An integrated approach of ensemble learning methods for stock index prediction using investor sentiments

XGBoost: A Scalable Tree Boosting System

Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569 · see citing works

A fast and elitist multiobjective genetic algorithm: NSGA-II

Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.996017 · Cited by 50,053 · see citing works

A Unified Approach to Interpreting Model Predictions

Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707 · see citing works

Bagging predictors

Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325 · see citing works

Ensemble methods in machine learning

Dietterich, T. G. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-45014-9_1 · Cited by 8,408 · see citing works

17. A Value for n-Person Games

Shapley, L. S. (1953) · Contributions to the Theory of Games (AM-28), Volume II · doi:10.1515/9781400881970-018 · Cited by 6,950 · see citing works

Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms

Srinivas, N., & Deb, K. (1994) · Evolutionary Computation · doi:10.1162/evco.1994.2.3.221 · Cited by 6,935 · see citing works

Investor Sentiment and the Cross‐Section of Stock Returns

BAKER, M., & WURGLER, J. (2006) · The Journal of Finance · doi:10.1111/j.1540-6261.2006.00885.x · Cited by 6,049 · see citing works

An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization

Dietterich, T. G. (2000) · Machine Learning · doi:10.1023/a:1007607513941 · Cited by 2,971 · see citing works

An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants

Bauer, E. J., & Kohavi, R. (1999) · Machine Learning · doi:10.1023/a:1007515423169 · Cited by 2,627 · see citing works

Good Day Sunshine: Stock Returns and the Weather

Hirshleifer, D. A., & Shumway, T. (2003) · The Journal of Finance · doi:10.1111/1540-6261.00556 · Cited by 1,744 · see citing works

Investor sentiment and the near-term stock market

Brown, G. W., & Cliff, M. T. (2003) · Journal of Empirical Finance · doi:10.1016/j.jempfin.2002.12.001 · Cited by 1,336 · see citing works

Market liquidity as a sentiment indicator

Baker, M. P., & Stein, J. C. (2004) · Journal of Financial Markets · doi:10.1016/j.finmar.2003.11.005 · Cited by 1,245 · see citing works

Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval

Tao, D., Tang, X., Li, X., & Wu, X. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.134 · Cited by 880 · see citing works

A novel hybridization of artificial neural networks and ARIMA models for time series forecasting

Khashei, M., & Bijari, M. (2010) · Applied Soft Computing · doi:10.1016/j.asoc.2010.10.015 · Cited by 826 · see citing works

Applications of deep learning in stock market prediction: Recent progress

Jiang, W. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115537 · Cited by 774 · see citing works

2020 25th International Conference on Pattern Recognition (ICPR)

(2021) · doi:10.1109/icpr48806.2021 · Cited by 649 · see citing works

Literature review: Machine learning techniques applied to financial market prediction

Henrique, B. M., Sobreiro, V. A., & Kimura, H. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.01.012 · Cited by 585 · see citing works

Earnings news and small traders

Lee, C. M. C. (1992) · Journal of Accounting and Economics · doi:10.1016/0165-4101(92)90021-s · Cited by 480 · see citing works

Short-term load forecasting using lifting scheme and ARIMA models

Lee, C., & Ko, C.-N. (2010) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.11.033 · Cited by 440 · see citing works

Short-term stock market price trend prediction using a comprehensive deep learning system

Shen, J., & Shafiq, M. O. (2020) · Journal Of Big Data · doi:10.1186/s40537-020-00333-6 · Cited by 405 · see citing works

Mean–variance portfolio optimization using machine learning-based stock price prediction

Chen, W., Zhang, H., Mehlawat, M. K., & Jia, L. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106943 · Cited by 303 · see citing works

GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques

Tehrany, M. S., Shabani, F., Jebur, M. N., Hong, H., Chen, W., & Xie, X. (2017) · Geomatics Natural Hazards and Risk · doi:10.1080/19475705.2017.1362038 · Cited by 297 · see citing works

Multiobjective, Multiconstraint Service Restoration of Electric Power Distribution System With Priority Customers

Kumar, B., Das, B., & Sharma, J. P. (2008) · IEEE Transactions on Power Delivery · doi:10.1109/tpwrd.2007.905412 · Cited by 237 · see citing works

A reinforcement learning level-based particle swarm optimization algorithm for large-scale optimization

Wang, F., Wang, X., & Sun, S. (2022) · Information Sciences · doi:10.1016/j.ins.2022.04.053 · Cited by 210 · see citing works

GGA: A modified genetic algorithm with gradient-based local search for solving constrained optimization problems

