# A Survey of Forex and Stock Price Prediction Using Deep Learning
## 4 Results
### Papers Descriptions
#### 4.1.5 Reinforcement Learning  
Eight articles that used reinforcement learning technology for stock and Forex prediction are briefly described below. Table 6 shows the author, variables, dataset, and model of the papers mentioned.  
[PERSON] proposed three different reinforcement learning methods to predict the price of stock. The results showed that the best-performing deep reinforcement learning model is Deep-Q Network (DQN), not Double DQN. This paper also demonstrated Duelling DQN, which was an improved model based on DQN [76].  
[PERSON] proposed a reinforcement learning model combined with LSTM and CNN. The model generated various charts from stock trading data and used them as inputs to the CNN layer. The features extracted through the CNN layer were divided into column vectors and inputted to the LSTM layer. The reinforcement learning defined the agents' policy neural network structure, reward, and action and provided buying, selling, and holding probabilities as final output [77].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference No.** & **Author** & **Dataset** & **Variables** & **Model** \\ \hline [76] & [PERSON], Y. & US stock dataset & Close price and volume & DQN \\  & & & A candlestick chart, four moving & & \\  & & & average curves (5, 20, 60, and & \\  & & & 120 days), a bar graph of trading & & \\  & & & volume, DMI, and SSD & & \\  & & & Open price, high price, low price, & \\  & & & & close price, volume, DEA, MACD & \\  & & & 600547, 600999, & & \\  & & & 600547, 600999, & & \\  & & & and 601988 & & \\  & & & & dm down & \\  & & & & & \\  & & & & \\  & & & & & \\  & & & & \\  & & & & & \\  & & & & \\  & & & & \\ \end{tabular}
\end{table}
Table 6: Information on the papers which used the reinforcement learning model.  
[PERSON] proposed a reinforcement learning with an LSTM-based agent that could automatically sense the dynamics of the stock market and could alleviate the difficulty of manually designing indicators from massive data. This paper compared a wide range of different input sets [78].  
[PERSON] proposed a reinforcement learning-Q network model; three hidden layers of ReLU neutrons were trained as Reinforcement Learning (RL) agents through the Q-learning algorithm under a novel simulated market environment framework. The framework was able to consistently induce stable learning that generalized to out-of-sample data [79].  
[PERSON] proposed to apply the state-of-art Asynchronous Advantage Actor-Critic algorithm (A3C algorithm) to solve the portfolio management problem and designed a standalone deep reinforcement learning model [80].  
[PERSON] proposed an adaptive box-normalization (ABN) stock trading strategy based on reinforcement learning, which improved the original box theory. In his ABN strategy, the stock market data was independently normalized inside each oscillation box [81].  
[PERSON] proposed a reinforcement learning model which had multi-objective and LSTM agents. It was found that feature learning could contribute to better performances. The LSTM network made continuous decisions and could change positions at the right time, which reduced the transaction cost, and the multi-objective structure made good profits within the acceptable risk [82].  
[PERSON] analyzed the data for 46 players extracted from a financial market online game and tested whether reinforcement learning could capture these players' behavior using a riskiness measure based on financial modelling [83].