A hybrid approach by integrating wavelet-based feature extraction with MARS and SVR for stock index forecasting

Kao, Ling‐Jing; Chiu, Chih‐Chou; Lu, Chi-Jie; Chang, Cheng-Yuan

doi:10.1016/j.dss.2012.11.012

Cited by 140 publications

(60 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The key to the error correction forecasting method is how to predict the error value effectively, for the forecasting results after correction may have even bigger deviation if our error predictive value is not accurate. However, owing to the high-frequency, non-stationary and chaotic properties, it is hardly to get satisfying results for the error forecasting, therefore, to solve this problem, a extraction technique which generally utilized to extract features contained in the signals is necessary, for the forecasting model based on these features could have better performance [18][19][20] .…”

Section: Introductionmentioning

confidence: 99%

An EPC Forecasting Method for Stock Index Based on Integrating Empirical Mode Decomposition, SVM and Cuckoo Search Algorithm

Zhang

Huang

2014

Journal of Systems Science and Information

View full text Add to dashboard Cite

In order to improve the forecasting accuracy, a hybrid error-correction approach by integrating support vector machine (SVM), empirical mode decomposition (EMD) and the improved cuckoo search algorithm (ICS) was introduced in this study. By using two indexes as examples, the empirical study shows our proposed approach by means of synchronously predict the prediction error which used to correct the preliminary predicted values has better prediction precision than other five competing approaches, furthermore, the improved strategies for cuckoo search algorithm has better performance than other three evolutionary algorithms in parameters selection.

show abstract

Section: Introductionmentioning

confidence: 99%

An EPC Forecasting Method for Stock Index Based on Integrating Empirical Mode Decomposition, SVM and Cuckoo Search Algorithm

Zhang

Huang

2014

Journal of Systems Science and Information

View full text Add to dashboard Cite

show abstract

“…With the rapid advancement of machine learning technology, recent works make at-tempts to incorporate these machine learning techniques to construct trading systems that support decisions of investors in security markets (Yeh et al, 2011;Lu et al, 2009;Wen et al, 2010;Hassan, 2009;Kao et al, 2013;Kazem et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Stock Trading Using PE Ratio Based on Bayesian Inference

Wang¹,

Chatpatanasiri²,

Sattayatham³

2017

Journal of Mathematics and Statistics

View full text Add to dashboard Cite

Abstract:The Price Earnings (PE) ratio is one of the most widely applied tool for the firm valuation in a security market. Unfortunately, recent academic developments in financial econometrics and machine learning have rarely looked at this tool. In the paper, we propose to formalize a process of fundamental PE ratio estimation by employing Dynamic Bayesian Network (DBN) methodology. Forward-backward inference and Expectation Maximization (EM) parameter estimation algorithms are derived with respect to our proposed DBN structure. A simple but practical trading strategy is invented based on the result of Bayesian inference. We make stock trading experiments using Thai stocks and American stocks, respectively. Extensive experiments show that our trading strategy statistically outperforms the buy-and-hold strategy.

show abstract

“…Typically, scholars use volatility and correlation coefficients to measure the variations in a single stock index (Schwert, 2011) and the strength of the linear relationship between two or multiple cross-stock indices (Wang et al, 2011) during a given period, respectively. Some scholars use artificial neural networks (Kara et al, 2011;Ticknor, 2013), wavelet analysis (Akoum et al, 2012;Reboredo and Rivera-Castro, 2014) and certain hybrid models (Wei et al, 2011;Liu and Wang, 2012;Wang et al, 2012;Kao et al, 2013) to forecast the fluctuation of stock indices and the interactions between stock indices and other time series. However, few scholars use autoregressive sub-patterns to study the transmission of a single stock index .…”

Section: Introductionmentioning

confidence: 99%

Characteristics of the co-fluctuation matrix transmission network based on financial multi-time series

Gao

Fang

2015

Palgrave Commun

View full text Add to dashboard Cite

The co-fluctuation of two time series has often been studied by analysing the correlation coefficient over a selected period. However, in both domestic and global financial markets, there are more than two active time series that fluctuate constantly as a result of various factors, including geographic locations, information communications and so on. In addition to correlation relationships over longer periods, daily co-fluctuation relationships and their transmission features are also important, since they can present the co-movement patterns of multi-time series in detail. To capture and analyse the features of the daily co-movements of multiple financial time series and their transmission characteristics, we propose a new term-"the co-fluctuation relation matrix"-which can reveal the co-fluctuation relationships of multi-time series directly. Here, based on complex network theory, we construct a multi-time series co-fluctuation relation matrix transmission network for financial markets by taking each matrix as a node and the succeeding time sequence as an edge. To reveal the process more clearly, we utilize daily time series data for four well-known stock indices-the NASDAQ Composite (COMP), the S&P 500 Index, the Dow Jones Industrial Average and the Russell 1000 Indexfrom 22 January 2003 to 21 January 2015, to examine the concentration of the transmission networks and the roles of each matrix-in addition to the transmission relationships between the matrices-based on a variety of coefficients. We then compare our results with the statistical features of the stock indices and find that there are not many discernible patterns of co-fluctuation matrices over the 12-year period, and few of these play important roles in the transmission network. However, the conductibility of the few dominant nodes is different and reveals certain novel features that cannot be obtained by traditional statistical analysis, such as the "all positive co-fluctuation matrix", which is the most important node, although one stock index has negative correlation with the other three. This research therefore provides a novel method for analysing the co-movement behaviour of multiple financial time series, which can help researchers obtain the roles and relations of co-fluctuation patterns both over short and long terms. The findings also provide an important basis for further investigations into financial market simulations and the fluctuation of multiple financial time series.

show abstract

A hybrid approach by integrating wavelet-based feature extraction with MARS and SVR for stock index forecasting

Cited by 140 publications

References 38 publications

An EPC Forecasting Method for Stock Index Based on Integrating Empirical Mode Decomposition, SVM and Cuckoo Search Algorithm

An EPC Forecasting Method for Stock Index Based on Integrating Empirical Mode Decomposition, SVM and Cuckoo Search Algorithm

Stock Trading Using PE Ratio Based on Bayesian Inference

Characteristics of the co-fluctuation matrix transmission network based on financial multi-time series

Contact Info

Product

Resources

About