Stock index direction forecasting using an explainable eXtreme Gradient Boosting and investor sentiments

Huang, Xiaoru; Zhu, Yingke; Su, Zhihao; Fu, Zhe; Shimada, Takehiko

doi:10.1016/j.najef.2022.101848

Cited by 13 publications

(3 citation statements)

References 23 publications

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“…Moreover, unlike other approaches based on proxy models, Shapley values are derivable for any tree-based model (Jabeur et al, 2021b ). These advantages have been exploited in the context of stock market prediction (Deng et al, 2023 ; Khalfaoui et al, 2023 ).…”

Section: Methodsmentioning

confidence: 99%

An interpretable system for predicting the impact of COVID-19 government interventions on stock market sectors

et al. 2023

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Evaluating and understanding the financial impacts of COVID-19 has emerged as an urgent research agenda. Nevertheless, the impacts of government interventions on stock markets remain poorly understood. This study explores, for the first time, the impact of COVID-19 related government intervention policies on different stock market sectors using explainable machine learning-based prediction models. The empirical findings suggest that the LightGBM model provides excellent prediction accuracy while preserving computationally efficient and easy explainability of the model. We also find that COVID-19 government interventions are better predictors of stock market volatility than stock market returns. We further show that the observed effects of government intervention on the volatility and returns of ten stock market sectors are heterogeneous and asymmetrical. Our findings have important implications for policymakers and investors in terms of promoting balance and sustaining prosperity across industry sectors through government interventions.

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Section: Methodsmentioning

confidence: 99%

An interpretable system for predicting the impact of COVID-19 government interventions on stock market sectors

et al. 2023

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“…The list of technical indicators based on momentum used is simple moving average, exponential moving average, weight moving average, relative strength index, money flow index, Williams indicator, momentum, accelerator oscillator, balance of power, and real moving average. The list of technical indicators based on volatility used is: true range, average true range, Bollinger bands, triple exponentials average, average directional movement index, and arc oscillator [16]- [21].…”

Section: Technical Indicatorsmentioning

confidence: 99%

Feature selection using non-parametric correlations and important features on recursive feature elimination for stock price prediction

Priyatno,

Ramadhan Sudirman,

Musridho

2024

IJECE

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Stock price prediction using machine learning is a rapidly growing area of research. However, the large number of features that can be used can complicate the learning process. The feature selection method that can be used to overcome this problem is recursive feature elimination. Standard recursive feature elimination carries the risk of producing inaccurate algorithms because the top-ranked features are not necessarily the most important features. This research proposes a feature selection method that combines important features and nonparametric correlation in recursive feature elimination for stock price prediction. The data features used are technical indicators and stock price history. The recursive feature elimination method is modified with important features and nonparametric correlation features. The strategy for combining important features and non-parametric features is average weight, 25:75% weight, 75:25% weight, maximum weight, and minimum weight. The performance evaluation results show that the proposed feature selection method succeeded in obtaining small error values. The proposed method for predicting PT Bank Rakyat Indonesia Tbk (BBRI) stock prices obtains mean squared error, root mean square error, mean absolute error, and mean absolute percentage error evaluation values of 0.0000336, 0.00577, 0.00459, and 1.78%, respectively. This shows that recursive feature elimination with feature selection that combines important features and non-parametric correlation works better than the original recursive feature elimination at predicting stock prices.

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“…Boosting [ 57 ]: It is a sequential ensemble network that improves ensemble performance by valuing the wrong data from previous classifiers. Initially, each data point in the dataset is assigned the same weight.…”

Section: Related Workmentioning

confidence: 99%

A Two-Stage Voting-Boosting Technique for Ensemble Learning in Social Network Sentiment Classification

Cui

Han

Duan

et al. 2023

Entropy

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In recent years, social network sentiment classification has been extensively researched and applied in various fields, such as opinion monitoring, market analysis, and commodity feedback. The ensemble approach has achieved remarkable results in sentiment classification tasks due to its superior performance. The primary reason behind the success of ensemble methods is the enhanced diversity of the base classifiers. The boosting method employs a sequential ensemble structure to construct diverse data while also utilizing erroneous data by assigning higher weights to misclassified samples in the next training round. However, this method tends to use a sequential ensemble structure, resulting in a long computation time. Conversely, the voting method employs a concurrent ensemble structure to reduce computation time but neglects the utilization of erroneous data. To address this issue, this study combines the advantages of voting and boosting methods and proposes a new two-stage voting boosting (2SVB) concurrent ensemble learning method for social network sentiment classification. This novel method not only establishes a concurrent ensemble framework to decrease computation time but also optimizes the utilization of erroneous data and enhances ensemble performance. To optimize the utilization of erroneous data, a two-stage training approach is implemented. Stage-1 training is performed on the datasets by employing a 3-fold cross-segmentation approach. Stage-2 training is carried out on datasets that have been augmented with the erroneous data predicted by stage 1. To augment the diversity of base classifiers, the training stage employs five pre-trained deep learning (PDL) models with heterogeneous pre-training frameworks as base classifiers. To reduce the computation time, a two-stage concurrent ensemble framework was established. The experimental results demonstrate that the proposed method achieves an F1 score of 0.8942 on the coronavirus tweet sentiment dataset, surpassing other comparable ensemble methods.

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Stock index direction forecasting using an explainable eXtreme Gradient Boosting and investor sentiments

Cited by 13 publications

References 23 publications

An interpretable system for predicting the impact of COVID-19 government interventions on stock market sectors

An interpretable system for predicting the impact of COVID-19 government interventions on stock market sectors

Feature selection using non-parametric correlations and important features on recursive feature elimination for stock price prediction

A Two-Stage Voting-Boosting Technique for Ensemble Learning in Social Network Sentiment Classification

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