D’Angelo, G., & Palmieri, F. (2020) · Information Sciences · doi:10.1016/j.ins.2020.08.040 · Cited by 197 · see citing works

Machine Learning Stock Market Prediction Studies: Review and Research Directions

Strader, T. J., Rozycki, J. J., Root, T. H., & Huang, Y.-H. J. (2020) · Journal of international technology and information management · doi:10.58729/1941-6679.1435 · Cited by 175 · see citing works

Bootstrap Aggregating and Random Forest

Lee, T., Ullah, A., & Wang, R. (2019) · Advanced studies in theoretical and applied econometrics · doi:10.1007/978-3-030-31150-6_13 · Cited by 172 · see citing works

Can investor sentiment be used to predict the stock price? Dynamic analysis based on China stock market

Guo, K., Sun, Y., & Qian, X. (2016) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2016.11.114 · Cited by 146 · see citing works

A machine learning and genetic algorithm-based method for predicting width deviation of hot-rolled strip in steel production systems

Ji, Y., Liu, S., Zhou, M., Zhao, Z., Guo, X., & Qi, L. (2021) · Information Sciences · doi:10.1016/j.ins.2021.12.063 · Cited by 145 · see citing works

Stock Price Prediction Using K-Nearest Neighbor (kNN) Algorithm

Al‐Khatib, K., Najadat, H., Hmeidi, I., & Shatnawi, M. K. A. (2013) · Cited by 132 · see citing works

Impact of COVID-19 on stock price crash risk: Evidence from Chinese energy firms

Huang, S., & Liu, H. (2021) · Energy Economics · doi:10.1016/j.eneco.2021.105431 · Cited by 128 · see citing works

Data poisoning attacks against machine learning algorithms

Yerlikaya, F. A., & Bahtıyar, Ş. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118101 · Cited by 123 · see citing works

Leveraging unstructured call log data for customer churn prediction

Vo, N. N. Y., Liu, S., Li, X., & Xu, G. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106586 · Cited by 117 · see citing works

Explainable stock prices prediction from financial news articles using sentiment analysis

Gite, S., Khatavkar, H., Kotecha, K. V., Srivastava, S., Maheshwari, P., & Pandey, N. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.340 · Cited by 113 · see citing works

Collaboration and transportation resource sharing in multiple centers vehicle routing optimization with delivery and pickup

Wang, Y., Zhang, J., Assogba, K., Liu, Y., Xu, M., & Wang, Y. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.07.024 · Cited by 103 · see citing works

Information Value and Investor Wealth: The Case of Earnings Announcements

Cready, W. M. (1988) · Journal of Accounting Research · doi:10.2307/2491111 · Cited by 101 · see citing works

Optimality of training/test size and resampling effectiveness in cross-validation

Afendras, G., & Markatou, M. (2018) · Journal of Statistical Planning and Inference · doi:10.1016/j.jspi.2018.07.005 · Cited by 86 · see citing works

SHAPforxgboost: SHAP Plots for 'XGBoost'

Liu, Y., & Just, A. C. (2019) · doi:10.32614/cran.package.shapforxgboost · Cited by 83 · see citing works

Twitter's daily happiness sentiment and the predictability of stock returns

You, W., Guo, Y., & Peng, C. (2017) · Finance research letters · doi:10.1016/j.frl.2017.07.018 · Cited by 82 · see citing works

Do the stock returns of clean energy corporations respond to oil price shocks and policy uncertainty?

Zhao, X. (2020) · Journal of Economic Structures · doi:10.1186/s40008-020-00229-x · Cited by 82 · see citing works

Oil price shocks, global economic policy uncertainty, geopolitical risk, and stock price in Malaysia: Factor augmented VAR approach

Hoque, M. E., Low, S.-W., & Zaidi, M. A. S. (2019) · Economic Research-Ekonomska Istraživanja · doi:10.1080/1331677x.2019.1675078 · Cited by 78 · see citing works

Are individual stock investors overconfident? Evidence from an emerging market

Tekçe, B., & Yılmaz, N. (2015) · Journal of Behavioral and Experimental Finance · doi:10.1016/j.jbef.2015.02.003 · Cited by 74 · see citing works

Deep Learning for Price Movement Prediction Using Convolutional Neural Network and Long Short-Term Memory

Yang, C., Zhai, J., & Tao, G. (2020) · Mathematical Problems in Engineering · doi:10.1155/2020/2746845 · Cited by 74 · see citing works

A close neighbor mobility method using particle swarm optimizer for solving multimodal optimization problems

