# A Novel Approach to Short-Term Stock Price Movement Prediction using Transfer Learning  
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Over the past few decades, recurrent neural networks (RNNs) [6; 7] have been used particularly for time series prediction because this type of network includes states that can capture historical information from an arbitrarily long context window. In our study, we use long short-term memory (LSTM) cells for sequence learning of financial market predictions. LSTM is a part of RNNs, and using LSTM cells can alleviate the problem of vanishing (and exploding) gradients [8]. However, in order to train LSTM cells effectively, the overfitting problem from an insufficient number of training samples should be considered [9]. Particularly, in stock price prediction, the number of data points that we can collect on a daily basis is only about 240 in a year. That is not sufficient, considering the number of model parameters. Currently, several methods have been found to avoid the overfitting problem, such as regularization techniques (i.e., L1 and L2 regularization [10]), dropout [11], data augmentation [12], and early stopping [13]. In recent years, transfer learning has been popularly used in training deep neural networks (DNNs) owing to the effectiveness of this technique in helping to reduce training time and to enhance prediction accuracy, despite a small amount of training data [14; 15; 16; 17; 18; 19]. However, transfer learning has not been used much in time series prediction tasks. In this paper, we propose a framework called deep transfer with related stock information (DTRSI) to predict stock price movement, to mitigate the overfitting problem caused by an insufficient number of training samples, and to improve prediction performance by using the relationships between stocks. Specifically, our framework has two phases. First, a base model using LSTM cells is pre-trained with a large amount of data (which were obtained from a number of different stocks) to optimize initial training parameters. Secondly, the base model is fine-tuned with a small amount of the target stock data to predict the target stock's price movement. The fine-tuned model is called a prediction model. For convenience, the target stock is called the company of interest (COI) stock. In addition, the prediction model is trained by using the different sets of input features that are constructed based on information about stocks related to the COI stock. In detail, we first calculate a one-day return, which shows the change of a stock's closing price between two consecutive days. The first set of input features includes only one-day returns of the COI stock. The second set is the combination of one-day returns of COI stock and the index (e.g., Korea Composite Stock Price Index 200 (KOSPI 200) and Standard&Poor's 500 (S&P 500)). The third set is the combination of one-day returns of the COI stock, the index, and stocks related to the COI stock (i.e., the highest cosine similarity to the COI stock, similar field to the COI, and the highest market capitalization). In this study, to validate the proposed framework, as COI stocks, we choose top-five companies on the KOSPI 200 and top-five companies from five popular sectors in the S&P 500 in terms of market capitalization. The KOSPI 200 represents the benchmark indicator of the Korean capital market, and the S&P 500 is a globally important benchmark indicator for the United States (US) market. The main contributions of our work can be summarized as follows:  
* First, our work considers the overfitting problem caused by an insufficient amount of data in forecasting stock price movements.