# A Novel Approach to Short-Term Stock Price Movement Prediction using Transfer Learning
## 6 Experimental Results
### Effectiveness of the Proposed Approach versus Benchmark Models  
In this subsection, we evaluate the performance of our model compared to four baseline classifiers, which are SVM, RF, KNN, and an existing model using LSTM cells [20]. First, the input vector for training four baseline models is constructed. Specifically, in constructing input vectors for the SVM, RF, and KNN models, five returns are stacked into one feature vector \(R_{t}^{s}=\{r_{t,1}^{s},r_{t,2}^{s},r_{t,5}^{s},r_{t,10}^{s},r_{t,20}^{s}\}\), which is used as an input vector. Unlike these models, in [20], they used 240 one-day returns, \(r_{t-p^{\prime},1}^{s}\), to design input vectors, in which \(p^{\prime}\in\{0,1,2,...,239\}\). Therefore, the input model is formed as \(R_{t}^{s}=\{r_{t-239,1}^{s},r_{t-238,1}^{s},...,r_{t,1}^{s}\}\). All returns, \(r_{t,p}^{s}\), are calculated by using Equation (6). In addition, targets of the baseline models are defined in the same way as the targets of our model in Section 4. Two kinds of non-linear kernels (i.e., the radial basis function (RBF) kernel and the polynomial kernel) of SVM are considered. Hyperparameters of SVM (e.g., \(\gamma\) of RBF kernel, degree \(d\) of polynomial, and regularization \(C\)), hyperparameters of RF (e.g., the number of trees \(B\), and maximum depth \(J\)), and hyperparameters of KNN (e.g., the number of neighbors \(K\)) are chosen by grid search for each branch. For choosing hyperparameters of the SVM model, the searching interval for \(\gamma\), \(d\), and \(C\) is \([0.5,10.5]\) with a step size of \(0.5\), \([1,4]\) with a step size of \(1\), and \([0.5,1.5,10,100]\), respectively. The search range \([10,100]\) with a step size of \(10\) and range \([1,5]\) with a step size of \(1\) are considered for number of trees and maximum depth of RF model, respectively. Also, the search range \([1,50]\) with a step size of \(1\) is considered for number of neighbors of KNN model. The model from [20] consists of the input layer with one feature and 240 timesteps, one LSTM layer with \(h=25\) hidden neurons and a dropout of \(0.1\), and the output layer with two neurons and softmax activation function. As shown in Table 6, the performance of our proposed approach is the highest, with a 60.70% average accuracy in the study periods, while the SVM, RF, and KNN methods show low performance with 51.57%, 51.49%, and 51.77% average accuracy, respectively. Note that even when our approach uses only one-day returns of COI stock, it achieves 53.74% average accuracy, which is higher than SVM, RF, and KNN models by 2.17 percentage points, 2.25 percentage points, and 1.97 percentage points, respectively. It indicates that simple classifier models cannot extract input features effectively. Moreover, the overfitting problem may occur in training the SVM, RF, and KNN models due to the insufficient amount of data, whereas our approach based on transfer learning can reduce the influence of this problem. Compared to the existing model from [20], our approach improves the average accuracy by 3.34%. In [20], the model predicted COI stock price movement on day \(t+1\) based on only one-day returns of the COI stock over one year. In fact, the direction of stock price movement is affected by other