# Portfolio Learning Based on Deep Learning
## 2 Related Work  
Applying deep learning to portfolio selection has attracted a lot of attention from researchers, and many efforts have been dedicated to apply deep convolution networks to portfolio selection. [PERSON] [13] presented an automated methodology for producing an informative set of features utilizing a deep neural network. [PERSON] [14] and [PERSON] et al. [15] surveyed the portfolio selection problem in the framework of competitive analysis. [PERSON] [16] considered a two-asset personal retirement portfolio and proposed several reinforcement learning agents for trading portfolio assets. Meanwhile, Reinforcement Learning (RL) has always been an important method in the field of quantitative trading. [PERSON] et al. [17] proposed AlphaStock, a novel reinforcement learning-based investment strategy enhanced by interpretable deep attention networks.  
Except for performing portfolio learning, there are many additional ways to use deep learning to make stock price predictions. [PERSON] [18] used a deep neural generative model for predicting daily stock price movements with new articles. [PERSON] et al. [11] proposed a deep learning method for event-driven stock market prediction. [PERSON] [19] introduced deep momentum networks--a hybrid approach that injects deep learning-based trading rules into the volatility scaling framework of time series momentum. These methods for stock price prediction are based on natural language processing (NLP). [PERSON] [20] presented the large-scale empirical application of reinforcement learning to the important problem of optimized trade execution in modern financial markets.  
Deep learning models based on time series have been widely used for stock prediction. [PERSON] [21] showed that CNN can be better than RNN in capturing semantics from texts and RNN is better in capturing context information and modeling complex temporal characteristics for stock market forecasting. However, both of them show some improvements when compared with previous studies. [PERSON] [12] proposed a novel application of deep learning models, Paragraph Vector, and Long Short-Term Memory (LSTM), to financial time series forecasting. They proposed an approach that converts newspaper articles into distributed representations via Paragraph Vector and models the temporal effects of past events on opening prices about multiple companies with LSTM.  
Beside mainstream deep learning methods, other deep learning frameworks have also been applied to predicting stocks. [PERSON] [22] investigated and compared a Feed-forward Neural Network (FNN) and an Adaptive Neural Fuzzy Inference System (ANFIS) in stock prediction using fundamental financial ratios. This study showed that both architectures possess the ability to separate winners and losers from a sample universe of stocks, and the selected portfolios outperformed the benchmark. [PERSON] [23] used two-directional two-dimensional principal component analysis in stock prediction. The performance was evaluated on Google stock price multimedia data (chart) from NASDAQ. This work showed that deep learning can improve stock market forecasting accuracy. [PERSON] et al. [24] applied three unsupervised feature extraction methods, principal component analysis, autoencoder, and the restricted Boltzmann machine, to high-frequency intraday stock returns to predict future market behavior. This study offered practical insights and potential applications.  
Existing methods in the portfolio learning and stock price prediction areas ignore the use of images of stock price fluctuations. Their models only use the time series data or the text data of news. We believe that image data contain more high-level information that has not been explored. Hence, we propose a new portfolio selecting model base on image data.