Using Deep Learning to Develop a Stock Price Prediction Model Based on Individual Investor Emotions

2020 ◽  
pp. 1-10
Author(s):  
Jaeheon Chun ◽  
Jaejoon Ahn ◽  
Youngmin Kim ◽  
Sukjun Lee
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Stock Price ◽  
Individual Investor ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Model Based

Stock Price Prediction Model Based on IWO Neural Network and its Applications

2014 ◽  
Vol 989-994 ◽  
pp. 1635-1640
Author(s):  
Hong Liu ◽  
Xiao Yan Lv
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Stock Price ◽  
Steepest Descent Method ◽  
Local Optimum ◽  
Back Propagation Algorithm ◽  
Feed Forward ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Model Based

In view of the deficiency of the standard back-propagation algorithm based on steepest descent method, a new kind of optimization strategy called invasive weed optimization (IWO) algorithm is introduced into the training process of feed-forward neural networks, and then a prediction model based on IWO feed-forward neural network (IWO-NN) is given. By the dynamic adjustment of standard deviation of the distribution of offspring individuals in IWO, the local convergence speed of networks is improved and the defect of trapping into a local optimum is reduced. By the empirical study of stock price prediction in Sany Heavy Industry, the results show that this method has better global astringency, robustness, and it is insensitive to initial values.

Stock Price Prediction Model Based on BA Neural Network and its Applications

2014 ◽  
Vol 989-994 ◽  
pp. 1646-1651 ◽  
Author(s):  
Xiao Yan Lv ◽  
Si Long Sun ◽  
Hong Liu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Stock Price ◽  
Frequency Tuning ◽  
Steepest Descent Method ◽  
Feed Forward ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Model Based ◽  
Intensive Exploitation

In view of the deficiency of the basic back-propagation (BP) algorithm based on steepest descent method. Bat algorithm (BA) in intelligent optimization is introduced into the training process of feed-forward neural networks, capturing the optimal solution of the objective function with a small population size and less number of iterations, and a prediction model based on BA feed-forward neural network (BA-NN) is given. By the empirical study of stock price prediction in Sany Heavy Industry, the results show that this method has advantages of frequency tuning and dynamic control of exploration and exploitation by automatic switching to intensive exploitation if necessary.

scholarly journals Importance of Event Binary Features in Stock Price Prediction

2020 ◽  
Vol 10 (5) ◽  
pp. 1597 ◽  
Author(s):  
Yoojeong Song ◽  
Jongwoo Lee
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Stock Prices ◽  
Stock Price ◽  
Prediction Models ◽  
Training Data ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Stock Investment ◽  
Binary Features

In Korea, because of the high interest in stock investment, many researchers have attempted to predict stock prices using deep learning. Studies to predict stock prices have been continuously conducted. However, the type of stock data that is suitable for deep learning has not been established, and it has not been confirmed that the developed stock prediction model can actually result in a profit. To date, designing a good deep learning model depends on how well the user can extract the features that represent all the characteristics of the training data. Among the various available features for training and test data, we determined that the use of event binary features can make stock price prediction models perform better. An event binary feature refers to a 0 or 1 value describing whether an indicator is satisfied (1) or not (0) for any given day and stock. We proposed and compared a stock price prediction model with three different feature combinations to verify the importance of binary features. As a result, we derived a prediction model that defeated the market (KOSPI and KODAQ (KOSPI (Korea Composite Stock Price Index) and KOSDAQ (Korean Securities Dealers Automated Quotations) is Korean stock indices)). The results suggest that deep learning is suitable for stock price prediction.

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