Application of Support Vector Machines in Financial Time Series Forecasting

By Wu Meng. 2007

358 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/41acd9b936bb09ae6e271b29f60981c4c4ddc85f

How researchers see this paper

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

Citations in the corpus

Enhancing quantitative intra-day stock return prediction by integrating both market news and stock prices information (2014, background): “Tay et al. [42] [43] and Cao et al. [3] modify the SVM objective function and make C an adaptive parameter for nonstationary time series.”
Using machine learning for medium frequency derivative portfolio trading (2015, methodology): “Most prior work in this area concentrates on using popular ML techniques like SVM [7], [12], [9] and neural networks [8], [3], [13] coupled with rigorously designed features, and the general area of focus is financial time series forecasting.”
A multiple-kernel support vector regression approach for stock market price forecasting (2011, methodology): “Given the original daily stock closing prices p = {p 1 , p 2 , . . . , p t , . . .}, we follow (Tay & Cao, 2001) to derive training patterns (x t , y t ) for SKSVR and MKSVR. Firstly, the n-day exponential moving average of the tth day, EMA n (t), is defined as EMA n ðtÞ ¼ EMA n ðt À 1Þ þ a Â ðp t…”
Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey (2010, unknown): “Finally, in a few applications the deposits are included as both an input and an output (e.g. Tortosa-Ausina, 2002a).”
Deep Learning in Medical Imaging: General Overview (2017, methodology): “SVR is a version of SVM for regression (8), and has shown reliable performance in forecasting weather and financial data (9, 10).”
Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model (2016, methodology): “By reviewing previously published studies [7, 10, 11, 25, 39], we set 13 technical indicators as Type 1 feature subset and 9 technical indicators as Type 2 feature subset.”
Textual analysis of stock market prediction using breaking financial news: The AZFin text system (2009, methodology): “18 Article and stock quote data was then processed by a Support Vector Machine derivative, using Sequential Minimal Optimization [21] in a form of regression, which can handle discrete number analysis [24].”
An Adaptive Network-Based Fuzzy Inference System (ANFIS) for the prediction of stock market return: The case of the Istanbul Stock Exchange (2010, unknown): “…, & Ragusa, 2002; Manish & Thenmozhi, 2006; Oh & Kim, 2002; Olson & Mossman, 2003; Pai & Lin, 2005; Saad, Prokhorov, & Wunsch, 1998; Takahashi, Tamada, & Nagasaka, 1998; Tan, Quek, & See Ng, 2007; Tay & Cao, 2001; Tay & Cao, 2002; Yao, Chew, & Poh, 1999; Yoon & Swales, 1991; Yudong & Lenan, 2009).”
Evaluating sentiment in financial news articles (2012, methodology): “A similar prediction method was employed in the forecasting of futures contracts (Tay and Cao, 2001).”
A Hybrid Machine Learning System for Stock Market Forecasting (2008, methodology): “SVMs were first used by Tay & Cao for financial time series forecasting [13], [14], [15].”
A Review of Deep Learning Models for Time Series Prediction (2019, unknown): “applied to nonlinear time series prediction [37]–[41].”
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].”
Support vector regression with genetic algorithms in forecasting tourism demand (2007, methodology): “This balance eventually leads to better generalization performance than other neural network models (Tay & Cao, 2001).”