Stock Price Index Prediction Based on Improved SVM

2011 ◽  
Vol 267 ◽  
pp. 468-471
Author(s):  
Jin Yan Shi ◽  
Xue Li ◽  
Yan Xi Li
Keyword(s):  
Kernel Function ◽  
Stock Price ◽  
Price Index ◽  
Kernel Functions ◽  
Classification Model ◽  
Support Vector ◽  
Theoretical Advantage ◽  
Stock Price Index ◽  
Hybrid Kernel Function ◽  
Hybrid Kernel

Accurate stock price predicting is a key problem to the financial field. Comparing with the traditional stock price predicting models such as GARCH models and neural networks, the theoretical advantage of applying support vector machine (SVM) to stock price predicting highly depends on solving the problem of kernel function construction and parameter optimization. For the effect of the kernel function in the SVM classification model, a hybrid kernel function is presented. In order to optimize and adjust the important parameters during the process of building the hybrid kernel function, an improved particle swarm optimization which has better global search ability is used. Experimental results about stock price index predicting show that this method has higher prediction accuracy compared with the traditional kernel functions.

Forecasting of stock price index using support vector regression with multivariate empirical mode decomposition

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yanmei Huang ◽  
Changrui Deng ◽  
Xiaoyuan Zhang ◽  
Yukun Bao
Keyword(s):  
Support Vector Regression ◽  
Empirical Mode Decomposition ◽  
Stock Price ◽  
Price Index ◽  
Support Vector ◽  
Modeling Framework ◽  
Content Type ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Stock Price Index

Purpose Despite the widespread use of univariate empirical mode decomposition (EMD) in financial market forecasting, the application of multivariate empirical mode decomposition (MEMD) has not been fully investigated. The purpose of this study is to forecast the stock price index more accurately, relying on the capability of MEMD in modeling the dependency between relevant variables. Design/methodology/approach Quantitative and comprehensive assessments were carried out to compare the performance of some selected models. Data for the assessments were collected from three major stock exchanges, namely, the standard and poor 500 index from the USA, the Hang Seng index from Hong Kong and the Shanghai Stock Exchange composite index from China. MEMD-based support vector regression (SVR) was used as the modeling framework, where MEMD was first introduced to simultaneously decompose the relevant covariates, including the opening price, the highest price, the lowest price, the closing price and the trading volume of a stock price index. Then, SVR was used to set up forecasting models for each component decomposed and another SVR model was used to generate the final forecast based on the forecasts of each component. This paper named this the MEMD-SVR-SVR model. Findings The results show that the MEMD-based modeling framework outperforms other selected competing models. As per the models using MEMD, the MEMD-SVR-SVR model excels in terms of prediction accuracy across the various data sets. Originality/value This research extends the literature of EMD-based univariate models by considering the scenario of multiple variables for improving forecasting accuracy and simplifying computability, which contributes to the analytics pool for the financial analysis community.

Multi-User Detection Based on Improved KICA with Bat Algorithm

2013 ◽  
Vol 336-338 ◽  
pp. 1867-1870
Author(s):  
Xiao Zhi Liu ◽  
Jing Li
Keyword(s):  
Independent Component Analysis ◽  
Kernel Function ◽  
Bit Error Rate ◽  
Bat Algorithm ◽  
Kernel Functions ◽  
Independent Component ◽  
Kernel Independent Component Analysis ◽  
Simulation Results ◽  
Hybrid Kernel Function ◽  
Hybrid Kernel

In this paper, an improved kernel independent component analysis (KICA) algorithm is proposed for multi-user detection (MUD). In this algorithm, a new hybrid kernel function is adopted. In addition, the bat algorithm is applied to the optimizing process of independent component separation. Simulation results show that the new hybrid kernel function performs better in MUD than other kernel functions, and the improved KICA with bat algorithm has the smallest bit error rate (BER) when compared with classical FastICA and KICA algorithms.

Wind power prediction method based on hybrid kernel function support vector machine

2017 ◽  
Vol 42 (3) ◽  
pp. 252-264 ◽  
Author(s):  
Zhongda Tian ◽  
Shujiang Li ◽  
Yanhong Wang ◽  
Xiangdong Wang
Keyword(s):  
Support Vector Machine ◽  
Wind Power ◽  
Kernel Function ◽  
Prediction Accuracy ◽  
Prediction Method ◽  
Support Vector ◽  
Power Prediction ◽  
Wind Power Prediction ◽  
Hybrid Kernel Function ◽  
Hybrid Kernel

The prediction accuracy of wind power affects the operation cost of the power grid, which is a direct result of the supply and demand balance of the grid. Therefore, how to improve the prediction accuracy of wind power is very important. Considering the prediction accuracy of current prediction methods is not high, a wind power prediction method based on a hybrid kernel function support vector machine is proposed. On the basis of the exhibited characteristics of different kernel functions, the hybrid kernel function is a linear combination of the radial basis function and the polynomial kernel function. The hybrid kernel function is selected as the kernel function of support vector machine. The global kernel function is used to fit the correlation of the distant sample data, while the partial kernel function is used to fit the correlation of the data in neighboring fields. The generalization performance of the support vector machine model is improved. At the same time, an improved particle swarm optimization algorithm is introduced to determine the optimal parameters of the hybrid kernel function and support vector machine prediction model. Finally, the built prediction model is used to predict the wind power. The simulation results demonstrate that the proposed prediction method has better prediction accuracy for wind power.

ANALISIS PENGARUH INFLASI, KURS DAN IHSG TERHADAP RETURN REKSA DANA SAHAM PT NIKKO SECURITIES INDONESIA PERIODE 2014-2017

2019 ◽  
Vol 2 (2) ◽  
pp. 103
Author(s):  
Muhammad Rois Rois ◽  
Manarotul Fatati Fatati ◽  
Winda Ihda Magfiroh
Keyword(s):  
Regression Analysis ◽  
Exchange Rate ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Stock Price ◽  
Price Index ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Equity Fund ◽  
Stock Price Index

This study aims to determine the effect of Inflation, Exchange Rate and Composite Stock Price Index (IHSG) to Return of PT Nikko Securities Indonesia Stock Fund period 2014-2017. The study used secondary data obtained through documentation in the form of PT Nikko Securities Indonesia Monthly Net Asset (NAB) report. Data analysis is used with quantitative analysis, multiple linear regression analysis using eviews 9. Population and sample in this research are PT Nikko Securities Indonesia. The result of multiple linear regression analysis was the coefficient of determination (R2) showed the result of 0.123819 or 12%. This means that the Inflation, Exchange Rate and Composite Stock Price Index (IHSG) variables can influence the return of PT Nikko Securities Indonesia's equity fund of 12% and 88% is influenced by other variables. Based on the result of the research, the variables of inflation and exchange rate have a negative and significant effect toward the return of PT Nikko Securities Indonesia's equity fund. While the variable of Composite Stock Price Index (IHSG) has a negative but not significant effect toward Return of Equity Fund of PT Nikko Securities Indonesia

Fuzzy complementary entropy using hybrid-kernel function and its unsupervised attribute reduction

2021 ◽  
Vol 231 ◽  
pp. 107398
Author(s):  
Zhong Yuan ◽  
Hongmei Chen ◽  
Xiaoling Yang ◽  
Tianrui Li ◽  
Keyu Liu
Keyword(s):  
Kernel Function ◽  
Attribute Reduction ◽  
Hybrid Kernel Function ◽  
Hybrid Kernel
Sign in / Sign up

Export Citation Format

Share Document