Forecasting stock market movement direction with support vector machine

By Wei Huang, Yoshiteru Nakamori, Shouyang Wang. 2005

1,131 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering, Management Science and Operations Research, Time-Series & Forecasting

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Citations in the corpus

Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets (2018, neutral): “The third one is support vectormachines (SVM).”
Using machine learning for medium frequency derivative portfolio trading (2015, neutral): “We use scikit [11] implementation of SVM with a Gaussian Kernel, own implementation of logistic regression and implementation of [10] for neural network.”
Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting (2014, neutral): “According to an extensive literature investigation, it is not difficult to find a wide variety of methodologies and techniques that have been used for stock price forecasting with various degrees of success, such as the Box–Jenkins method [1], general autoregressive conditional heteroskedasticity [2”
A Multiple-Criteria Approach for Forecasting Stock Price Direction : Nonlinear Probability Models with Application in S & P 500 Index (2016, neutral): “) have been utilized in forecasting of financial problems [9-13].”
A Comparison between Regression, Artificial Neural Networks and Support Vector Machines for Predicting Stock Market Index (2015, neutral): “These techniques includes Artificial Neural Networks (ANN) [9], [10], Fuzzy logic (FL) [11], Support Vector Machines (SVM) [12], particle swarm optimization (PSO) [13] and many others.”
Evolving Least Squares Support Vector Machines for Stock Market Trend Mining (2009, positive): “Each of the ARIMA and LDA models is estimated by in-sample data.”
Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; a Comparative Analysis (2020, methodology): “The predictability of ﬁnancial trend with SVM model by evaluating the weekly trend of NIKKEI 225 index was investigated by Huang et al. [8].”
A Feature Fusion Based Forecasting Model for Financial Time Series (2014, neutral): “The variables are shown in publications [48],[49].”
Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model (2016, positive): “Huang, Nakamori [9] forecasted stock market movement using support vector machines (SVM), and concluded that the model was good at predicting the direction.”
Empirical analysis: stock market prediction via extreme learning machine (2014, neutral): “[26] predict the price directional movement of NIKKEI 225 index using SVM.”
Understanding Machine-learned Density Functionals (2014, neutral): “[33] Wei Huang, Yoshiteru Nakamori, and Shou-Yang Wang, “Forecasting stock market movement direction with support vector machine,” Computers & Operations Research 32, 2513–2522 (2005).”