# A Novel Approach to Short-Term Stock Price Movement Prediction using Transfer Learning
## 2 Related Works
### Feature Selection for Stock Price Prediction  
Stock prices are dramatically affected by many factors [1]. Some basic factors (financial ratios, technical indicators, macroeconomic indices, and competitors) should be used as important factors affecting the rise and fall in stock values. However, each researcher has chosen their input features differently for their prediction models [37]. In the following subsection, choosing input features for stock price movement prediction in recent studies is presented.  
Several studies have used technical indicators (TIs) as input variables in forecasting future stock prices [26; 38; 39; 40; 41; 42; 43]. For example, for every day of training, to construct input feature vectors, the authors in [40; 42] calculated 10 technical indicators (e.g., simple moving average, exponential moving average, and relative strength index) from raw time series data including opening price, closing price, high and low prices, and trading volume. Thus, they fed these technical indicator values into their models (based on SVM, ANN, and RF). Experimental results showed satisfactory prediction accuracy. Although technical indicators have been used widely in stock market prediction tasks, the selection of suitable indicators to form model inputs from among various technical indicators is still a challenging task.  
Other studies were aimed at predicting stock prices given textual information from the financial news [44; 45; 46]. For instance, [PERSON] et al. [44] converted newspaper articles into distributed representations via paragraph vectors and modeled the temporal effects of past events with an LSTM on predicting opening prices of stocks on the Tokyo Stock Exchange. [PERSON] et al. [47] revealed that investor's emotions derived from Twitter have impacts on stock indicators. [PERSON] et al. [48] first introduced a novel word representation method to extract important features from text data. Then, they used an SVM model to predict the direction of stock price movements in the next day. Those studies proved that textual information can be used in financial time series prediction tasks. However, their objective was to find effective feature extraction methods to help improve prediction performance. Moreover, most of the research assumed that a company's news was always published each day, which is a rare case in reality.  
[PERSON] and [PERSON] [20] deployed an LSTM network in predicting the directional movement of constituent stocks of the S&P 500 from 1992 to 2015. In their research, they used 240 one-day returns as input variables for the prediction model. They divided the entire time period into 23 study periods, where each study period had 750 days (approximately three years). Their model was compared with other classifier models, such as RF, DNN, and logistic regression. They provided an in-depth guide on data preprocessing, as well as the development of LSTM for financial time series prediction tasks, and concluded that their approach outperformed other baselines over 23 study periods in terms of average prediction accuracy, shape ratio, and profit. However, in their study, they only used one-day returns of the target stock to form input models. In contrast, our work takes relationships between other stocks and the target stock into account in designing effective input. Thus, the proposed approach is compared to the work by [PERSON] and [PERSON] [20]. Results show that the considered relationships can enhance the performance of the prediction model.