scholarly journals A New Kernel of Support Vector Regression for Forecasting High-Frequency Stock Returns

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Hui Qu ◽  
Yu Zhang
Keyword(s):  
Support Vector Regression ◽  
Stock Returns ◽  
High Frequency ◽  
Forecast Accuracy ◽  
Support Vector ◽  
Nonlinear Relationship ◽  
Sigmoid Function ◽  
Accuracy Rate ◽  
Out Of Sample ◽  
Future Return

This paper investigates the value of designing a new kernel of support vector regression for the application of forecasting high-frequency stock returns. Under the assumption that each return is an event that triggers momentum and reversal periodically, we decompose each future return into a collection of decaying cosine waves that are functions of past returns. Under realistic assumptions, we reach an analytical expression of the nonlinear relationship between past and future returns and introduce a new kernel for forecasting future returns accordingly. Using high-frequency prices of Chinese CSI 300 index from January 4, 2010, to March 3, 2014, as empirical data, we have the following observations: (1) the new kernel significantly beats the radial basis function kernel and the sigmoid function kernel out-of-sample in both the prediction mean square error and the directional forecast accuracy rate. (2) Besides, the capital gain of a simple trading strategy based on the out-of-sample predictions with the new kernel is also significantly higher. Therefore, we conclude that it is statistically and economically valuable to design a new kernel of support vector regression for forecasting high-frequency stock returns.

Comparative Analysis of Statistical, Machine Learning, and Grey Methods for Short-Term Electricity Load Forecasting

2016 ◽  
pp. 1161-1183 ◽  
Author(s):  
Tuncay Ozcan ◽  
Tarik Küçükdeniz ◽  
Funda Hatice Sezgin
Keyword(s):  
Support Vector Regression ◽  
Electricity Market ◽  
Load Forecasting ◽  
Forecast Accuracy ◽  
Grey Model ◽  
Support Vector ◽  
Forecasting Model ◽  
Data Sets ◽  
Electricity Load ◽  
Electricity Load Forecasting

Electricity load forecasting is crucial for electricity generation companies, distributors and other electricity market participants. In this study, several forecasting techniques are applied to time series modeling and forecasting of the hourly loads. Seasonal grey model, support vector regression, random forests, seasonal ARIMA and linear regression are benchmarked on seven data sets. A rolling forecasting model is developed and 24 hours of the next day is predicted for the last 14 days of each data set. This day-ahead forecasting model is especially important in day-ahead market activities and plant scheduling operations. Experimental results indicate that support vector regression and seasonal grey model outperforms other approaches in terms of forecast accuracy for day-ahead load forecasting.

Parameter C Optimizing for Robust ε-Support Vector Regression

2014 ◽  
Vol 543-547 ◽  
pp. 2045-2048
Author(s):  
Yuan Lv ◽  
Zhong Gan
Keyword(s):  
Experimental Data ◽  
Support Vector Regression ◽  
System Parameter ◽  
Forecast Accuracy ◽  
Regression Method ◽  
Support Vector ◽  
Penalty Parameter ◽  
Optimal Method ◽  
Good Forecast ◽  
Selection Of

In case of experimental data contaminated with errors and noise, the robust ε-support vector regression has good forecast accuracy and high generalization ability. However, it depends on the selection of system parameter. Firstly, this paper introduces the robust ε-support vector regression method. Secondly, as the experiments prove, the new method achieves high forecast accuracy by virtue of the optimal penalty parameter C. Finally, the optimal method of parameter C is presented in the last section.

scholarly journals Estimating dynamic copula dependence using intraday data

2015 ◽  
Vol 19 (4) ◽  
Author(s):  
Lidan Grossmass ◽  
Ser-Huang Poon
Keyword(s):  
Stock Returns ◽  
Long Memory ◽  
High Frequency ◽  
Time Varying ◽  
Credit Crisis ◽  
Dynamic Copula ◽  
Tick Data ◽  
Out Of Sample ◽  
Intraday Data ◽  
Better Than

AbstractWe estimate the dynamic daily dependence between assets by applying the Semiparametric Copula-Based Multivariate Dynamic (SCOMDY) model on intraday data. Using tick data of three stock returns of the period before and during the credit crisis, we find that our dependence estimator better captures the steep increase in dependence during the onset of the crisis as compared to other commonly used time-varying copula methods. Like other high-frequency estimators, we find that the dependence estimator exhibits long memory and forecast it using a HAR model. We show that for out-of-sample forecasts, our dependence estimator performs better than the constant estimator and other commonly used time-varying copula dependence estimators.

scholarly journals Coupling Square Wave Anodic Stripping Voltammetry with Support Vector Regression to Detect the Concentration of Lead in Soil under the Interference of Copper Accurately

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6792
Author(s):  
Ning Liu ◽  
Guo Zhao ◽  
Gang Liu
Keyword(s):  
Support Vector Regression ◽  
Anodic Stripping Voltammetry ◽  
Multiple Linear Regression Model ◽  
Stripping Voltammetry ◽  
Support Vector ◽  
Detection Accuracy ◽  
Nonlinear Relationship ◽  
Square Wave ◽  
Anodic Stripping ◽  
Average Recovery Rate

