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Forecasting stock price by hybrid model of cascading Multivariate Adaptive Regression Splines and Deep Neural Network
14 references·2 citing·56 second hopsampled
Forecasting stock price by hybrid model of cascading Multivariate Adaptive Regression Splines and Deep Neural Network (2021)Forecasting stock price by hybrid model o…Improving stock index forecasts by using a new weighted fuzzy-trend time series method (2017)Improving stock index forecas…Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies (2017)Deep learning networks for st…A feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction (2017)A feature weighted support ve…A comprehensive cluster and classification mining procedure for daily stock market return forecasting (2017)A comprehensive cluster and c…Forecasting movements of health-care stock prices based on different categories of news articles using multiple kernel learning (2016)Forecasting movements of heal…Forecasting daily stock market return using dimensionality reduction (2016)Forecasting daily stock marke…Application of artificial neural network for the prediction of stock market returns: The case of the Japanese stock market (2016)Application of artificial neu…An intelligent pattern recognition model for supporting investment decisions in stock market (2016)An intelligent pattern recogn…A data analytic approach to forecasting daily stock returns in an emerging market (2016)A data analytic approach to f…Stock market trend prediction using dynamical Bayesian factor graph (2015)Stock market trend prediction…Stock Return Predictability and Variance Risk Premia: Statistical Inference and International Evidence (2014)Stock Return Predictability a…A hybrid SOFM-SVR with a filter-based feature selection for stock market forecasting (2008)A hybrid SOFM-SVR with a filt…Expected returns and expected dividend growth (2005)Expected returns and expected…Forecasting with artificial neural networks: (1998)Forecasting with artificial n…A hybrid forecasting model with logistic regression and neural networks for improving key performance indicators in supply chains (2023)A hybrid forecasting model wi…A Machine Learning Method for Prediction of Stock Market Using Real-Time Twitter Data (2022)A Machine Learning Method for…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)DeepAR: Probabilistic forecasting with autoregressive recurrent networks (2019)Application of support vector machines in financial time series forecasting (2001)Financial time series forecasting using support vector machines (2003)Forecasting stock market movement direction with support vector machine (2004)A hybrid stock selection model using genetic algorithms and support vector regression (2011)A novel deep learning framework: Prediction and analysis of financial time series using CEEMD and LSTM 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media, news (2020)Time Series Analysis, Forecasting and Control. (1971)Time Series Analysis: Forecasting and Control. (1978)Support vector machine with adaptive parameters in financial time series forecasting (2003)Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction (2018)A comprehensive evaluation of ensemble learning for stock-market prediction (2020)Prediction of stock price direction using a hybrid GA-XGBoost algorithm with a three-stage feature engineering process (2021)Does the Stock Market Overreact? (1985)Hierarchical structure in financial markets (1999)Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average? (2007)A systematic review of fundamental and technical analysis of stock market predictions (2019)CNNpred: CNN-based stock market prediction using a diverse set of variables (2019)Applications of deep learning in stock market prediction: Recent progress (2021)Dynamic Conditional Correlation (2002)Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches (2008)The Econometrics of Financial Markets (2012)Good and Bad Variance Premia and Expected Returns (2017)Variance Risk Premia, Asset Predictability Puzzles, and Macroeconomic Uncertainty (2018)DeepLOB: Deep Convolutional Neural Networks for Limit Order Books (2019)Dividend Policy, Growth, and the Valuation of Shares (1961)Statistical analysis of cointegration vectors (1988)Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models (1991)Measuring and Modeling Variation in the Risk-Return Trade-off (2010)Predictive Regressions: A Present‐Value Approach (2010)On the Timing and Pricing of Dividends (2012)
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References

  • 1998Forecasting with artificial neural networks:4,225 cited
  • 2017Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies833 cited
  • 2005Expected returns and expected dividend growth433 cited
  • 2016Forecasting daily stock market return using dimensionality reduction395 cited
  • 2017A feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction382 cited
  • 2014Stock Return Predictability and Variance Risk Premia: Statistical Inference and International Evidence275 cited
  • 2008A hybrid SOFM-SVR with a filter-based feature selection for stock market forecasting257 cited
  • 2016Application of artificial neural network for the prediction of stock market returns: The case of the Japanese stock market230 cited
  • 2016A data analytic approach to forecasting daily stock returns in an emerging market111 cited
  • 2016An intelligent pattern recognition model for supporting investment decisions in stock market99 cited
  • 2017A comprehensive cluster and classification mining procedure for daily stock market return forecasting91 cited
  • 2016Forecasting movements of health-care stock prices based on different categories of news articles using multiple kernel learning83 cited
  • 2017Improving stock index forecasts by using a new weighted fuzzy-trend time series method77 cited

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2015
Stock market trend prediction using dynamical Bayesian factor graph
54 cited

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  • 2022A Machine Learning Method for Prediction of Stock Market Using Real-Time Twitter Data31 cited
  • 2023A hybrid forecasting model with logistic regression and neural networks for improving key performance indicators in supply chains19 cited