Zou, J., Deng, Q., Zheng, J., & Yang, S. (2020) · Information Sciences · doi:10.1016/j.ins.2020.01.049 · Cited by 72 · see citing works

A new automatic machine learning based hyperparameter optimization for workpiece quality prediction

Wen, L., Ye, X., & Gao, L. (2020) · Measurement and Control · doi:10.1177/0020294020932347 · Cited by 47 · see citing works

On optimal data split for generalization estimation and model selection

Larsen, J. O., & Goutte, C. (2003) · Machine learning for signal processing ... · doi:10.1109/nnsp.1999.788141 · Cited by 45 · see citing works

Predictive intelligence using ANFIS‐induced OWAWA for complex stock market prediction

Hussain, W., Merigó, J. M., & Raza, M. R. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22732 · Cited by 45 · see citing works

Stock index prediction based on wavelet transform and FCD‐MLGRU

Li, X., & Tang, P. (2020) · Journal of Forecasting · doi:10.1002/for.2682 · Cited by 44 · see citing works

Spiral of Silence Theory

Scheufele, D. A. (2008) · SAGE Publications Ltd eBooks · doi:10.4135/9781848607910.n17 · Cited by 44 · see citing works

Forecasting Gold Price with Auto Regressive Integrated Moving Average Model

Tripathy, N. (2017) · International Journal of Economics and Financial Issues · Cited by 39 · see citing works

Prediction of B2C e-commerce order arrival using hybrid autoregressive-adaptive neuro-fuzzy inference system (AR-ANFIS) for managing fluctuation of throughput in e-fulfilment centres

Leung, E. K. H., Choy, K. L., Ho, G. T. S., Lee, C. K. M., Lam, H. Y., & Luk, C. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.05.027 · Cited by 37 · see citing works

Can happiness predict future volatility in stock markets?

Naeem, M. A., Farid, S., Balli, F., & Shahzad, S. J. H. (2020) · Research in International Business and Finance · doi:10.1016/j.ribaf.2020.101298 · Cited by 37 · see citing works

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Sadi, K. A. A., & Balachandran, W. (2023) · Applied Sciences · doi:10.3390/app13042344 · Cited by 37 · see citing works

Take Profit and Stop Loss Trading Strategies Comparison in Combination with an MACD Trading System

Vezeris, D., Kyrgos, T., & Schinas, C. J. (2018) · Journal of risk and financial management · doi:10.3390/jrfm11030056 · Cited by 35 · see citing works

Medical machine learning based on multiobjective evolutionary algorithm using learning decomposition

Wang, M., Li, X., Chen, L., & Chen, H. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119450 · Cited by 35 · see citing works

A System of Insolvency Prediction for industrial companies using a financial alternative model with neural networks

Gil, Á. C., Molina, A. M. C., Fernández, M. A., & Peláez, J. I. (2013) · International Journal of Computational Intelligence Systems · doi:10.1080/18756891.2013.754167 · Cited by 33 · see citing works

Hybrid particle swarm optimizer with fitness-distance balance and individual self-exploitation strategies for numerical optimization problems

Zheng, K., Yuan, X., Xu, Q., Dong, L., Yan, B., & Chen, K. (2022) · Information Sciences · doi:10.1016/j.ins.2022.06.059 · Cited by 33 · see citing works

An ISHAP-based interpretation-model-guided classification method for malignant pulmonary nodule

He, W., Li, B., Liao, R.-Q., Mo, H., & Tian, L. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107778 · Cited by 29 · see citing works

PREDIKSI HARGA SAHAM MENGGUNAKAN SUPPORT VECTOR REGRESSION DENGAN ALGORITMA GRID SEARCH

Yasin, H., Prahutama, A., & Utami, T. W. (2014) · MEDIA STATISTIKA · doi:10.14710/medstat.7.1.29-35 · Cited by 29 · see citing works

Bacterial foraging optimization algorithm in robotic cells with sequence-dependent setup times

Majumder, A., Laha, D., & Suganthan, P. N. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.02.016 · Cited by 29 · see citing works

Enhancing Stock Price Trend Prediction via a Time-Sensitive Data Augmentation Method

Teng, X., Wang, T., Zhang, X., Lan, L., & Luo, Z. (2020) · Complexity · doi:10.1155/2020/6737951 · Cited by 26 · see citing works

Analysis Accuracy of XGBoost Model for Multiclass Classification - A Case Study of Applicant Level Risk Prediction for Life Insurance

