## 2 Background and Literature Review  
Since the risk factor of the frontier market is fully undiscovered in the research field, we have not found any related work; instead, we have gone through some of the time series related works that have been used the up-to-date analysis approaches. [PERSON] et al. [4] proposed a modified SVR model which separates the time-series data into linear and non-linear parts. Their model combined with the linear model and the SVR model. The linear model predicts the linear part, whereas the SVR model predicts the non-linear part. [PERSON] et al. [5] showed a comparative work between the Linear Regression (LR) and Support Vector Regression (SVR) models. They achieved RMSE values of 12.54 and 12.87, respectively, for the LR and SVR models. [PERSON] [6] showed a comparative work between time series models such as GARCH (1,1), EGARCH (1,1) and TGARCH (1,1), and deep learning models such as ANN and LSTM. They mentioned that none of the time series models captured the fluctuations properly. They found that long short-term memory (LSTM) accurately captured the patterns as it remembers the previous pattern of the data. [PERSON] and [PERSON] [7] showed a machine learning approach for predicting Stock Price; they used a Support Vector Machine (SVM) model for their investigation. They mentioned that the neural network finds the local minima and SVM finds the global minima. However, finding local minima may lead the model to underfitting or overfitting issues, which prevents the model from being generalized. Therefore, they chose SVM to predict their stock price data. They achieved a mean accuracy within 49.5 percent and 50 percent. [PERSON] and [PERSON] [8] showed a neural network approach to investigate its ability to predict complex market stock price prediction. They compared the model with multiple discriminant analysis (MDA) methods. In their experiment, the NN approach outperformed the MDA methods. [PERSON] et al. [9] showed a deep learning ensemble method using a set of stacked denoising autoencoders (SDAE) for the base model. The prediction values from the SDAE models are averaged together to form the final prediction value. They compared their proposed model with a random walk (RW), Markov regime-switching model (MRS), feedforward neural network (FNN) and Support Vector Regression (SVR) ensembled model; their proposed model's result outperformed all the aforementioned models. [PERSON] et al. [10] showed multiple machine learning models and one neural network model to compare the performance of the models. They performed Logistic Regression (LR), linear discriminant analysis (LDA), RandomForest (RF), XGBoost (XGB), Quadratic Discriminant Analysis (QDA), Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) on a bitcoin daily price dataset. The LSTM model has achieved the best accuracy among all the aforementioned models. [PERSON] et al. [11] showed a comparative analysis, where they made the comparison between deep learning methods such as Long short-term Memory (LSTM), Convolutional Neural Network (CNN), Multi-Layer Perceptron (MLP) and Artificial Neural Network (ANN). The CNN model has shown the best result among all the models. [PERSON] et al. [12] also proposed a comparative work between three deep learning models, which are: LSTM, RNN and CNN-Sliding Window model. [PERSON] et al. [13] showed a Multilayer Perceptron Neural Network approach on different stock price datasets. [PERSON] et al. [14] obtained 72 percent accuracy for the model to predict stock price data by using the LSTM model. Their experiment precisely states that the LSTM model can play a better forecasting effect. [PERSON] et al. [15] showed that the BiLSTM model outperforms the regular unidirectional LSTM model due to the bi-directional learning process. BiLSTM learns data from both the forward and backward directions. [PERSON] and [PERSON] [16] showed multiple deep learning models such as Recurrent Neural Network (RNN), Long short-term memory and Generalized Linear Model (GLM) for predicting the stock price.