# Prediction of Stock Performance Using Deep Neural Networks
## 1 Introduction
adopted the Long Short-Term Memory (LSTM) neural network, which is an improved version of RNN, and incorporates investor sentiment and market factors to analyze the irrational component of stock price [26]. [PERSON] et al. studied the usage of LSTM networks to predict future trends of stock prices based on the price history, alongside with technical analysis indicators [27]. [PERSON] et al. presented a deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs), and LSTM are combined for stock price forecasting [28]. [PERSON] et al. used deep learning, random forests, gradient-boosted trees, and different ensembles as forecasting methods on all S&P 500 constituents from 1992 to 2015. One key finding in their research is that LSTM networks outperform memory-free classification methods [29]. In order to show accountability to their customers, [PERSON] et al. proposed to approximate and linearize the learned LSTM models by layer-wise relevance propagation [30]. Compared to RNN and LSTM, there are a relatively few examples of applying CNN for stock market prediction. [PERSON] et al. proposed a novel algorithmic trading model CNN-TA using a 2-D convolutional neural network based on 2-D images converted from financial time series data [31]. [PERSON] et al. proposed a generic framework employing LSTM and CNN for adversarial training to forecast high-frequency stock market [32]. On the whole, the LSTM network is the most widely used deep learning technology for stock performance prediction. As mentioned above, automatic trading systems that use Artificial Intelligence (AI) have become a commonplace topic, but there are few examples that successfully leverage the proven method invented by human stock traders to build automatic trading systems. The first contribution of this study is the development of an intelligent trading system by integrating AI and the knowledge of human stock traders. In this study, the important index variables suggested by economic analystsand stock traders are used in a deep neural network to predict future stock performance. The second contribution of this study is the verification of the effectiveness of the knowledge of human stock traders and various investment strategies for constructing a successful intelligent trading system. In this study, what index variables are the most significant, and how to perform the stock performance prediction to maximize earning and minimize the risk of investment are investigated. This study is focused on Japanese stock data to explore a reliable investment algorithm for the Japanese stock market. This also aims to verify whether the method invented based on United State (US) stocks is also effective in Japanese stocks, because the traders [PERSON] and [PERSON] summarize their experience based on US stocks. The rest of the paper is organized as follows: Section 2 describes the important index variables and four strategies for stock classification. Section 3 presents the proposed deep neural network to classify the distinguished stocks with good performance. Section 4 shows the evaluation of the proposed systems. Finally, Section 5 concludes this paper.