Mustika, W. F., Murfi, H., & Widyaningsih, Y. (2019) · International Conference on Science in Information Technology · doi:10.1109/icsitech46713.2019.8987474 · Cited by 26 · see citing works

Stock price network autoregressive model with application to stock market turbulence

Khoojine, A. S., & Han, D. (2020) · The European Physical Journal B · doi:10.1140/epjb/e2020-100419-9 · Cited by 24 · see citing works

An empirical investigation of the relationship between the real economy and stock returns for the United States

Andros Gregoriou, A., Hunter, J. E., & Wu, F. (2008) · Journal of Policy Modeling · doi:10.1016/j.jpolmod.2008.04.010 · Cited by 24 · see citing works

Investor Sentiment and Stock Price Crash Risk: Evidence from China

Fan, Y., Zhou, F., An, Y., & Yang, J. (2021) · Global Economic Review · doi:10.1080/1226508x.2021.1947340 · Cited by 24 · see citing works

High-frequency forecasting of the crude oil futures price with multiple timeframe predictions fusion

Deng, S., Zhu, Y., Duan, S., Yu, Y., Fu, Z., Liu, J., Yang, X., & Liu, Z. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119580 · Cited by 24 · see citing works

How do investors behave in the context of a market crash? Evidence from India

Mushinada, V. N. C. (2020) · International Journal of Emerging Markets · doi:10.1108/ijoem-05-2019-0357 · Cited by 22 · see citing works

TIME SERIES FORECASTING USING A MOVING AVERAGE MODEL FOR EXTRAPOLATION OF NUMBER OF TOURIST

Ivanovski, Z., Milenkovski, A., & Narasanov, Z. (2018) · RePEc: Research Papers in Economics · Cited by 20 · see citing works

High-frequency direction forecasting and simulation trading of the crude oil futures using Ichimoku KinkoHyo and Fuzzy Rough Set

Deng, S., Xiao, C., Zhu, Y., Peng, J., Li, J., & Liu, Z. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119326 · Cited by 20 · see citing works

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Alizadeh, M., Hamilton, M., Jones, P., Ma, J., & Jaradat, R. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114928 · Cited by 18 · see citing works

Data Normalization for Bilinear Structures in High-Frequency Financial Time-series

Tran, D. T., Kanniainen, J., Gabbouj, M., & Iosifidis, A. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412547 · Cited by 17 · see citing works

Prediction of Photovoltaic (PV) Output Using Artificial Neutral Network (ANN) Based on Ambient Factors

Jumaat, S. A., Crocker, F., Wahab, M. H. A., Radzi, N. H. M., & Othman, M. F. (2018) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1049/1/012088 · Cited by 16 · see citing works

2020 IEEE 7th International Conference on Engineering Technologies and Applied Sciences (ICETAS)

(2020) · doi:10.1109/icetas51660.2020 · Cited by 16 · see citing works

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Smith, R. T., & Xu, X. (2017) · Financial markets and portfolio management · doi:10.1007/s11408-016-0280-x · Cited by 15 · see citing works

EmSM: Ensemble Mixed Sampling Method for Classifying Imbalanced Intrusion Detection Data

Jung, I. O., Ji, J., & Cho, C. (2022) · Electronics · doi:10.3390/electronics11091346 · Cited by 14 · see citing works

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Chen, M., Guo, Z., Abbass, K., & Huang, W. (2022) · Frontiers in Environmental Science · doi:10.3389/fenvs.2022.1068398 · Cited by 13 · see citing works

Stock Price Prediction using Fractional Gradient-Based Long Short Term Memory

Singh, N. S. S., Sugandha, Mathur, T., Agarwal, S., & Tiwari, K. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1969/1/012038 · Cited by 11 · see citing works

Indoor lighting optimization: a comparative study between grid search optimization and particle swarm optimization

Mandal, P., Dey, D., & Roy, B. (2019) · Journal of Optics · doi:10.1007/s12596-019-00549-7 · Cited by 10 · see citing works

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Hu, Y., Shao, L., La, L., & Hua, H. (2021) · IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science (BCD) · doi:10.1109/bcd51206.2021.9581619 · Cited by 10 · see citing works

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Boyacıoğlu, M. A., Dhaoui, A., & Bourouis, S. (2013) · Journal of Academic Research in Economics · Cited by 10 · see citing works

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Ma, H., Ma, J., Wang, H., Li, P., & Du, W. (2021) · IEEE/ACIS International Conference on Computer and Information Science (ICIS) · doi:10.1109/icisfall51598.2021.9627470 · Cited by 9 · see citing works

