# Importance of Event Binary Features in Stock Price Prediction
## 3 Design and Development of the Stock Price Prediction Model
### Model 1: 315 Novel Input Features Model  
Model 1 is the next version of a stock price prediction model using 715 novel input features presented in a previous study [7]. A total of 715 input features were presented according to our own technical analysis and opinions of other stock chart analysts. Table 1 is a brief description of the configuration of the 715 input features [7]. A previous model using these 715 features was able to make a profit. However, we determined that there are excessive duplicate meaningful input features. In addition, there are some possibilities to improve performance through feature value preprocessing and normalization. We refined these 715 feature values and removed less meaningful features to improve the performance of the prediction model. Figure 2: Deep neural network structure used in our study. The process of feature refinement is as follows. First, distributions of each of the 715 features were calculated in the form of a histogram to determine the value distributions. We found that there are some noisy features that are either biased or single-valued. These data are likely to interfere with training performance. An example of the data distribution is shown in Table 2. Valid values mostly show Gaussian distributions. Outliers are sparse among these data, and there are also some biased data. However, they can be cleared by the data normalization algorithm that will be discussed in Section 3.6. The histograms presented at the bottom of Table 2 show examples of heavily biased, even single-valued, features. These features should be eliminated because they definitely have no significant effect on performance improvement. Through this feature refinement process, we reduced the number of features from 715 to 315. Table 3 shows details of the input features of Model 1. It has a total 315 input features. Here, moving average refers to the average stock price. The moving average of the closing price is denoted as _MA_, while the moving average of volume is expressed as _VMA_. For example, the closing price of a stock at trading date \(t\) is expressed as _Close\({}_{t}^{s}\)_ whereas the moving average of the closing price over five days can be expressed as _MA5\({}_{t}^{s}\)_. Equations (3) and (4) show examples of 5-day moving averages of the closing price and volume [20]. \[MA5_{t}^{s}=\frac{1}{5}\sum_{k=0}^{4}Close_{t-k}^{s} \tag{3}\] \begin{table}
\begin{tabular}{l c} \hline \hline
**Description of 715 Features** & **Number of Features** \\ \hline
1. Gradient of moving average line & 10 \\
2. Sum of long-term moving average gradient & 2 \\
3. Gradient of 5-days/20-days volume moving average & 3 \\
4. Change in volume moving average & 60 \\
5. Difference between yesterday’s and today’s moving average & 40 \\
6. The change of long and short-term moving average and the point of & 220 \\ golden/dead cross & 140 \\
8. Disparity of moving average & 10 \\
9. Disparity of 60-days/120-days volume moving average & 30 \\
10. Disparity of 20-days/60-days volume moving average & 30 \\
11. Disparity of 5-days/20-days volume moving average & 30 \\
12. Change in closing price on a specific day & 110 \\
13. Other simple price indicators & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Novel input features presented in previous studies.