# A Novel Approach to Short-Term Stock Price Movement Prediction using Transfer Learning
## 2 Related Works  
### Stock Market Prediction  
Several studies have attempted financial time series predictions. Specifically, most early studies tended to employ statistical methods, such as the weighted moving average (WMA) [21], the generalized autoregressive conditional heteroskedasticity (GARCH) model [22], and the autoregressive integrated moving average (ARIMA) [23]. However, these approaches made many statistical assumptions, such as linearity and normality, whereas the financial time series is non-linear. Therefore, these approaches are not suitable for forecasting a stock price. Apart from the statistical models, machine learning techniques have been extensively applied to financial time series prediction tasks due to their capability in nonlinear mapping and generalization [3; 4; 24; 25; 26; 27]. For instance, [PERSON] et al. [25] presented the performance of a multi-layer perceptron (MLP) model and compared it to the performance of an ARIMA model in forecasting stock prices on the New York Stock Exchange (NYSE). They concluded that the MLP model performed better than the ARIMA model in terms of the mean squared error (MSE). [PERSON] et al. [27] used MLP and SVM to predict the direction of movement in the daily Istanbul Stock Exchange (ISE) National 100 Index. Experimental results indicated that their models outperformed the ordinary least square (OLS) regression model in terms of prediction accuracy. Although MLP can be used in financial time series prediction, several studies showed that MLP has some limitations in learning the patterns, as stock data have tremendous amounts of noise, and a high dimension. MLP often exhibits inconsistent and unpredictable performance on noisy data [28; 29].  
In recent years, with the significant advances in deep learning techniques, recurrent neural networks have emerged as a promising model for handling sequential data in various tasks such as natural language processing, speech recognition, and computer vision. Moreover, several studies proved that RNN including LSTM cells is the most useful model in the financial time series prediction problem [30; 31; 32; 33; 34; 35; 36]. For example, [PERSON] et al. [30] introduced a stock price movement prediction model based on LSTM. A lot of basic variables for stocks (e.g., high price, low price, closing price, volume, and adjusted closing price) are considered as input features. The model was trained on 900,000 samples and tested using another 311,361 samples. They concluded that LSTM could attain satisfactory performance in predicting China's index price movements. [PERSON] and [PERSON] [34] employed an LSTM network to predict KOSPI index prices in the next day. They tested their method on KOSPI index data from 2000 to 2016 and found that the method achieved satisfactory performance. In [33], the authors integrated wavelet transforms (WTs) and recurrent neural network based on an artificial bee colony algorithm. The closing price values of the Dow Jones Industrial Average Index (DJIA), the London FTSE-100 Index(FTSE), Tokyo Nikkei-225 Index (Nikkei), and Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) were predicted. Results showed that their proposed approaches can maximize profits. [PERSON] et al. [35] employed a denoising approach using WTs as data-preprocessing, and then applied stack autoencoders (SAEs) to extract deep daily features. Their proposed framework outperformed their baseline methods in terms of MSE. However, most of the previous studies aimed at finding a suitable framework that can learn from non-linear and noisy stock data based on assumptions about sufficient data, whereas, in fact, collecting more stock data is difficult with only about 240 data points available in a year. Meanwhile, our work aims at finding a novel approach that can effectively train an LSTM network despite having an insufficient amount of data.