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Deng, S., Zhu, Y., Huang, X., Duan, S., & Fu, Z. (2022) · Future Internet · doi:10.3390/fi14060180 · Cited by 8 · see citing works

Multi-objective Parameters Selection for SVM Classification Using NSGA-II

Xu, L. W., & Li, C. (2006) · Lecture notes in computer science · doi:10.1007/11790853_29 · Cited by 8 · see citing works

Machine learning applied to the prediction of root architecture of soybean cultivars under two water availability conditions

Duarte, A. B., Ferreira, D. D. O., Ferreria, L. B., & da Silva, F. L. (2022) · Semina Ciências Agrárias · doi:10.5433/1679-0359.2022v43n3p1017 · Cited by 8 · see citing works

2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE)

(2021) · doi:10.1109/csaiee54046.2021 · Cited by 7 · see citing works

THE IMPACT OF INVESTOR PSYCHOLOGY ON STOCK MARKETS: EVIDENCE FROM FRANCE

Dhaoui, A., Bourouis, S., & BOYACIOGLU, M. A. (2013) · RePEc: Research Papers in Economics · Cited by 6 · see citing works

MODEL HIBRID GENETIC-XGBOOST DAN PRINCIPAL COMPONENT ANALYSIS PADA SEGMENTASI DAN PERAMALAN PASAR

Indonesia, U. M., Siringoringo, R., Perangin-angin, R., & Jamaluddin, J. (2021) · METHOMIKA Jurnal Manajemen Informatika dan Komputerisasi Akuntansi · doi:10.46880/jmika.vol5no2.pp97-103 · Cited by 5 · see citing works

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Tsai, C.-Y., Cheong, H.-I., Houghton, R., Majumdar, A., Liu, W., Lee, K., Wu, C.-J., & Liu, Y.-S. (2020) · IEEE International Conference on Engineering Technologies and Applied Sciences (ICETAS) · doi:10.1109/icetas51660.2020.9484319 · Cited by 4 · see citing works

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Inam, S. G. (2021) · doi:10.47750/cibg.2021.27.06.036 · Cited by 4 · see citing works

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Gunasekara, A. L., & Jayasinghe, J. A. D. K. (2020) · Kelaniya Journal of Management · doi:10.4038/kjm.v8i2.7604 · Cited by 4 · see citing works

Multi objective non-dominated sorting genetic algorithm (NSGA-II) for optimizing fuzzy rule base system

Omar, M. T., Gope, M., Khandaker, A. I., & Shill, P. C. (2015) · International Conference on Electrical Information and Communication Technologies · doi:10.1109/eict.2015.7391927 · Cited by 3 · see citing works

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Metsileng, L. D., Moroke, N. D., & Tsoku, J. T. (2018) · Journal of Economics and Behavioral Studies · doi:10.22610/jebs.v10i5(j).2511 · Cited by 3 · see citing works

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arman, S., Hairun, Y., Alhaddad, I., Machmud, T., Suharna, H., & Rusiman, M. S. (2021) · Webology · doi:10.14704/web/v18si04/web18180 · Cited by 3 · see citing works

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Zha, W. (2018) · DEStech Transactions on Economics Business and Management · doi:10.12783/dtem/icssed2018/20301 · Cited by 2 · see citing works

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Huang, Y., & Xiugang, Y. (2018) · DOAJ (DOAJ: Directory of Open Access Journals) · doi:10.18533/ijbsr.v8i7.1121 · Cited by 2 · see citing works

Common Mistakes of InvestorsPreprint

Yuen, R. W. P. (2012) · SSRN Electronic Journal · doi:10.2139/ssrn.2047622 · Cited by 2 · see citing works

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Peng, L. (2022) · BCP Business & Management · doi:10.54691/bcpbm.v34i.3111 · Cited by 2 · see citing works

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Senoguchi, J. (2022) · Journal of Computer Science and Technology Studies · doi:10.32996/jcsts.2022.4.2.11 · Cited by 1 · see citing works

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Li, J., & Zhao, C. (2021) · International Conference on Computer Engineering and Application (ICCEA) · doi:10.1109/iccea53728.2021.00042 · Cited by 1 · see citing works

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Lin, H., Yi-fu, H., & Shi, M. (2022) · International Conference on Automation Control, Algorithm and Intelligent Bionics (ACAIB) · doi:10.1117/12.2639549 · Cited by 1 · see citing works

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Saha, J., Patel, S., Xing, F., & Cambria, E. (2022) · Journal of the Association for Information Systems · Cited by 1 · see citing works

