A multiple-kernel support vector regression approach for stock market price forecasting

Support vector regression has been applied to stock market forecasting problems. However, it is usually needed to tune manually the hyperparameters of the kernel functions. Multiple-kernel learning was developed to deal with this problem, by which the kernel matrix weights and Lagrange multipliers can be simultaneously derived through semidefinite programming. However, the amount of time and space required is very demanding. We develop a two-stage multiple-kernel learning algorithm by incorporating sequential minimal optimization and the gradient projection method. By this algorithm, advantages from different hyperparameter settings can be combined and overall system performance can be improved. Besides, the user need not specify the hyperparameter settings in advance, and trial-and-error for determining appropriate hyperparameter settings can then be avoided. Experimental results, obtained by running on datasets taken from Taiwan Capitalization Weighted Stock Index, show that our method performs better than other methods.     

A two-stage architecture for stock price forecasting by integrating self-organizing map and support vector regression

Stock price prediction has attracted much attention from both practitioners and researchers. However, most studies in this area ignored the non-stationary nature of stock price series. That is, stock price series do not exhibit identical statistical properties at each point of time. As a result, the relationships between stock price series and their predictors are quite dynamic. It is challenging for any single artificial technique to effectively address this problematic characteristics in stock price series. One potential solution is to hybridize different artificial techniques. Towards this end, this study employs a two-stage architecture for better stock price prediction. Specifically, the self-organizing map (SOM) is first used to decompose the whole input space into regions where data points with similar statistical distributions are grouped together, so as to contain and capture the non-stationary property of financial series. After decomposing heterogeneous data points into several homogenous regions, support vector regression (SVR) is applied to forecast financial indices. The proposed technique is empirically tested using stock price series from seven major financial markets. The results show that the performance of stock price prediction can be significantly enhanced by using the two-stage architecture in comparison with a single SVR model.     

Application of feature-weighted Support Vector regression using grey correlation degree to stock price forecasting

A feature-weighted Support Vector Machine regression algorithm is introduced in this paper. We note that the classical SVM is based on the assumption that all the features of the sample points supply the same contribution to the target output value. However, this assumption is not always true in real problems. In the proposed new algorithm, we give different weight values to different features of the samples in order to improve the performance of SVM. In our algorithm, firstly, a measure named grey correlation degree is applied to evaluate the correlation between each feature and the target problem, and then the values of the grey correlation degree are used as weight values assigned to the features. The proposed method is tested on sample stock data sets selected from China Shenzhen A-share market. The result shows that the new version of SVM can improve the accuracy of the prediction.

     

公路旅游客流量预测的支持向量回归模型

介绍了基于统计学习理论的支持向量机回归原理,为解决公路旅游客流量预测建模中的小样本问题,实现对公路旅游客流量的快速准确预测,提出了基于支持向量机回归模型的公路旅游客流量预测方法,给出了参数优化选取算法。仿真实验表明,该方法具有比神经网络等方法更好的预测精度。说明支持向量回归方法用于公路旅游客流量预测是可行有效的。     

Incorporating feature selection method into support vector regression for stock index forecasting

Stock index forecasting is one of the most difficult tasks that financial organizations, firms and private investors have to face. Support vector regression (SVR) has become a popular alternative in stock index forecasting tasks due to its generalization capability in obtaining a unique solution. However, the major limitation of SVR is that it cannot capture the relative importance of independent variables to the dependent variable when many potential independent variables are considered. This study incorporates feature selection method and SVR for building stock index forecasting model. The proposed model uses multivariate adaptive regression splines (MARS), an effective nonlinear and nonparametric regression methodology, to identify important forecasting variables. The obtained significant predictor variables are then served as the inputs for the SVR model. Experimental results reveal that the obtained important variables from MARS can improve the forecasting performance of the SVR models. Moreover, the MARS results provide useful information about the relationship between the selected predictor variables and stock index through the obtained basis functions, important predictor variables and the MARS prediction function. Hence, the proposed stock index forecasting model can generate good forecasting performance and exhibits the capability of identifying significant predictor variables, which provide valuable information for further investment decisions/strategies.     

Short term forecasting with support vector machines and application to stock price prediction

Forecasting a stock price movement is one of the most difficult problems in finance. The reason is that financial time series are complex, non stationary. Furthermore, it is also very difficult to predict this movement with parametric models. Instead of parametric models, we propose two techniques, which are data driven and non parametric. Based on the idea that excess returns would be possible with publicly available information, we developed two models in order to forecast the short term price movements by using technical indicators. Our assumption is that the future value of a stock price depends on the financial indicators although there is no parametric model to explain this relationship. This relationship comes from the technical analysis. Comparison shows that support vector regression (SVR) out performs the multi layer perceptron (MLP) networks for a short term prediction in terms of the mean square error. If the risk premium is used as a comparison criterion, then the SVR technique is as good as the MLP method or better.     

