Empirical Analysis for Stock Price Prediction Using NARX Model with Exogenous Technical Indicators

The file attached to this record is the Publisher's final version. Open access article.Stock price prediction is one of the major challenges for investors who participate in the stock markets. Therefore, different methods have been explored by practitioners and academicians to predict stock price movement. Artificial intelligence models are one of the methods that attracted many researchers in the field of financial prediction in the stock market. This study investigates the prediction of the daily stock prices for Commerce International Merchant Bankers (CIMB) using technical indicators in a NARX neural network model. The methodology employs comprehensive parameter trails for different combinations of input variables and different neural network designs. The study seeks to investigate the optimal artificial neural networks (ANN) parameters and settings that enhance the performance of the NARX model. Therefore, extensive parameter trails were studied for various combinations of input variables and NARX neural network configurations. The proposed model is further enhanced by preprocessing and optimising the NARX model’s input and output parameters. The prediction performance is assessed based on the mean squared error (MSE), R-squared, and hit rate. The performance of the proposed model is compared with other models, and it is shown that the utilisation of technical indicators with the NARX neural network improves the accuracy of one-step-ahead prediction for CIMB stock in Malaysia. The performance of the proposed model is further improved by optimising the input data and neural network parameters. The improved prediction of stock prices could help investors increase their returns from investment in stock markets

Real time mobile based license plate recognition system with neural networks

In this paper, the implementation of localizing and recognizing license plate in real time environment with a neural network using a mobile device is described. The neural networks used in this research are Convolutional Neural Network (CNN) and Backpropagation Feed Forward Neural Network (BPFFNN). Image processing algorithm for pre-processing, localization and segmentation is chosen based on its ability to cope with limited computational resource in mobile device. The proposed license plate localization steps include combination of Sobel edge detection method and morphological based method. Detected license plate image is segmented using connected component analysis (CCA) and bounding box method. Each cropped character is fed into CNN or BPFFNN model for character recognition process. The neural network model was pretrained using desktop computer and then later exported and implemented in Android mobile device. The experiment was conducted in a moving vehicle on selected driving routes. The results obtained showed that CNN performed better compared to BPFFNN in a real time environment

A holistic auto-configurable ensemble machine learning strategy for financial trading

Financial markets forecasting represents a challenging task for a series of reasons, such as the irregularity, high fluctuation, noise of the involved data, and the peculiar high unpredictability of the financial domain. Moreover, literature does not offer a proper methodology to systematically identify intrinsic and hyper-parameters, input features, and base algorithms of a forecasting strategy in order to automatically adapt itself to the chosen market. To tackle these issues, this paper introduces a fully automated optimized ensemble approach, where an optimized feature selection process has been combined with an automatic ensemble machine learning strategy, created by a set of classifiers with intrinsic and hyper-parameters learned in each marked under consideration. A series of experiments performed on different real-world futures markets demonstrate the effectiveness of such an approach with regard to both to the Buy and Hold baseline strategy and to several canonical state-of-the-art solutions

A Novel Approach for Stock Price Prediction Using Gradient Boosting Machine with Feature Engineering (GBM-wFE)

The prediction of stock prices has become an exciting area for researchers as well as academicians due to its economic impact and potential business profits. This study proposes a novel multiclass classification ensemble learning approach for predicting stock prices based on historical data using feature engineering. The proposed approach comprises four main steps, which are pre-processing, feature selection, feature engineering, and ensemble methods. We use 11 datasets from Nasdaq and S&P 500 to ensure the accuracy of the proposed approach. Furthermore, eight feature selection algorithms are studied and implemented. More importantly, a feature engineering concept is applied to construct two new features, which are appears to be very auspicious in terms of improving classification accuracy, and this is considered the first study to use feature engineering for multiclass classification using ensemble methods. Finally, seven ensemble machine learning (ML) algorithms are used and compared to discover the ultimate collaboration prediction model. Besides, the best feature selection algorithm is proposed. This study proposes a novel multiclass classification approach called Gradient Boosting Machine with Feature Engineering (GBM-wFE) and Principal Component Analysis (PCA) as the feature selection. We find that GBM-wFE outperforms the previous studies and the overall prediction results are auspicious, as MAPE of 0.0406% is achieved, which is considered the best result compared to the available studies in the literature

Homogeneous ensemble Neural Cognition for CIMB stock price prediction

Stock market forecasting has always been a topic of research interest due to the lucrative profit. However, stock market forecasting is a complicated task because of the not linear, volatile and random nature of the stock market. Literature reviews have shown that Artificial Neural Network (ANN) is an appropriate technique to be used in forecasting activities. However, there are certain limitation in single neural network. Therefore ensemble tecniques is introduced to overcome the limitation of single neural network. Ensemble techniques overcome the limitation of single learner by covering the different area of the problem search space aggregating multiple learner, consequently, reducing the error of estimation. Hence, this research investigates the performance of homogeneous ensemble neural network in closing price prediction. Ensemble Neural Network (ENN) has shown to be able to overcome the limitation of single neural network by covering the different area of the problem search space aggregating multiple single neural network, consequently, reducing the error of estimation. As the thesis title implies, the investigation will be focusing on homogeneous ensemble neural network which means multiple same architecture neural network will be used in the ensemble neural network. The two single neural network architectures as the building block for homogeneous ENN are used in this thesis which are FeedForward Neural Network (FFNN) and Recurrent Neural Network (RNN). These two architectures are among the ANN architectures that are widely adopted in ANN research. As shown in the literature review, both architectures shown to perform well in different studies which using different parameters and network configuration that have been conducted. Hence, it is also the interest of this thesis to the performance comparison of FFNN and RNN as well in this case study. The resulted single FFNN and RNN learner conducted in the experiment will then be used in experiment of homogeneous ENN. The results of these experiments are compared against each other to see how well the homogeneous ENN can perform better than single FFNN and RNN. The study case adopted in this thesis is CIMB stock listed in KLSE. CIMB is a well established financial bank company in Malaysia. The stock is selected due the volatile pattern trend that is suitable as the study case. In this study, a collection of input parameters which include technical data and economic variables are presented to the ANN to forecast the stock closing price of CIMB. The performance of the different ANN architectures are empirically evaluated based on Mean Square Error (MSE) and prediction accuracy