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Deng, S., Su, J., Zhu, Y., Yu, Y., & Xiao, C. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122502 · Cited by 92 · see citing works

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Arsenault, P.-D., Wang, S., & Patenaude, J.-M. (2025) · ACM Computing Surveys · doi:10.1145/3729531 · Cited by 82 · see citing works

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Xiao, J., Jiang, J., & Zhang, Y. (2024) · Pacific-Basin Finance Journal · doi:10.1016/j.pacfin.2024.102303 · Cited by 46 · see citing works

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Ahaggach, H., Lylia Abrouk, A., & Lebon, E. (2024) · Forecasting · doi:10.3390/forecast6030028 · Cited by 33 · see citing works

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Ruiz-Rodríguez, M. L., Sylvain Kubler, A., Robert, J., & Traon, Y. L. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123404 · Cited by 34 · see citing works

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Essahraui, S., Lamaakal, I., Maleh, Y., Makkaoui, K. E., Bouami, M. F., Ouahbi, I., El‐Latif, A. A. A., Almousa, M., & Rodrigues, J. J. P. C. (2025) · IEEE Access · doi:10.1109/access.2025.3589938 · Cited by 35 · see citing works

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Zhen, K., Xie, D., & Hu, X. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127381 · Cited by 25 · see citing works

PCA-ICA-LSTM: A Hybrid Deep Learning Model Based on Dimension Reduction Methods to Predict S&P 500 Index Price

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Evolutionary Computation for Sparse Multi-Objective Optimization: A Survey

Shao, S., Tian, Y., Zhang, Y., Yang, S., Zhang, P., He, C., Zhang, X., & Jin, Y. (2025) · ACM Computing Surveys · doi:10.1145/3734865 · Cited by 23 · see citing works

Financial risk forewarning with an interpretable ensemble learning approach: An empirical analysis based on Chinese listed companies

Deng, S., Luo, Q., Zhu, Y., Ning, H., & Shimada, T. (2024) · Pacific-Basin Finance Journal · doi:10.1016/j.pacfin.2024.102393 · Cited by 21 · see citing works

Incorporating Multi-Source Market Sentiment and Price Data for Stock Price Prediction

Fu, K., & Zhang, Y. (2024) · Mathematics · doi:10.3390/math12101572 · Cited by 21 · see citing works

Forecasting stock market time series through the integration of bee colony optimizer and multivariate empirical mode decomposition with extreme gradient boosting regression

Liu, X., Wu, Z., & Xin, J. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110353 · Cited by 20 · see citing works

Enhancing skin lesion classification: a CNN approach with human baseline comparison

Ajabani, D., Shaikh, Z. A., Yousef, A., Ali, K., & Albahar, M. A. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.2795 · Cited by 18 · see citing works

An intelligent framework based on optimized variational mode decomposition and temporal convolutional network: Applications to stock index multi-step forecasting

Yu, Y., Dai, D., Yang, Q., Zeng, Q., Lin, Y. Q., & Chen, Y. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.126222 · Cited by 15 · see citing works

Combining market-guided patterns and mamba for stock price prediction

Chang, Y. F., Lu, W., Xue, F., & Lu, X. (2024) · Alexandria Engineering Journal · doi:10.1016/j.aej.2024.10.117 · Cited by 16 · see citing works

On Assessing the Performance of LLMs for Target-Level Sentiment Analysis in Financial News Headlines

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Unlocking ETF price forecasting: Exploring the interconnections with statistical dependence-based graphs and xAI techniques

Choi, I., & Kim, W. C. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112567 · Cited by 14 · see citing works

Multi-sentiment fusion for stock price crash risk prediction using an interpretable ensemble learning method

Deng, S., Luo, Q., Zhu, Y., Ning, H., Yu, Y., Gao, Y., Shen, Q., & Shimada, T. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108842 · Cited by 13 · see citing works

Ensemble time series models for stock price prediction and portfolio optimization with sentiment analysis

Narayana, M. L., Kartha, A. J., Mandal, A. K., Roshini, P., Suresh, A., & Jose, A. C. (2025) · Journal of Intelligent Information Systems · doi:10.1007/s10844-025-00928-6 · Cited by 12 · see citing works

Economic insight through an optimized deep learning model for stock market prediction regarding Dow Jones industrial Average index

Yu, H., Dong, J., & Li, B. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128473 · Cited by 12 · see citing works

Decision Trees for Strategic Choice of Augmenting Management Intuition with Machine Learning

