Integration of nonlinear independent component analysis and support vector regression for stock price forecasting

Kao, Ling‐Jing; Chiu, Chih‐Chou; Lu, Chi-Jie; Yang, Jung-Li

doi:10.1016/j.neucom.2012.06.037

Cited by 123 publications

(62 citation statements)

References 39 publications

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“…SVR has been successfully applied in different problems of time-series prediction such as demand forecasting, traffic flow prediction, and financial time series forecasting [7].…”

Section: Introductionmentioning

confidence: 99%

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Integration of mutual information and CRPSO-based fuzzy model for stock index forecasting

Yu¹,

Kim²

2015

Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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In this paper, the integration of mutual information (MI) and fuzzy model is proposed to predict stock indexes with complex and non-linear characteristics. Technical indicators are considered as initial input candidates and significant inputs are determined by MI-based input selection method. To identify the structures and parameters of fuzzy models simultaneously, cooperative random learning particle swarm optimization (CRPSO), proposed by Zhao et al., is used. To confirm the effectiveness, the proposed method and comparison methods are applied to the Korea Composite Stock Price Index (KOSPI). The experimental results show that the proposed method, on average, outperforms other comparison methods.

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“…SVR has been successfully applied in different problems of time-series prediction such as demand forecasting, traffic flow prediction, and financial time series forecasting [7].…”

Section: Introductionmentioning

confidence: 99%

“…Kao et al [7] developed a stock price forecasting model, NLICA-SVR, which first uses NLICA for pre-processing to extract features from forecasting variables. Xiong et al [8] investigated the possibility of forecasting an interval-valued stock price index series over short and long horizons using multioutput SVR.…”

Section: Introductionmentioning

confidence: 99%

Integration of mutual information and CRPSO-based fuzzy model for stock index forecasting

Yu¹,

Kim²

2015

Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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“…Song and Chissom (1993a;1993b; have studied fuzzy application to predict university enrollment. Time series using fuzzy model has been applied to predict peak load electricity demand (Ismail et al, 2009) and has been applied to predict stock price (Egrioglu, 2014;Kao et al, 2013;Nurhayadi et al, 2014;Singh and Borah, 2014). Rodger (2014) has used fuzzy model to predict the need of natural gas and the energy cost savings in public buildings.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Model Optimization for Time Series Data Using a Translation in the Extent of Mean Error

Nurhayadi¹,

Subanar²,

Abdurakhman³

et al. 2014

Journal of Mathematics and Statistics

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Recently, many researchers in the field of writing about the prediction of stock price forecasting, electricity load demand and academic enrollment using fuzzy methods. However, in general, modeling does not consider the model position to actual data yet where it means that error is not been handled optimally. The error that is not managed well can reduce the accuracy of the forecasting. Therefore, the paper will discuss reducing error using model translation. The error that will be reduced is Mean Square Error (MSE). Here, the analysis is done mathematically and the empirical study is done by applying translation to fuzzy model for enrollment forecasting at the Alabama University. The results of this analysis show that the translation in the extent of mean error can reduce the MSE.

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“…This is because that stock market is noisy, chaotic, nonparametric and non-linear in nature, and many external entities like politics, human psychology/behavior, liquid money and related news influence the direction of the stock market (Abu-Mostafa and Atiya, 1996). Recently, a lot of interesting work has been carrying on in the area of applying machine learning algorithms, including support vector machine (SVM), for analyzing price patterns and predicting stock prices and index changes (Yang et al, 2002;Grosan and Abraham, 2006;Chen et al, 2006;Sapankevych and Sankar, 2009;Kao et al, 2013;Kazem et al, 2013;Zhi-gang et al, 2013). The advantage of SVM is that it is able to reach the global optimum and is resistant to the undertraining or overtraining problems (Yoo et al, 2005;Chen et al, 2006;Sapankevych and Sankar, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…The advantage of SVM is that it is able to reach the global optimum and is resistant to the undertraining or overtraining problems (Yoo et al, 2005;Chen et al, 2006;Sapankevych and Sankar, 2009). This machine learning method has been successfully used for stock return predictions in several financial areas (Yang et al, 2002;Chen et al, 2006;Sapankevych and Sankar, 2009;Kao et al, 2013;Kazem et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Moving Direction of Google Inc. Stock Price Using Support Vector Classification and Regression

Li¹,

Liu²

2014

AJFA

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Forecasting the short-term trend of a stock market has long been a big challenging task. Parameters of stock markets, including open/close prices, daily-high/low prices and trading volumes, were frequently used in previous studies to forecast the stock market. Basing on the fact that the moving direction of these parameters have certain inertia within short-term period, we here explored the potential application of the moving trends of these parameters within 4 different time periods (5, 15, 30 and 45 trading days respectively) for forecasting the movement direction of stock price of Google Inc. by using support vector classification (SVC) and support vector regression (SVR). We found that among the 4 different time periods tested, the moving trend within 30 days has the best accuracy on the prediction of the stock price of Google Inc., and using SVC and SVR combination improved the prediction performance. These results indicated that moving trends of stock transaction data within a certain time period have good inertia and are thus useful for forecasting the moving direction of stock price.

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Integration of nonlinear independent component analysis and support vector regression for stock price forecasting

Cited by 123 publications

References 39 publications

Integration of mutual information and CRPSO-based fuzzy model for stock index forecasting

Integration of mutual information and CRPSO-based fuzzy model for stock index forecasting

Fuzzy Model Optimization for Time Series Data Using a Translation in the Extent of Mean Error

Prediction of the Moving Direction of Google Inc. Stock Price Using Support Vector Classification and Regression

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