scholarly journals A New Stock Selection Model Based on Decision Tree C5.0 Algorithm

2018 ◽  
Vol 7 (4) ◽  
pp. 117
Author(s):  
Qiansheng Zhang
Keyword(s):  
Decision Tree ◽  
Selection Model ◽  
Stock Selection ◽  
Model Based

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.

scholarly journals Multi Factor Stock Selection Model Based on LSTM

2018 ◽  
Vol 10 (8) ◽  
pp. 36 ◽  
Author(s):  
Ru Zhang ◽  
Chenyu Huang ◽  
Weijian Zhang ◽  
Shaozhen Chen
Keyword(s):  
Factor Analysis ◽  
Stock Returns ◽  
Investment Strategy ◽  
Selection Model ◽  
Stock Selection ◽  
Model Based ◽  
Back Testing ◽  
With Memory ◽  
Memory Characteristics

This paper takes CSI- 300 stock as the research object, and uses the LSTM model with memory characteristics and the traditional multi factor analysis to build an improved multi factor stock selection model. In back testing experiments, we use the trained LSTM model to forecast the stock returns and make a portfolio classification to construct the investment strategy. The result shows that the multi factor stock selection model based on LSTM has good profit forecasting ability and profitability.

Stock Selection Model Based on Machine Learning with Wisdom of Experts and Crowds

2020 ◽  
Vol 35 (2) ◽  
pp. 54-64
Author(s):  
Xianjiao Wu ◽  
Qiang Ye ◽  
Hong Hong ◽  
Yijun Li
Keyword(s):  
Machine Learning ◽  
Selection Model ◽  
Stock Selection ◽  
Model Based

scholarly journals Multi-factor Stock Selection Model Based on Adaboost

2018 ◽  
Vol 8 (4) ◽  
pp. 119
Author(s):  
Ru Zhang ◽  
Tong Cao
Keyword(s):  
Large Class ◽  
Factor Model ◽  
Comparative Test ◽  
Selection Model ◽  
Stock Selection ◽  
Model Based

In this paper, we established multi-factor stock selection model based on Adaboost by using Adaboost to integrate the custom week classifier model, and Shanghai and Shenzhen 300 stocks are taken as the research object. During the stock retest, the first is make a comparative test between Adaboost multi-factor stock selection model and the traditional multi-factor model, among them, the factor large class isn’t considered in the multi-factor stock selection model. And the results of two contrast experiment showed that the multi-factor stock selection model based on Adaboost has stronger profitability and less risk than the traditional multi-factor model.

Research on Multi-Factor Stock Selection Model Based on EKPCA Algorithm

Finance ◽  
2019 ◽  
Vol 09 (04) ◽  
pp. 327-340
Author(s):  
钰 黄
Keyword(s):  
Selection Model ◽  
Stock Selection ◽  
Model Based

A Strategy of Constructing Heterogeneous Cost-Sensitive Decision Tree

2013 ◽  
Vol 756-759 ◽  
pp. 3414-3418
Author(s):  
Ding Rong Yuan ◽  
Xiao Meng Huang ◽  
Qiao Ling Duan ◽  
Hui Wen Fu
Keyword(s):  
Decision Tree ◽  
Selection Model ◽  
Experimental Results ◽  
Attribute Selection ◽  
Conversion Function ◽  
Model Based ◽  
Pruning Strategy

Usually, the algorithm of constructing cost-sensitive decision tree assume that all types of cost can be converted into a unified units of the same price, apparently how to construct an cost conversion function is an challenge. In this paper, a strategy of constructing heterogeneous cost-sensitive decision tree is designed and the different cost are take into account together in split attribute selection. Whats more, an attribute selection model based on heterogeneous cost-sensitive is constructed and the pruning strategy based on cost-sencitive is designed. The experimental results show that the proposed method is correct and more efficient than the present other methods

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