# Importance of Event Binary Features in Stock Price Prediction
## 6 Conclusions  
Most of the stock price prediction studies use simple price values, such as closing price, high price, low price, and volume. Conversely, in the image recognition field, implicit feature values are extracted through convolution and pooling to perform deep learning. High-performance image recognition models can be created through this process.  
Therefore, we believe that we can develop a stock price prediction model with better performance by constructing meaningful features instead of using simple price features. Based on this, we proposed three models. Model 1 was developed through input feature refinement and normalization. The second model utilizes event binary features. The crucial investment points found in the charts are represented by event binary features, which are used as input for Model 2. Finally, the third model uses well-known technical analysis indicators. We valuated these three models under the same experimental conditions. As a result of the performance verification, all three models showed low error. The MSE value ranged from 0.0795 to 0.1171. The MAE value ranged from 0.2162 to 0.2498. Model 2 had the lowest error. Moreover, we performed additional fund simulations because it requires more than low error to be a good model. As a result, we could obtain some profit from Model 1 and Model 2. Among them, Model 2 was able to earn more profit than the domestic stock index, KOSPI/KOSDAQ.  
In conclusion, a model with binary features is most effective, and binary features that have event moments can play a critical role in deep learning stock price prediction. Some researchers argue that stock price prediction must reflect social economic situations, but our experiment results have proved that sufficient profit can be generated using only numerical data that consist of binary event features  
Figure 5: Profit graph of Model 2.  
and well-tuned features based on technical analysis. If we use Model 1 or Model 2 developed in this study, we can obtain recommendations of stocks that are expected to rise.  
Finally, we believe our conclusions clash with the efficient market hypothesis. According to this hypothesis, the stock fluctuations are close to random because of the efficient sharing of disclosed information. This hypothesis has been confirmed by Model 3. In contrast, the input features of Model 1 and 2 are not well known, but we have derived a model that yields a better return than the market. This result means that the presentation of new input features and the event binary features can be very important factors in stock price prediction.  
This study can be extended to various time series data (oil price, gold price, real estate price, etc.) in the future. In addition, it is possible to identify various factors and precautions to be considered with stock price prediction using deep learning. We plan to propose a unified platform by combining this model with an automated trading system in future studies. We would also like to apply CNN, recurrent neural network, etc., instead of a simple neural network structure. If we combine our high-performance features with more complex neural network structures, we may be able to derive better prediction models.  
Y.S.: development, writing, and editing, J.L.: design and Writing--Review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF-2018R1D1A1B07040312).  
We would like to thank Editage (www.editage.co.kr) for English language editing.  
We declare no conflict of interest.