A dynamic trading rule based on filtered flag pattern recognition for stock market price forecasting

In this paper we propose and validate a trading rule based on flag pattern recognition, incorporating important innovations with respect to the previous research. Firstly, we propose a dynamic window scheme that allows the stop loss and take profit to be updated on a quarterly basis. In addition, since the flag pattern is a trend-following pattern, we have added the EMA indicator to filter trades. This technical analysis indicator is calculated both for 15-min and 1-day timeframes, which enables short and medium terms to be considered simultaneously. We also filter the flags according to the price range on which they are developed and have limited the maximum loss of each trade to 100 points. The proposed methodology was applied to 91,309 intraday observations of the DJIA index, considerably improving the results obtained in the previous proposals and those obtained by the buy & hold strategy, both for profitability and risk, and also after taking into account the transaction costs. These results seem to challenge market efficiency in line with other similar studies, in the specific analysis carried out on the DJIA index and is also limited to the setup considered.     

Predicting the blast-induced vibration velocity using a bagged support vector regression optimized with firefly algorithm

Ground vibration is the most detrimental effect induced by blasting in surface mines. This study presents an improved bagged support vector regression (BSVR) combined with the firefly algorithm (FA) to predict ground vibration. In other words, the FA was used to modify the weights of the SVR model. To verify the validity of the BSVR–FA, the back-propagation neural network (BPNN) and radial basis function network (RBFN) were also applied. The BSVR–FA, BPNN and RBFN models were constructed using a comprehensive database collected from Shur River dam region, in Iran. The proposed models were then evaluated by means of several statistical indicators such as root mean square error (RMSE) and symmetric mean absolute percentage error. Comparing the results, the BSVR–FA model was found to be the most accurate to predict ground vibration in comparison to the BPNN and RBFN models. This study indicates the successful application of the BSVR–FA model as a suitable and effective tool for the prediction of ground vibration.

     

Hybridizing nonlinear independent component analysis and support vector regression with particle swarm optimization for stock index forecasting

One of the major activities of financial firms and private investors is to predict future prices of stocks. However, stock index prediction is regarded as a challenging task of the prediction problem since the stock market is a complex, chaotic and nonlinear dynamic system. As stock markets are highly dynamic and exhibit wide variation, it may be more realistic and practical that assumed the stock index data are a nonlinear mixture data. In this study, a hybrid stock index prediction model by utilizing nonlinear independent component analysis (NLICA), support vector regression (SVR) and particle swarm optimization (PSO) is proposed. In the proposed model, first, the NLICA is used to deal with the nonlinearity property of the stock index data. The proposed model utilizes NLICA to extract features from the observed stock index data. The features which can be used to represent underlying/hidden information of the data are then served as the inputs of SVR to build the stock index prediction model. Finally, PSO is applied to optimize the parameters of the SVR prediction model since the parameters of SVR must be carefully selected in establishing an effective and efficient SVR model. In order to evaluate the performance of the proposed approach, the closing indexes of the Taiwan stock exchange capitalization weighted stock index, Shanghai stock exchange composite index and Bombay stock exchange index are used as illustrative examples. Experimental results showed that the proposed hybrid stock index prediction method significantly outperforms the other six comparison models. It is an efficient and effective alternative for stock index forecasting.     

Support vector machine with genetic algorithm for forecasting of key-gas ratios in oil-immersed transformer

Failures of power transformer are related with key-gas ratios C₂H₂/C₂H₄, CH₄/H₂ and C₂H₄/C₂H₆ strongly. Forecasting of these ratios of key-gas in power transformer oil is very significant to detect and identify incipient failures of transformer early. Forecasting of the ratios of key-gas in power transformer oil is a complicated problem due to its non-linearity and the small quantity of training data. In this study, support vector machine with genetic algorithm (SVMG) is proposed to forecast the ratios of key-gas in power transformer oil, among which genetic algorithm (GA) is used to determine free parameters of support vector machine. The experimental results indicate that the SVMG method can achieve greater accuracy than grey model, artificial neural network under the circumstance of small training data. Consequently, the SVMG model is a proper alternative for forecasting of the ratios of key-gas in power transformer oil.     

Housing price forecasting based on genetic algorithm and support vector machine

Accurate forecasting for future housing price is very significant for socioeconomic development and national lives. In this study, a hybrid of genetic algorithm and support vector machines (G-SVM) approach is presented in housing price forecasting. Support vector machine (SVM) has been proven to be a robust and competent algorithm for both classification and regression in many applications. However, how to select the most appropriate the training parameter value is the important problem in the using of SVM. Compared to Grid algorithm, genetic algorithm (GA) method consumes less time and performs well. Thus, GA is applied to optimize the parameters of SVM simultaneously. The cases in China are applied to testify the housing price forecasting ability of G-SVM method. The experimental results indicate that forecasting accuracy of this G-SVM approach is more superior than GM.     

