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.

Research of Building Intelligent Investment Decision Mode for Investment Portfolio — Using Taiwan Electronic Stock as an Example

2010 ◽  
Vol 13 (04) ◽  
pp. 621-645 ◽  
Author(s):  
Wen-Rong Jerry Ho ◽  
C. H. Liu ◽  
H. W. Chen
Keyword(s):  
Stock Returns ◽  
Stock Prices ◽  
Stock Exchange ◽  
Investment Decision ◽  
Investment Strategy ◽  
Return Rate ◽  
Selection Strategy ◽  
Stock Selection ◽  
Taiwan Stock Exchange ◽  
Book To Market Ratio

This research uses all of the listed electronic stocks in the Taiwan Stock Exchange as a sample to test the performance of the return rate of stock prices. In addition, this research compares it with the electronic stock returns. The empirical result shows that no matter which kind of stock selection strategy we choose, a majority of the return rate is higher than that of the electronics index. Evident in the results, the predicted effect of BPNN is better than that of the general average decentralized investment strategy. Furthermore, the low price-to-earning ratio and the low book-to-market ratio have a significant long-term influence.

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.

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.

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