Luo, G., Arshad, M. A., & Luo, G. (2025) · Symmetry · doi:10.3390/sym17070976 · Cited by 21 · see citing works

Multi-scale contrast approach for stock index prediction with adaptive stock fusion

Gao, J., Wang, S., He, C., & Qin, C. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125590 · Cited by 10 · see citing works

Can real-time investor sentiment help predict the high-frequency stock returns? Evidence from a mixed-frequency-rolling decomposition forecasting method

Cai, Y., Tang, Z., & Chen, Y. (2024) · The North American Journal of Economics and Finance · doi:10.1016/j.najef.2024.102147 · Cited by 9 · see citing works

Multi-feature fusion stock prediction based on knowledge graph

Liu, Z., Qian, Y., Lv, W., Fang, Y., & Liu, S. (2024) · The Electronic Library · doi:10.1108/el-02-2023-0053 · Cited by 7 · see citing works

Implicit-Causality-Exploration-Enabled Graph Neural Network for Stock Prediction

Li, Y., Xue, X., Liu, Z., Duan, P., & Zhang, B. (2024) · Information · doi:10.3390/info15120743 · Cited by 8 · see citing works

EELMCDA: Combining evolutionary ensemble learning with matrix feature decomposition for predicting circRNA-disease associations

Wang, Z., Wang, L., You, Z., Wang, L., Li, Y., & Wang, Z. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822623 · Cited by 6 · see citing works

Explainable gradient boosting for corporate crisis forecasting in Italian businesses

Maturo, F., Riccio, D., Mazzitelli, A., Bifulco, G. M., & Paolone, F. (2025) · Annals of Operations Research · doi:10.1007/s10479-025-06570-3 · Cited by 6 · see citing works

Opinion mining for stock trend prediction using deep learning

Albahli, S., & Nazir, T. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19793-6 · Cited by 6 · see citing works

A decade systematic review of fusion techniques in financial market prediction

Mehrmolaei, S., & Abadeh, M. S. (2025) · Computer Science Review · doi:10.1016/j.cosrev.2025.100813 · Cited by 5 · see citing works

Leveraging cosine similarity for stock trend prediction with options chain data and piecewise linear encoding

Trivedi, A., & Sangeetha, S. (2025) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2025.110565 · Cited by 4 · see citing works

High-precision forecasting of Indian stock market indices using weighted ensemble of hyperparameter-tuned LightGBM models with diverse technical indicators

Samantaray, S., Mohapatra, S., & Mishra, M. (2025) · Systems Science & Control Engineering · doi:10.1080/21642583.2025.2567887 · Cited by 3 · see citing works

Predicting and explaining life satisfaction among older adults using tree-based ensemble models and SHAP: Evidence from the digital divide survey

Kong, H., Fang, H., & Zhang, G. (2025) · PLoS ONE · doi:10.1371/journal.pone.0337938 · Cited by 3 · see citing works

Confidence-scaled margin adaptation boosting for interpretable financial distress prediction

Liu, W., Lan, X., Xia, M., Zou, Y., Pang, C., & Song, G. (2026) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2026.01.003 · Cited by 3 · see citing works

Novel way to predict stock movements using multiple models and comprehensive analysis: leveraging voting meta-ensemble techniques

Kayit, A. D., & Tahir, M. I. (2024) · Journal of the Nigerian Society of Physical Sciences · doi:10.46481/jnsps.2024.2039 · Cited by 3 · see citing works

A Hybrid Deep Learning Model for Coal Index Forecasting Based on Sentiment Analysis and Decomposition–Reconstruction Methods

Xiao, Y., Zhang, X., He, C., & Hu, Y. (2025) · Journal of Forecasting · doi:10.1002/for.70010 · Cited by 3 · see citing works

Exploiting the potential of a directional changes-based trading algorithm in the stock market

Ao, H., & Li, M. (2023) · Finance research letters · doi:10.1016/j.frl.2023.104936 · Cited by 3 · see citing works

Integration of Explainable AI with Deep Learning for Real-Time Sentiment-Driven Stock Price Prediction

Bethi, S., Krishna, G. B., Murthy, G. V., Kumar, D. R., Sridevi, M., & Rao, M. (2025) · Computational Economics · doi:10.1007/s10614-025-11159-w · Cited by 2 · see citing works

An enhanced risk-aware multi-task learning framework with dual-source sentiment and multi-scale decomposition for robust stock ranking forecasting

Shen, F., Kuang, J., Hu, Y.-C., & Chen, S.-T. (2026) · Expert systems with applications · doi:10.1016/j.eswa.2026.132462 · Cited by 2 · see citing works

