Stock Price Prediction with Combination of XGBoost and Binary Gray Wolf Optimizer
Subject Areas : Financial EconomicsHadi Esmaeili 1 , Shahab جهانگیری 2 , Ali Rezazadeh 3
1 - Department of Economics- faculty of economics and management-urmia university
2 - استادیار، گروه اقتصاد، دانشگاه ارومیه
3 - Department of Economics, Urmia University, Urmia, Iran
Keywords: Stock Price Prediction, XGBoost, Binary Gray Wolf Optimizer,
Abstract :
Aim: Quantitative investing powered by machine learning has opened up new opportunities to generate more insights from financial data, leading to the development of ideas to enhance the performance of investments and portfolio management in stock markets. Method: In this research, the proposed model called CBGWO-XGBoost is proposed to predict the closing price of the future stock of Tehran Stock Exchange companies in the form of two sets of developed features. Findings: In general, the developed feature set 2 performed better than the developed feature set 1 without the feature engineering process. Also, by applying the feature engineering process in the developed feature set 2, the algorithms CBGWO, BGWO2 and BGWO1 obtained the best performance, respectively. Conclusion: This study showed that the successful performance of stock price forecasting does not depend on an exclusive forecasting method. Instead, it is shown that a good prediction performance depends largely on feature engineering processes. Aim: Quantitative investing powered by machine learning has opened up new opportunities to generate more insights from financial data, leading to the development of ideas to enhance the performance of investments and portfolio management in stock markets. Method: In this research, the proposed model called CBGWO-XGBoost is proposed to predict the closing price of the future stock of Tehran Stock Exchange companies in the form of two sets of developed features. Conclusion: This study showed that the successful performance of stock price forecasting does not depend on an exclusive forecasting method. Instead, it is shown that a good prediction performance depends largely on feature
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