A genetic-based stock selection model using investor sentiment indicators

2011 ◽  
Author(s):  
Chien-Feng Huang ◽  
Chih-Hsiang Chang ◽  
Bao Rong Chang ◽  
Tsung-Nan Hsieh
Keyword(s):  
Investor Sentiment ◽  
Selection Model ◽  
Stock Selection

scholarly journals A SVM Stock Selection Model within PCA

2014 ◽  
Vol 31 ◽  
pp. 406-412 ◽  
Author(s):  
Huanhuan Yu ◽  
Rongda Chen ◽  
Guoping Zhang
Keyword(s):  
Selection Model ◽  
Stock Selection

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.

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