Financial Stock Investment Management Using Deep Learning Algorithm in the Internet of Things.

This paper aims to explore a new model to study financial stock investment management (SIM) and obtain excess returns. Consequently, it proposes a financial SIM model using deep Q network (DQN) as reinforcement earning (RL) algorithm and Long Short-Term Memory (LSTM) as deep neural network (DNN). Then, after training and optimization, the proposed model is back-tested. The research findings are as follows: the LSTM neural network (NN)-based model will import the observation of the market at each time and the change of transaction information over time. The LSTM network can find and learn the potential relationship between time series data. There are two hidden layers and one output layer in the model. The hidden layer is an LSTM structure and the output layer is the fully connected NN. DQN algorithm first stores the experience sample data of the agent-environment interaction into the experience pool. It then randomly selects a small batch of data from the experience pool to train the network. Doing so removes the correlation and dependence between samples so that the DNN model can better learn the value function in the RL task. The model can predict the future state according to historical information and decide which actions to take in the next step. Meanwhile, five stocks of Chinese A-shares are selected to form an asset pool. The initial 500,000 amount of the account is divided into five equal shares, which are invested and traded. Overall, the model account's rate of return (RoR) during the back-test is 32.12%. The Shanghai Stock Exchange (SSI) has risen by 19.157% in the same period. Thus, the model's performance has exceeded the SSI's in the same period. E stock has the maximum RoR of 78.984%. The RoR of A, B, and C stocks is 54.129%, 11.594%, and 9.815%, respectively. B stock presents a minimum RoR of 6.084%. All these stocks have got positive returns. Therefore, the proposed financial SIM based on the DL algorithm is scientific and feasible. The research content has certain significant reference for the DL-based financial SIM.