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• W4308458519 endingPage "119186" @default.
• W4308458519 startingPage "119186" @default.
• W4308458519 abstract "Stock market prediction is considered as an important yet challenging aspect of financial analysis. The difficulty of forecasting arises from volatile and non-linear nature of stock market, which is affected by varied uncertain factors, ranging from financial ratios to macroeconomic indicators. Recent advances in machine learning, particularly ensembles, have made it possible for academic researchers and financial practitioners to forecast the stock market more efficiently. The novelty of this work is to evaluate how stock return in an oil-dependent country (i.e., Iran), which has been facing stagflation for a long time due to economic and political issues, is affected by fundamental and macroeconomic indicators. Our main objectives are to (1) find the most important fundamental and macroeconomic indicators that control the stock returns of companies listed on the Tehran Stock Exchange (TSE); (2) compare the performance of newly developed bagging- and boosting-based ensembles in predicting annual real stock returns of the TSE; and (3) develop multiclass classification models to forecast stock returns. Prior studies mainly focused on developing binary classification models, which simply predict whether stock returns will be positive or negative in the future. We, however, design multiclass classification models to provide more information for the investors and reduce the uncertainties associated with the prediction. To this end, we first provide a comprehensive list of 57 potential features affecting the stock returns. Next, the data are carefully preprocessed and fed to 14 different bagging- and boosting-based ensembles (e.g., Random Forest, LightGBM, XGBoost, Extra-Trees, AdaBoost, CatBoost) to predict the stock returns. The performance of ensembles is evaluated through different measures (e.g., accuracy, F-score, G-mean). We then propose a novel feature selection method to identify the most contributing features to the stock returns. Our proposed model identifies nearly 65% of 57 original features as redundancy, resulting in 20 most significant features. The selected features are fed to the mentioned ensembles to re-predict the stock returns. Finally, the performance of stock returns forecasts with and without selected features is compared. To design the ensembles, we employ the data from listed companies on the TSE for a 15-year period, spanning between 2005 and 2020. Results suggest that boosting ensembles, in general, outperform bagging-based methods. Among the boosting ensembles, XGBoost and AdaBoost provide the best and worst predictive performance, respectively. Among the bagging-like ensembles, Rotation Forest is the most accurate one, whereas Random Patches performs the worst. Further, our proposed feature selection approach effectively identifies the most representative features for stock returns prediction and can be used as a reliable framework for future investment decisions." @default.
• W4308458519 created "2022-11-12" @default.
• W4308458519 creator A5033159121 @default.
• W4308458519 creator A5046490855 @default.
• W4308458519 date "2023-03-01" @default.
• W4308458519 modified "2023-10-10" @default.
• W4308458519 title "Evaluating the performance of ensemble classifiers in stock returns prediction using effective features" @default.
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• W4308458519 cites W1555686918 @default.
• W4308458519 cites W1559666148 @default.
• W4308458519 cites W1574447377 @default.
• W4308458519 cites W1581797854 @default.
• W4308458519 cites W1678356000 @default.
• W4308458519 cites W1766594731 @default.
• W4308458519 cites W1965805826 @default.
• W4308458519 cites W1968969471 @default.
• W4308458519 cites W1970872742 @default.
• W4308458519 cites W1980831577 @default.
• W4308458519 cites W1981971464 @default.
• W4308458519 cites W1985066035 @default.
• W4308458519 cites W1986145156 @default.
• W4308458519 cites W1992018127 @default.
• W4308458519 cites W2001399757 @default.
• W4308458519 cites W2004463884 @default.
• W4308458519 cites W2011368107 @default.
• W4308458519 cites W2012079387 @default.
• W4308458519 cites W2017537474 @default.
• W4308458519 cites W2022122945 @default.
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• W4308458519 cites W2024046085 @default.
• W4308458519 cites W2024935505 @default.
• W4308458519 cites W2046792933 @default.
• W4308458519 cites W2056132907 @default.
• W4308458519 cites W2066456070 @default.
• W4308458519 cites W2066795664 @default.
• W4308458519 cites W2070338492 @default.
• W4308458519 cites W2071698610 @default.
• W4308458519 cites W2074250525 @default.
• W4308458519 cites W2077365830 @default.
• W4308458519 cites W2084216799 @default.
• W4308458519 cites W2091007025 @default.
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• W4308458519 cites W2104679989 @default.
• W4308458519 cites W2113640060 @default.
• W4308458519 cites W2118978333 @default.
• W4308458519 cites W2137130182 @default.
• W4308458519 cites W2146081744 @default.
• W4308458519 cites W2150757437 @default.
• W4308458519 cites W2155632266 @default.
• W4308458519 cites W2165250079 @default.
• W4308458519 cites W2167101736 @default.
• W4308458519 cites W2170505850 @default.
• W4308458519 cites W2170654002 @default.
• W4308458519 cites W2305435702 @default.
• W4308458519 cites W2326324325 @default.
• W4308458519 cites W2512272949 @default.
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• W4308458519 cites W2950213400 @default.
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• W4308458519 doi "https://doi.org/10.1016/j.eswa.2022.119186" @default.
• W4308458519 hasPublicationYear "2023" @default.
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