Stock Selection Strategy Based on Support Vector Machine

2020 ◽  
Author(s):  
Runhuan Liu
Keyword(s):  
Support Vector Machine ◽  
Selection Strategy ◽  
Support Vector ◽  
Stock Selection

scholarly journals Multi-factor Stock Selection Model Based on Kernel Support Vector Machine

2018 ◽  
Vol 10 (5) ◽  
pp. 9 ◽  
Author(s):  
Ru Zhang ◽  
Zi-ang Lin ◽  
Shaozhen Chen ◽  
Zhixuan Lin ◽  
Xingwei Liang
Keyword(s):  
Support Vector Machine ◽  
Predictive Power ◽  
Factor Model ◽  
Selection Strategy ◽  
Support Vector ◽  
Stock Selection ◽  
Financial Investment ◽  
Model Based ◽  
Kernel Support Vector Machine ◽  
Selection Of

In recent years, the combination of machine learning method and traditional financial investment field has become a hotspot in academic and industry. This paper takes CSI 300 and CSI 500 stocks as the research objects. First, this paper carries out kernel function test and parameter optimization for the kernel support vector machine system, and then predict and optimize the combination of market-neutral stock selection strategy and stock right strategy. The results of the experiment show that the multi-factor model based on SVM has a strong predictive power for the selection of stock, and it has a difference in the predictive power of different nuclear functions.

scholarly journals Adaboost-SVM Multi-Factor Stock Selection Model Based on Adaboost Enhancement

2018 ◽  
Vol 7 (5) ◽  
pp. 9
Author(s):  
Ru Zhang ◽  
Zi-ang Lin ◽  
Shaozhen Chen ◽  
Min Zhao ◽  
Mingjie Yuan
Keyword(s):  
Support Vector Machine ◽  
Selection Model ◽  
Selection Strategy ◽  
Support Vector ◽  
Linear Support Vector Machine ◽  
Financial Industry ◽  
Stock Selection ◽  
Financial Investment ◽  
Original Algorithm ◽  
Model Based

In recent years, the applications of machine learning techniques to perfect traditional financial investment models has gained a widespread attention from the academic circle and the financial industry. This paper takes CSI300 stocks as the object of the research, uses Adaboost to enhance the classification ability of original linear support vector machine, and combines all major factors to build Adaboost-SVM multi-factor stock selection model based on Adaboost enhancement. In the backtesting analysis, the stock selection strategy of original linear support vector machine was compared with the Adaboost-SVM multi-factor stock selection strategy based on Adaboost enhancement. The result shows that the Adaboost-SVM multi-factor stock selection strategy based on Adaboost enhancement possesses stronger profitability and smaller income fluctuation than the original algorithm model.

A dynamic model selection strategy for support vector machine classifiers

2012 ◽  
Vol 12 (8) ◽  
pp. 2550-2565 ◽  
Author(s):  
Marcelo N. Kapp ◽  
Robert Sabourin ◽  
Patrick Maupin
Keyword(s):  
Support Vector Machine ◽  
Model Selection ◽  
Dynamic Model ◽  
Selection Strategy ◽  
Support Vector

scholarly journals A node localization algorithm based on Voronoi diagram and support vector machine for wireless sensor networks

2021 ◽  
Vol 17 (2) ◽  
pp. 155014772199341
Author(s):  
Zhanjun Hao ◽  
Jianwu Dang ◽  
Yan Yan ◽  
Xiaojuan Wang
Keyword(s):  
Support Vector Machine ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Voronoi Diagram ◽  
Wireless Sensor ◽  
Selection Strategy ◽  
Support Vector ◽  
Localization Algorithm ◽  
Target Node ◽  
Localization Scheme

For wireless sensor network, the localization algorithm based on Voronoi diagram has been applied. However, the location accuracy node position in wireless sensor network needs to be optimized by the analysis of the literature, a node location algorithm based on Voronoi diagram and support vector machine is proposed in this article. The basic idea of the algorithm is to first divide the region into several parts using Voronoi diagram and anchor node in the localization region. The range of the initial position of the target node is obtained by locating the target node in each region and then the support vector machine is used to optimize the position of the target node accurately. The localization performance of the localization algorithm is analyzed by simulation and real-world experiments. The experimental results show that the localization algorithm proposed in this article is better than the optimal region selection strategy based on Voronoi diagram-based localization scheme and Weighted Voronoi diagram-based localization scheme localization algorithms in terms of localization accuracy. Therefore, the performance of the localization algorithm proposed in this article is verified.

GENETIC ALGORITHM BASED FEATURE SELECTION FOR PARAPHRASE RECOGNITION

2013 ◽  
Vol 22 (02) ◽  
pp. 1350007 ◽  
Author(s):  
A. CHITRA ◽  
ANUPRIYA RAJKUMAR
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Selection Strategy ◽  
Support Vector ◽  
Semantic Features ◽  
Evaluation Systems ◽  
Evaluation Task ◽  
Wrapper Method ◽  
Work Done ◽  
Original Feature

Paraphrase Recognition systems most often use various lexical, syntactic and semantic features to recognize paraphrases. This paper presents the work done in designing a Support Vector Machine (SVM) based Paraphrase Recognizer and then improving its performance using feature selection strategy. Wrapper method of feature selection has been adopted by combining Genetic Algorithms with Support Vector Machine Classifiers. Experimental results show that applying Feature selection improves the accuracy besides reducing the number of features. The developed paraphrase recognizer has been applied for the Student Answer Evaluation task. The results obtained show that the performance of Answer Evaluation systems which use only half the number of features is comparable to systems using the original feature set.

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