Homogeneous Ensemble FeedForward Neural Network in CIMB Stock Price Forecasting

Gan, Kim Soon; Chin, Kim On; Anthony, Patricia; Chang, Sim Vui

doi:10.1109/iicaiet.2018.8638452

Cited by 7 publications

(7 citation statements)

References 29 publications

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“…The study shows that the ensemble technique outperformed single classifier in terms of accuracy. Gan et al [42] proposed an ensemble of feedforward neural networks for predicting the stock closing price and reported a higher accuracy in prediction as compared with single feedforward neural networks.…”

Section: Related Work Evaluationmentioning

confidence: 99%

A comprehensive evaluation of ensemble learning for stock-market prediction

2020

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Section: Related Work Evaluationmentioning

confidence: 99%

A comprehensive evaluation of ensemble learning for stock-market prediction

2020

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“…Researchers in [43]proposed AdaBoost and the stacking ensemble method to predict the stock price movement and achieved a Kappa of 0.5516% as the best result; however, our model achieved a Kappa of (0.99) with the NASDAQ and Kappa of (1) with the S&P 500. Furthermore, our proposed model surpasses the proposed ensemble model in [44], in which an RMSE of 0.00 and 0.04 achieved for S&P 500 and NASDAQ respectively, whereas they had an RMSE of 0.02 in the best situation.…”

Section: Gbm-wfe Approach Benchmarkingmentioning

confidence: 68%

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

Nabi

Saeed

Harron

2020

KJAR

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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.

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“…The authors of [53] combined results of bivariate empirical mode decomposition, interval Multilayer Perceptrons, and an interval exponential smoothing method to predict crude oil prices. Other interesting approaches using ensembles are the use of multiple feed forward neural networks [54], multiple artificial neural networks with model selection [55], among others.…”

Section: Background and Related Workmentioning

confidence: 99%

A Holistic Auto-Configurable Ensemble Machine Learning Strategy for Financial Trading

et al. 2019

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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.

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Homogeneous Ensemble FeedForward Neural Network in CIMB Stock Price Forecasting

Cited by 7 publications

References 29 publications

A comprehensive evaluation of ensemble learning for stock-market prediction

A comprehensive evaluation of ensemble learning for stock-market prediction

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

A Holistic Auto-Configurable Ensemble Machine Learning Strategy for Financial Trading

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