Financial time series forecasting using support vector machines

By Kyoung-jae Kim. 2003

1,741 citations recorded by the source corpus.

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

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Electrical and Electronic Engineering

In Management Science and Operations Research

In Time-Series & Forecasting

Citations in the corpus

Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting (2019, methodology): “For a comprehensive treatment of traditional methods, we refer the readers to [13, 19, 4, 18, 12] and the references there in.”
Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets (2018, background): “Many fund managers and investors in the stock market generally accept and use certain criteria for technical indicators as the signal of future market trends [12, 41].”
Stock Price Prediction by Deep Neural Generative Model of News Articles (2018, methodology): “, support vector machines (SVMs) [3], [4], [9], [10] and multilayer perceptrons (MLPs) [11]–[14].”
Financial Time Series Prediction Using Deep Learning (2017, methodology): “Decision Trees [13] and SVM [14, 15] were also applied to time series forecasting.”
Prediction of Human Activity by Discovering Temporal Sequence Patterns (2014, background): “Concepts “action” and “event” are always interchangeably used in computer vision and other AI fields.”
Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters (2005, methodology): “…approach has been introduced to several ﬁnancial applications such as credit rating, time series prediction, and insurance claim fraud detection (Fan & Palaniswami, 2000; Gestel et al., 2001; Huang, Chen, Hsu, Chen, & Wu, 2004; Kim, 2003; Tay & Cao, 2001; Viaene, Derrig, Baesens, & Dedene, 2002).”
Support vector regression with chaos-based firefly algorithm for stock market price forecasting (2013, background): “…of Software, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia c Islamic Azad University, Tafresh Branch, Young Researchers Club, Tafresh, Iran d School of Information Systems, Curtin University, Perth, WA, Australia a r t i c l e i n f o”
Stock market's price movement prediction with LSTM neural networks (2017, background): “They go from evolutionary computation through genetic algorithms as exemplified in [7], statistical learning by using algorithms like Support Vector Machines (SVM) [8] and a variety of others including neural networks, component modeling, textual analysis based on news data, that are discussed by [9”
Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange (2011, methodology): “Kim (2003) used SVM to predict the direction of daily stock price change in the Korea composite stock price index (KOS- PI).”
CNNpred: CNN-based stock market prediction using a diverse set of variables (2019, background): “So, most of the existing works in this ﬁeld have limited themselves to a set of technical indicators representing a single market ' s recent history (Kim, 2003; Zhang & Wu, 2009).”
Machine Learning Techniques and Use of Event Information for Stock Market Prediction: A Survey and Evaluation (2005, background): “Recently, several applications of SVM to financial forecasting problems have been reported [15] [38] [39].”
Empirical analysis: stock market prediction via extreme learning machine (2014, methodology): “For predicting index prices, Kim [23] concludes that the performance of SVM is better than that of BP-NN.”
Plato (2020, positive): “For example, polynomials and ARMA models are better at modeling data from physical processes such as temperature [42, 12], while Gaussian functions are better for modeling relatively randomized data [33] such as stock prices.”
Machine learning in cell biology – teaching computers to recognize phenotypes (2013, background): “…Guigo´, 2004; Ben-Hur et al., 2008) and proteomics (Yang and Chou, 2004; Datta and Pihur, 2010; Reiter et al., 2011), as well as in many other fields outside of biology, such as speech (Rabiner, 1989) and face recognition (Viola and Jones, 2004), and prediction of stock market trends (Kim, 2003).”
Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches (2010, background): “Work Dataset Prediction model Input variables Feature selection Huang and Tsai (2009) [15] Taiwan index futures (FITX) A hybrid SOMa-SVRb model 13 Technical indexes Filter-based feature selection Lai et al., (2009) [28] Taiwan Stock Exchange Corporation K-means, GA-based fuzzy decision tree 7 Techni”