks to an emerging financial market: forecasting and trading the Taiwan Stock Index (2002)Application of neural network…Financial Forecasting Using Support Vector Machines (2001)Financial Forecasting Using S…Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index (2000)Genetic algorithms approach t…Forecasting with artificial neural networks: (1998)Forecasting with artificial n…Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications (2023)Forecasting Stock Market Pric…An Overview of Machine Learning, Deep Learning, and Reinforcement Learning-Based Techniques in Quantitative Finance: Recent Progress and Challenges (2023)An Overview of Machine Learni…Multi-scale local cues and hierarchical attention-based LSTM for stock price trend prediction (2022)Multi-scale local cues and hi…A New Stock Price Forecasting Method Using Active Deep Learning Approach (2022)A New Stock Price Forecasting…The applications of artificial neural networks, support vector machines, and long–short term memory for stock market prediction (2021)The applications of artificia…Multivariate CNN‐LSTM Model for Multiple Parallel Financial Time‐Series Prediction (2021)Multivariate CNN‐LSTM Model f…Forecasting directional movement of Forex data using LSTM with technical and macroeconomic indicators (2021)Forecasting directional movem…Effectiveness of Artificial Intelligence in Stock Market Prediction based on Machine Learning (2021)Effectiveness of Artificial I…Artificial Intelligence Applied to Stock Market Trading: A Review (2021)Artificial Intelligence Appli…An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination (2021)An efficient stock market pre…Training Multilayer Perceptron with Genetic Algorithms and Particle Swarm Optimization for Modeling Stock Price Index Prediction (2020)Training Multilayer Perceptro…Portfolio selection: a fuzzy-ANP approach (2020)Portfolio selection: a fuzzy-…Machine Learning Stock Market Prediction Studies: Review and Research Directions (2020)Machine Learning Stock Market…Forecasting Stock Prices Using a Hybrid Deep Learning Model Integrating Attention Mechanism, Multi-Layer Perceptron, and Bidirectional Long-Short Term Memory Neural Network (2020)Forecasting Stock Prices Usin…Forecasting daily stock trend using multi-filter feature selection and deep learning (2020)Forecasting daily stock trend…Evaluation of forecasting methods from selected stock market returns (2019)Evaluation of forecasting met…Time Series Analysis: Forecasting and Control (1977)Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)Managerial Applications of Neural Networks: The Case of Bank Failure Predictions (1992)Financial time series forecasting using support vector machines (2003)DeepAR: Probabilistic forecasting with autoregressive recurrent networks (2019)Mutual Fund Performance (1966)Modern Portfolio Theory and Investment Analysis. 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this work references cited by second hop

References

  • 2003Time series forecasting using a hybrid ARIMA and neural network model4,469 cited
  • 1998Forecasting with artificial neural networks:4,225 cited
  • 2008Surveying stock market forecasting techniques – Part II: Soft computing methods841 cited
  • 2017Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies833 cited
  • 2000Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index758 cited
  • 2002Application of neural networks to an emerging financial market: forecasting and trading the Taiwan Stock Index527 cited
  • 2005The use of data mining and neural networks for forecasting stock market returns465 cited
  • 2016Forecasting daily stock market return using dimensionality reduction395 cited
  • 2001Financial Forecasting Using Support Vector Machines392 cited
  • 2003Neural network techniques for financial performance prediction: integrating fundamental and technical analysis380 cited
  • 2003A hybrid genetic-neural architecture for stock indexes forecasting320 cited

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  • 2023Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications

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  • 2021Multivariate CNN‐LSTM Model for Multiple Parallel Financial Time‐Series Prediction136 cited
  • 2020Portfolio selection: a fuzzy-ANP approach123 cited
  • 2020Forecasting Stock Prices Using a Hybrid Deep Learning Model Integrating Attention Mechanism, Multi-Layer Perceptron, and Bidirectional Long-Short Term Memory Neural Network111 cited
  • 2021An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination96 cited
  • 2021Effectiveness of Artificial Intelligence in Stock Market Prediction based on Machine Learning93 cited
  • 2019Evaluation of forecasting methods from selected stock market returns92 cited
  • 2020Training Multilayer Perceptron with Genetic Algorithms and Particle Swarm Optimization for Modeling Stock Price Index Prediction82 cited
  • 2022Multi-scale local cues and hierarchical attention-based LSTM for stock price trend prediction73 cited
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