组合粗糙集和支持向量回归的船舶交通流预测

影响交通流变化的因素众多,为改进传统的船舶交通流预测精度不高,一种结合粗糙集和支持向量回归智能算法的交通流预测模型提出,通过ROSETTA软件进行属性约简预处理,筛选出影响交通流变化的关键影响因素,剔除冗余信息。筛选结果显示外轮进出艘次、对外贸易总额、港口GDP、集装箱标准箱、港口货物吞吐量为输入变量,运用Libsvm软件构建基于遗传算法参数寻优的支持向量回归模型预测2008年和2009年的交通流。算例结果表明,与BP神经网络和SVM模型相比,组合预测模型是有效和实用的预测工具。     

Simulation and prediction of hydrological processes based on firefly algorithm with deep learning and support vector for regression

ABSTRACT

Hydrological processes are hard to accurately simulate and predict because of various natural and human influences. In order to improve the simulation and prediction accuracy of the hydrological process, the firefly algorithm with deep learning (DLFA) was used in this study to optimise the parameters of support vector for regression (SVR) automatically, and a prediction model was established based on DLFA and SVR. The hydrological process of Huangfuchuan in Fugu County, Shanxi Province was taken as the research object to verify the performance of the prediction model, and the results were compared with those by the other six prediction models. The experimental results showed that the proposed prediction model achieved improved prediction performance compared with the other six models.     

The impact of oil price shocks on stock market activities: Asymmetric effect with quantile regression

This paper examines the impact of changes in real oil prices on the real stock returns of G7 countries. In addition to investigating the asymmetric effect of oil price shocks on stock returns, we also examine the effect of the performances of stock markets themselves, which are relevant to firms’ strategies in the future. Although the responses of stock markets to oil price shocks are diverse among G7 countries, we present the inconsistent reflections of stock markets based on their performances. In many cases, quantile regression estimates are quite different from OLS models. These results carry crucial implications for the linkage between oil and stock markets.     

Support vector ordinal regression

In this letter, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales. Both approaches guarantee that the thresholds are properly ordered at the optimal solution. The size of these optimization problems is linear in the number of training samples. The sequential minimal optimization algorithm is adapted for the resulting optimization problems; it is extremely easy to implement and scales efficiently as a quadratic function of the number of examples. The results of numerical experiments on some benchmark and real-world data sets, including applications of ordinal regression to information retrieval, verify the usefulness of these approaches.     

基于PSO优化的SVM预测应用研究*

支持向量机参数对支持向量机的性能有着重要影响,参数选择问题是支持向量机的重要研究内容。针对此问题,提出一种基于粒子群优化算法的支持向量机参数选择方法。实验结果表明,经粒子群优化算法优化的支持向量机回归模型具有较高的预测精度,粒子群优化算法是选取支持向量机参数的有效方法。     

Combining wavelet-based feature extractions with relevance vector machines for stock index forecasting

Abstract: The relevance vector machine (RVM) is a Bayesian version of the support vector machine, which with a sparse model representation has appeared to be a powerful tool for time-series forecasting. The RVM has demonstrated better performance over other methods such as neural networks or autoregressive integrated moving average based models. This study proposes a hybrid model that combines wavelet-based feature extractions with RVM models to forecast stock indices. The time series of explanatory variables are decomposed using some wavelet bases and the extracted time-scale features serve as inputs of an RVM to perform the non-parametric regression and forecasting. Compared with traditional forecasting models, our proposed method performs best. The root-mean-squared forecasting errors are significantly reduced.     

A feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction

This study investigates stock market indices prediction that is an interesting and important research in the areas of investment and applications, as it can get more profits and returns at lower risk rate with effective exchange strategies. To realize accurate prediction, various methods have been tried, among which the machine learning methods have drawn attention and been developed. In this paper, we propose a basic hybridized framework of the feature weighted support vector machine as well as feature weighted K-nearest neighbor to effectively predict stock market indices. We first establish a detailed theory of feature weighted SVM for the data classification assigning different weights for different features with respect to the classification importance. Then, to get the weights, we estimate the importance of each feature by computing the information gain. Lastly, we use feature weighted K-nearest neighbor to predict future stock market indices by computing k weighted nearest neighbors from the historical dataset. Experiment results on two well known Chinese stock market indices like Shanghai and Shenzhen stock exchange indices are finally presented to test the performance of our established model. With our proposed model, it can achieve a better prediction capability to Shanghai Stock Exchange Composite Index and Shenzhen Stock Exchange Component Index in the short, medium and long term respectively. The proposed algorithm can also be adapted to other stock market indices prediction.     

基于隐马尔可夫模型的股票价格预测组合模型

提出了一种用于股票价格预测的人工神经网络(ANN),隐马尔可夫模型(HMM)和粒子群优化算法(PSO)的组合模型-APHMM模型.在APHMM模型中,ANN算法将股票的每日开盘价、最高价、最低价与收盘价转换为相互独立的量并作为HMM的输入.然后,利用PSO算法对HMM的参数初始值进行优化,并用Baum-Welch算法进行参数训练.经过训练后的HMM在历史数据中找出一组与今天股票的上述4个指标模式最相似数据,加权平均计算每个数据与它后一天的收盘价格差,则今天的股票收盘价加上这个加权平均价格差便为预测的股票收盘价.实验结果表明,APHMM模型具有良好的预测性能.