In this study, an effective method for accurately detecting Pb(II) concentration was developed by coupling square wave anodic stripping voltammetry (SWASV) with support vector regression (SVR) based on a bismuth-film modified electrode. The interference of different Cu2+ contents on the SWASV signals of Pb2+ was investigated, and a nonlinear relationship between Pb2+ concentration and the peak currents of Pb2+ and Cu2+ was determined. Thus, an SVR model with two inputs (i.e., peak currents of Pb2+ and Cu2+) and one output (i.e., Pb2+ concentration) was trained to quantify the above nonlinear relationship. The SWASV measurement conditions and the SVR parameters were optimized. In addition, the SVR mode, multiple linear regression model, and direct calibration mode were compared to verify the detection performance by using the determination coefficient (R2) and root-mean-square error (RMSE). Results showed that the SVR model with R2 and RMSE of the test dataset of 0.9942 and 1.1204 μg/L, respectively, had better detection accuracy than other models. Lastly, real soil samples were applied to validate the practicality and accuracy of the developed method for the detection of Pb2+ with approximately equal detection results to the atomic absorption spectroscopy method and a satisfactory average recovery rate of 98.70%. This paper provided a new method for accurately detecting the concentration of heavy metals (HMs) under the interference of non-target HMs for environmental monitoring.

scholarly journals Particle Swarm Optimization-Based Support Vector Regression for Tourist Arrivals Forecasting

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Hsiou-Hsiang Liu ◽  
Lung-Cheng Chang ◽  
Chien-Wei Li ◽  
Cheng-Hong Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Support Vector Regression ◽  
Particle Swarm ◽  
Forecast Accuracy ◽  
Tourism Industry ◽  
Support Vector ◽  
Tourism Demand ◽  
Economic Sectors ◽  
Data Set ◽  
Swarm Optimization

The tourism industry has become one of the most important economic sectors for governments worldwide. Accurately forecasting tourism demand is crucial because it provides useful information to related industries and governments, enabling stakeholders to adjust plans and policies. To develop a forecasting tool for the tourism industry, this study proposes a method that combines feature selection (FS) and support vector regression (SVR) with particle swarm optimization (PSO), named FS–PSOSVR. To ensure high forecast accuracy, FS and a PSO algorithm are employed to, respectively, select reliable input variables and to identify the optimal initial parameters of SVR. The proposed method was tested using a data set of monthly tourist arrivals to Taiwan from January 2006 to December 2016. The results reveal that the errors obtained using FS–PSOSVR are comparatively smaller than those obtained using other methods, indicating that FS–PSOSVR is an effective method for forecasting tourism demand.

scholarly journals Forecasting Time Series Movement Direction with Hybrid Methodology

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Salwa Waeto ◽  
Khanchit Chuarkham ◽  
Arthit Intarasit
Keyword(s):  
Time Series ◽  
Support Vector Regression ◽  
Hybrid Model ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Movement Direction ◽  
Support Vector ◽  
Autoregressive Integrated Moving Average ◽  
Proposed Model ◽  
Hybrid Methodology

Forecasting the tendencies of time series is a challenging task which gives better understanding. The purpose of this paper is to present the hybrid model of support vector regression associated with Autoregressive Integrated Moving Average which is formulated by hybrid methodology. The proposed model is more convenient for practical usage. The tendencies modeling of time series for Thailand’s south insurgency is of interest in this research article. The empirical results using the time series of monthly number of deaths, injuries, and incidents for Thailand’s south insurgency indicate that the proposed hybrid model is an effective way to construct an estimated hybrid model which is better than the classical time series model or support vector regression. The best forecast accuracy is performed by using mean square error.

Robustness Analysis of Novel ε-Support Vector Regression

2014 ◽  
Vol 543-547 ◽  
pp. 2049-2052
Author(s):  
Yuan Lv ◽  
Zhong Gan
Keyword(s):  
Normal Distribution ◽  
Support Vector Regression ◽  
Robustness Analysis ◽  
Forecast Accuracy ◽  
Support Vector ◽  
Kolmogorov Test ◽  
Hyper Plane ◽  
Good Forecast

The key to the robust ε-support vector regression algorithm is searching for the optimal regression hyper plane while data with disturbance in the X-direction. In the paper, the optimal regression hyper plane and the optimal separating hyper plane are compared and analyzed. By means of Kolmogorov test, it is can be deduced that the testing errors of the robust ε-support vector regression experiments follow normal distribution. The result demonstrates that the algorithm has good forecast accuracy and high robustness.

The best of two worlds: Forecasting high frequency volatility for cryptocurrencies and traditional currencies with Support Vector Regression

2018 ◽  
Vol 97 ◽  
pp. 177-192 ◽  
Author(s):  
Yaohao Peng ◽  
Pedro Henrique Melo Albuquerque ◽  
Jader Martins Camboim de Sá ◽  
Ana Julia Akaishi Padula ◽  
Mariana Rosa Montenegro
Keyword(s):  
Support Vector Regression ◽  
High Frequency ◽  
Support Vector

Comparative Analysis of Statistical, Machine Learning, and Grey Methods for Short-Term Electricity Load Forecasting

2016 ◽  
pp. 96-118
Author(s):  
Tuncay Ozcan ◽  
Tarik Küçükdeniz ◽  
Funda Hatice Sezgin
Keyword(s):  
Support Vector Regression ◽  
Electricity Market ◽  
Load Forecasting ◽  
Forecast Accuracy ◽  
Grey Model ◽  
Support Vector ◽  
Forecasting Model ◽  
Data Sets ◽  
Electricity Load ◽  
Electricity Load Forecasting

Electricity load forecasting is crucial for electricity generation companies, distributors and other electricity market participants. In this study, several forecasting techniques are applied to time series modeling and forecasting of the hourly loads. Seasonal grey model, support vector regression, random forests, seasonal ARIMA and linear regression are benchmarked on seven data sets. A rolling forecasting model is developed and 24 hours of the next day is predicted for the last 14 days of each data set. This day-ahead forecasting model is especially important in day-ahead market activities and plant scheduling operations. Experimental results indicate that support vector regression and seasonal grey model outperforms other approaches in terms of forecast accuracy for day-ahead load forecasting.

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