# Prediction of Stock Performance Using Deep Neural Networks
## 1 Introduction  
Stock performance prediction is one of the most challenging issues in time series data analysis. How to accurately predict stock performance changing is an open question with respect to the financial world and academia field. Stock performance prediction is a difficult task, due to the complexity and dynamic of the markets and many inexplicit, intertwined factors involved. Economic analysts and stock traders are the earliest pioneers who perform the prediction of stock performance. In the past several decades, thousands of books in stock trading have been published. Many economic analysts and stock traders have studied the historical patterns of financial time series data and have proposed various methods to predict stock performance. In order to achieve a promising performance, most of these methods require careful selection of index variables and finding the sharing features among the distinguished stocks. [PERSON] and [PERSON] are two representatives of successful traders. They summarized their stock trading experience in the publications [1; 2; 3; 4]. [PERSON] CAN SLIM method has a huge following, and also performed well in American Association of Individual Investors (AAII)'s implementation of hismodel [5]. [PERSON] revealed the proven, time-tested trading system he used to achieve triple-digit returns for five consecutive years, averaging 220% per year [6]. Many followers referred to their methodology in stock trading due to their remarkable achievement. On the other hand, machine learning models, such as Artificial Neural Networks (ANNs) [7; 8; 9; 10; 11; 12], Support Vector Regression (SVR) [13; 14; 15], Genetic Algorithms (GA) [16], as well as hybrid models [17] have been widely used to predict financial time series during recent decades. In addition, the time-series problem considers Dynamic Time Warping (DTW) that handles scaling and shifting, which is common in the stock market. The recently developed DTW Network is an algorithm candidate for financial time series data processing [18]. [PERSON] et al. [19] used the Hurst exponent to measure the correlation and co-movement between two different series. [PERSON] et al. [20] surveyed papers using machine learning techniques for financial time series forecasting based on technique categories, such as ANN-based, evolutionary and optimization techniques, and multiple/hybrid methods. [PERSON] et al. [21] provided a comprehensive overview of the most important primary studies, which cover techniques such as ANN, SVM, hybrid mechanisms, optimization, and ensemble methods. The surveys indicate that the approaches differ regarding the number and types of variables used in modeling financial behavior; however, there is no consensus on which input variables are the best to be used. In addition, it is important to note that there is no well-established methodology to guide the construction of a successful intelligent trading system. The profit evaluation of the proposed methods when used in real-world applications are generally neglected [21]. Recently, deep learning, as an advanced version of ANN, has attracted attention in the machine learning field because of its high performance in areas such as image recognition and speech recognition. In the field of financial forecasting, a similar new trend considers that a deep neural network has the possibility to increase the accuracy of stock market prediction [22; 23]. There are two main deep learning approaches that have been used in stock market prediction: Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN). [PERSON] et al. made use of a low complexity RNN for stock market prediction [24]. [PERSON] et al. explored RNN with character-level language model pre-training for both intraday and interday stock market forecasting. The proposed automated trading system that, given the release of news information about a company, predicted changes in stock prices [25]. [PERSON] et al.