# Importance of Event Binary Features in Stock Price Prediction
###### Abstract  
In Korea, because of the high interest in stock investment, many researchers have attempted to predict stock prices using deep learning. Studies to predict stock prices have been continuously conducted. However, the type of stock data that is suitable for deep learning has not been established, and it has not been confirmed that the developed stock prediction model can actually result in a profit. To date, designing a good deep learning model depends on how well the user can extract the features that represent all the characteristics of the training data. Among the various available features for training and test data, we determined that the use of event binary features can make stock price prediction models perform better. An event binary feature refers to a 0 or 1 value describing whether an indicator is satisfied (1) or not (0) for any given day and stock. We proposed and compared a stock price prediction model with three different feature combinations to verify the importance of binary features. As a result, we derived a prediction model that defeated the market (KOSPI and KODAQ (KOSPI (Korea Composite Stock Price Index) and KOSDAQ (Korean Securities Dealers Automated Quotations) is Korean stock indices)). The results suggest that deep learning is suitable for stock price prediction. deep learning; stock price prediction; novel input features; event binary features; technical analysis +
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who are not wealthy unable to collect or lend money. As a result of this low interest rate, Koreans are turning their attention to investment in stocks, which is a rather risky investment method [5]. The increased interest in stock investments is boosting research on stock price prediction. If such research can lead to suitable prediction of stock prices, the low interest rates of saving accounts can be overcome, creating opportunities for investors to easily expand their assets. As interest in stock investments has increased, many researchers around the world have attempted to predict future stock prices using artificial intelligence. Stock price prediction research has been historically popular, but ultimately unsatisfactory. However, as deep learning has emerged, the possibility of stock price prediction has increased. To structure stock price input data suitable for deep learning applications is fundamentally difficult. It is also difficult to construct a prediction model because of the randomness inherent in price fluctuations [6]. To predict fluctuations in stock price prediction more accurately, an in-depth analysis of input features is essential. Good performance of deep learning depends on how well the features that represent the characteristics of the whole training/test dataset are extracted. In addition, the compatibility between the input features and the target vector must be matched, which has been proven already through previous studies [7]. In this paper, we propose three models with an emphasis on input features. Previously, only simple input features, such as easy-to-use price information, were used to set input features in deep learning for stock price forecasting. However, we need to use more refined and more implicit input features to develop better prediction models. A deep learning model with good performance ultimately provides sufficient returns to users to achieve the final goal of stock price prediction. The contributions of this paper are as follows. * We developed three stock price prediction models to determine the importance of input feature selection using input features with different characteristics. We organized models by selecting and calculating input features that are expected to be able to describe the reason for fluctuation. * The first model uses 315 input features based on technical analysis. The second model uses 250 event binary features to represent the moment of stock price fluctuation. The third model is based on 13 existing well-known technical analysis indicators. * The 315 input features used in Model 1 were developed as follows.