A Comparative Study for Stock Market Forecast Based on a New Machine Learning Model

Several works can be found across the literature explaining the complexity of forecasting stock market indices, due to their noisy, unpredictable, nonlinear dynamics as main characteristics of their behavior, and considering the application of different machine learning techniques as state-of-the-art predicting tools. In this respect, Ayyıldız [5] offers a literature review of machine learning algorithms applied to the prediction of stock market indices. Saboor et al. [6] delivered the forecast of the KSE 100 (Karachi Stock Exchange), the DSE 30 (Dhaka Stock Exchange) and the BSE Sensex (Bombay Stock Exchange) using methods such as Support Vector Regression (SVR), Random Forest Regression (RF) and Long Short-Term Memory (LSTM). In contrast, Aliyev et al. [7], offer the prediction of the RTS Index (Russian Stock Exchange), applying an ARIMA-GARCH model and an LSTM model. Ding et al. [8], performed similar work while producing the projection of the SSE (Shanghai Stock Exchange) using ARIMA and LSTM models. In their work, Haryono et al. [9] present the forecast of the IDX (Indonesia Stock Exchange) by applying different combinations of architectures using Convolutional Neural Networks (CNNs), Gated Recurrent Units (GRUs) and LSTM, implemented through TensorFlow (TF). Similarly to Haryono, Pokhrel et al. [10] performed the forecast of the NEPSE (Nepal Stock Exchange), employing CNN, GRU and LSTM architectures. Further, Singh [11] forecast the Nifty 50 (Indian Stock Market Index) using eight machine learning models, including Adaptive Boost (AdaBoost), k-Nearest Neighbors (KNN) and Artificial Neural Networks (ANNs), among others. As a final example, Harahap et al. [12] present the usage of Deep Neural Networks (DNNs), Back Propagation Neural Networks (BPNNs) and SVR techniques for the forecast of the N225. A summary and a brief discussion of the results presented in this section are given in Section 5.3.