A Stacking Ensemble Deep Learning Model for Stock Price Forecasting

Hao, J., & Zhang, C. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5663-6_13 · Cited by 2 · see citing works

A Novel Model for Predicting Stock Index Trends through Hybrid Observed Mode Decomposition-Based Optimized Dynamic Sequential Extreme Learning Machine

Sumathi, R., & Ashokkumar, S. (2024) · International Journal of Electronics and Communication Engineering · doi:10.14445/23488549/ijece-v11i6p108 · Cited by 1 · see citing works

Sentiment News in the Nigerian Reit Market: Source and its Dynamic Nature

Fateye, T. B., Ajayi, C. A., & Peiser, R. B. (2024) · Journal of African Real Estate Research · doi:10.15641/jarer.v9i1.1510 · Cited by 1 · see citing works

A Novel Decomposition‐Ensemble Approach for Forecasting Stock Price With Quantum Neural Network and Big Data

Liu, S., & Xie, G. (2025) · Journal of Forecasting · doi:10.1002/for.70047 · Cited by 1 · see citing works

Interconnected freight markets: An ensemble learning and GAM approach to regional and international dry bulk shipping rates

Fışkın, C. S., Cakir, E., Fışkın, R., Akgül, E. F., Nasibov, E. N., & Akkaya, T. (2025) · Ocean & Coastal Management · doi:10.1016/j.ocecoaman.2025.108070 · Cited by 1 · see citing works

Unveiling the Dynamics of Stock Market Crashes: From Price Bubbles to Machine Learning Predictions

Basak, S., Paul, S., & Banerjee, S. (2024) · Studies in autonomic, data-driven and industrial computing · doi:10.1007/978-981-97-6806-6_14

Stock price prediction based on ARIMA-BP neural network and sentiment analysis

Liu, C., Lin, X., & Zhao, H. (2024) · International Conference on Economic Data Analytics and Artificial Intelligence (EDAAI) · doi:10.1145/3717664.3717676

A Qualitative Study of the Influence of Artificial Intelligence on Individual Investors in Asian Stock Markets

Pullagura, R., & Arumugam, S. (2025) · Springer proceedings in business and economics · doi:10.1007/978-981-96-6526-6_8

Hvb-Persian: Hybrid Vader-Parsbert for Persian Sentiment Classification of Product ReviewsPreprint

Alimardani, Z., Jaberi, S., & Amini, A. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5371417

Beyond static representations: A dynamic structural transformer for adaptive stock ranking

Zhang, S., Nie, S., Zhang, Y., Lü, T., Chai, X., & Wang, J. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132806

Benchmarking large language models for target-based financial sentiment and stock return

Muhammad, I., Rospocher, M., Knez, T., & Žitnik, S. (2026) · International Journal of Data Science and Analysis · doi:10.1007/s41060-026-01201-x

A Robust Framework for Stock Price Prediction via BorutaSHAP-Based Feature Selection and Ensemble Deep Learning

Dosdoğru, A. T. (2026) · IEEE Access · doi:10.1109/access.2026.3719338

CIMTS: candlestick-integrated multi-modal trading system for stock indices

Jang, J., Kim, J., & Enke, D. L. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.4069

Optimization of technical analysis-driven algorithmic trading using deep reinforcement learning

Wang, Y., Wang, X., & Liu, X. (2026) · Applied Intelligence · doi:10.1007/s10489-026-07478-6

Dynamic forecasting of the Shanghai Stock Exchange index movement using multiple types of investor sentiment

Deng, S., Xiao, C., Zhu, Y., Tian, Y. H., Liu, Z., & Yang, T. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109132 · Cited by 25 · see citing works

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

Deng, S., Huang, X., Zhu, Y., Su, Z., Fu, Z., & Shimada, T. (2022) · The North American Journal of Economics and Finance · doi:10.1016/j.najef.2022.101848 · Cited by 41 · see citing works

A multi-feature selection fused with investor sentiment for stock price prediction

Zhen, K., Xie, D., & Hu, X. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127381 · Cited by 25 · see citing works

Machine Learning and Artificial Intelligence Applied in the Research of the Emotions Impact on Forecasting

Jiang, Y., Zuo, Y., & Yang, Y. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1982/1/012052 · Cited by 2 · see citing works

Predictive Analysis of Stock Price by Machine Learning and Online Investors’ Sentiment

(2022) · Journal of Global Economy Business and Finance · doi:10.53469/jgebf.2022.04(04).12