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Comparing Machine Learning Methods—SVR, XGBoost, LSTM, and MLP— For Forecasting the Moroccan Stock Market
10 references·1 citing·48 second hop
Comparing Machine Learning Methods—SVR, XGBoost, LSTM, and MLP— For Forecasting the Moroccan Stock Market (2023)Comparing Machine Learning Methods—SVR, X…Machine learning approaches in stock market prediction: A systematic literature review (2023)Machine learning approaches i…Stock Market Prediction Using Microblogging Sentiment Analysis and Machine Learning (2022)Stock Market Prediction Using…Forecasting stock market for an efficient portfolio by combining XGBoost and Hilbert–Huang transform (2022)Forecasting stock market for …Forecasting Selected Colombian Shares Using a Hybrid ARIMA-SVR Model (2022)Forecasting Selected Colombia…A graph-based CNN-LSTM stock price prediction algorithm with leading indicators (2021)A graph-based CNN-LSTM stock …Deep learning for financial applications : A survey (2020)Deep learning for financial a…A CNN-LSTM-Based Model to Forecast Stock Prices (2020)A CNN-LSTM-Based Model to For…Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data (2019)Forecasting stock prices with…A Comparative Study of SVM and LSTM Deep Learning Algorithms for Stock Market Prediction. (2019)A Comparative Study of SVM an…A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring (2017)A boosted decision tree appro…Forecasting International Stock Market Trends: XGBoost, LSTM, LSTM-XGBoost, and Backtesting XGBoost Models (2023)Forecasting International Sto…FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY (1968)A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting (1997)Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research (2015)Statistical and machine learning models in credit scoring: A systematic literature survey (2020)CatBoost model and artificial intelligence techniques for corporate failure prediction (2021)Forecasting gold price with the XGBoost algorithm and SHAP interaction values (2021)Efficient Capital Markets: A Review of Theory and Empirical Work (1970)EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK* (1970)The Cross-Section of Expected Stock Returns (1992)A hybrid stock price index forecasting model based on variational mode decomposition and LSTM network (2020)Fusion in stock market prediction: A decade survey on the necessity, recent developments, and potential future directions (2020)A stock price prediction method based on deep learning technology (2021)Textual Analysis in Accounting and Finance: A Survey (2016)Deep learning with long short-term memory networks for financial market predictions (2017)A novel graph convolutional feature based convolutional neural network for stock trend prediction (2020)Generación de series de tiempo financieras sintéticas para "data augmentation" usando redes neuronales generativas adversarias (GAN) (2021)Financial applications of machine learning: A literature review (2023)An Empirical Comparison of Machine Learning Models for Time Series Forecasting (2010)Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques (2014)Stock Market Prediction Using Machine Learning (2018)Stock Market Prediction Using Machine Learning and Deep Learning Techniques: A Review (2025)Time series forecasting using a hybrid ARIMA and neural network model (2003)Stock Price Prediction Using the ARIMA Model (2014)Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models (2018)The applications of artificial neural networks, support vector machines, and long–short term memory for stock market prediction (2021)Cryptocurrency trading: a comprehensive survey (2022)Machine learning techniques and data for stock market forecasting: A literature review (2022)The use of technical analysis in the foreign exchange market (1992)A deep learning framework for financial time series using stacked autoencoders and long-short term memory (2017)A CNN-LSTM based deep learning model with high accuracy and robustness for carbon price forecasting: A case of Shenzhen's carbon market in China (2024)Data-driven stock forecasting models based on neural networks: A review (2024)Sentiment analysis of Twitter data for predicting stock market movements (2016)The impact of microblogging data for stock market prediction: Using Twitter to predict returns, volatility, trading volume and survey sentiment indices (2016)Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; a Comparative Analysis (2020)Deep LSTM and LSTM-Attention Q-learning based reinforcement learning in oil and gas sector prediction (2023)Financial stock market forecast using evaluated linear regression based machine learning technique (2023)Enhancing the Prediction of Stock Market Movement Using Neutrosophic-Logic-Based Sentiment Analysis (2024)Forecasting with artificial neural networks: (1998)Comparing Predictive Accuracy (2002)PORTFOLIO SELECTION* (1952)CNNpred: CNN-based stock market prediction using a diverse set of variables (2019)ESG rating results and corporate total factor productivity (2024)Hybrid of jellyfish and particle swarm optimization algorithm-based support vector machine for stock market trend prediction (2024)Stock market's price movement prediction with LSTM neural networks (2017)Stock price prediction using LSTM, RNN and CNN-sliding window model (2017)A stock series prediction model based on variational mode decomposition and dual-channel attention network (2023)SMP-DL: a novel stock market prediction approach based on deep learning for effective trend forecasting (2023)Enhancing stock market Forecasting: A hybrid model for accurate prediction of S&P 500 and CSI 300 future prices (2024)
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References

  • 2017A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring770 cited
  • 2020A CNN-LSTM-Based Model to Forecast Stock Prices564 cited
  • 2020Deep learning for financial applications : A survey550 cited
  • 2019Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data342 cited
  • 2021A graph-based CNN-LSTM stock price prediction algorithm with leading indicators332 cited
  • 2023Machine learning approaches in stock market prediction: A systematic literature review174 cited
  • 2022Forecasting stock market for an efficient portfolio by combining XGBoost and Hilbert–Huang transform91 cited
  • 2022Stock Market Prediction Using Microblogging Sentiment Analysis and Machine Learning88 cited
  • 2022Forecasting Selected Colombian Shares Using a Hybrid ARIMA-SVR Model58 cited
  • 2019A Comparative Study of SVM and LSTM Deep Learning Algorithms for Stock Market Prediction.51 cited

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  • 2023Forecasting International Stock Market Trends: XGBoost, LSTM, LSTM-XGBoost, and Backtesting XGBoost Models48 cited

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