# Prediction of Stock Performance Using Deep Neural Networks
## 2 Important Index Variables and Stock Classification  
### Important Index Variables for Stock Performance Prediction  
In the current stock market, there are hundreds of index variables indicating the value of a stock from different aspects. Professional analysts and stock traders have tried hard to find the correlation between variables and the future performance of stocks. [PERSON] and [PERSON] provided many important points and rules for successful stock trading [1; 2]. Table 1 lists the 21 index variables (are also called features) that are the most frequently used for recognizing the distinguished stocks in their related publications [1; 2].  
Among the most important issues in the development of an intelligent trading system is to decide what features should be used for stock performance prediction. One way is just following the suggestions of human stock traders and feeding all features into the developed system. In addition to using all these suggested features, this study presents a feature selection test and verifies the effectiveness of those features. Based on the definition and characteristic of the features, the 21 features are categorized into four groups: price-related features, trading volume features, company financial status-related features, and others. The results of the feature selection test are discussed in Section 4.  
In this study, the related data of the important indices with a weekly resolution are downloaded from the stock database and these important features are used as the input of the deep learning algorithm. Daily price-related data and daily trading volume data are also used as the input data of the deep learning algorithm because these two kinds of data (price and trading volume) are the most important for the prediction of stock price from the viewpoint of human stock traders. Using the additional daily resolution data can avoid missing significant dynamic in each week. When the weekly and daily data are used together, the two kinds of data should be synchronized based on time. The solution for data synchronization is explained in Section 3.  
\begin{table}
\begin{tabular}{l c c} \hline \multicolumn{1}{c}{**Important Index Variables (Features)**} & **Resolution** & **Group** \\ \hline Price/Earnings Ratio & Weekly & \\ Relative Strength & Weekly & \\ Price Average of 10 Weeks & Weekly & \\ Price Average of 30 Weeks & Weekly & \\ Price Average of 40 Weeks & Weekly & \\ Year Price High & Weekly & \\ Year Price Low & Weekly + Daily & \\ Price Open & Weekly + Daily & \\ Price High & Weekly + Daily & \\ Price Low & Weekly + Daily & \\ Price Close & Weekly + Daily & Trading volume features \\ Trading Volume & Weekly & \\ Earnings Per Share 3 Year Growth & Weekly & \\ Earnings Per Share 5 Year Growth & Weekly & \\ Quarterly Profit Growth Rate Relative to the Last Quarter & Weekly & Company financial status-related features \\ Quarterly Profit Growth Rate Relative to the Same Quarter in the Last Year & Weekly & \\ Quarterly Sales Growth Rate Relative to the Last Quarter & Weekly & \\ Quarterly Sales Growth Rate Relative to the Same Quarter in the Last Year & Weekly & \\ Common Shares Outstanding & Weekly & Others \\ Years in the Stock Market & & \\ \hline \end{tabular}
\end{table}
Table 1: Important index variables for stock performance prediction.