Application of support vector machines in !nancial time series forecasting

By Lijuan Cao. 2001

1,029 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering, Management Science and Operations Research

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

1. Kyoung-jae Kim
2. Xiaodong Li
3. Jae H. Min
4. Young-Chan Lee
5. Yosi Keller
6. A. Navon
7. Chetan Rao
8. Abhijit Sharang

In Control and Systems Engineering

1. Xiaodong Li
2. Chetan Rao
3. Abhijit Sharang
4. Orlando Belo
5. Yosi Keller
6. Jae H. Min
7. Shom Prasad Das
8. Young-Chan Lee

In Management Science and Operations Research

1. Kyoung-jae Kim
2. Xiaodong Li
3. Ravi Sankar
4. Nicholas I. Sapankevych
5. Takashi Matsubara
6. Ryo Akita
7. Yosi Keller
8. A. Navon

Citations in the corpus

• 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): “The classification probability estimate of the SVM was used by the proposed Probabilistic Trading Strategy.”
• Empirical analysis: stock market prediction via extreme learning machine (2014, methodology): “To address this issue, we follow Cao and Tay [20, 35] approach, which uses RDP indicators to summarize the short price series.”
• A Review of Deep Learning Models for Time Series Prediction (2019, methodology): “1) RepresentativeClassicalModel-SupportVectorMachine: Support vector machine (SVM), proposed by Vapnik [36] in 1995, is a supervised kernel-based method and always applied to nonlinear time series prediction [37]–[41].”
• An application of support vector machines in bankruptcy prediction model (2005, methodology): “These results partly support the conclusions of Tay and Cao (2001).”
• Optimal partition algorithm of the RBF neural network and its application to financial time series forecasting (2005, background): “There is growing interest both in the field of neural computing and in the financial world in explaining the use of neural networks to forecast the future changes in prices of stocks, exchange rates, commodities, and other financial time series [4–11].”
• Automated trading with performance weighted random forests and seasonality (2014, methodology): “It has since been used in many studies to predict the daily stock price movements and has been shown to outperform neural networks (Tay and Cao, 2001; Kim, 2003; Huang et al., 2005; Chen and Shih, 2006).”
• Empirical mode decomposition–based least squares support vector regression for foreign exchange rate forecasting (2012, methodology): “Following Lu et al. (2009) and Tay and Cao (2001), the following performance measures are used and evaluated respectively: applied mean absolute percentage error (MAPE), root‐mean‐square error (RMSE), mean absolute difference (MAD), directional symmetry (DS), correct uptrend (CP), and correct…”
• Support Vector Machines for Prediction of Futures Prices in Indian Stock Market (2012, methodology): “SVMs forecast better than that BPN, as shown in our results, SVM provides a smaller NMSE (Normalized Mean Square Error) and MAE (Mean Absolute Error) and larger DS (Directional Symmetry) than of BPN in most of the cases.”