A Fusion Framework for Forecasting Financial Market Direction Using Enhanced Ensemble Models and Technical Indicators

Dushmanta Kumar Padhi, Neelamadhab Padhy, Akash Kumar Bhoi, Jana Shafi and Muhammad Fazal Ijaz
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Dushmanta Kumar Padhi: Department of Computer Science and Engineering, School of Engineering and Technology, GIET University, Gunupur 765022, Odisha, India
Neelamadhab Padhy: Department of Computer Science and Engineering, School of Engineering and Technology, GIET University, Gunupur 765022, Odisha, India
Akash Kumar Bhoi: Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar 737136, Sikkim, India
Jana Shafi: Department of Computer Science, College of Arts and Science, Prince Sattam bin Abdul Aziz University, Wadi Ad-Dwasir 11991, Saudi Arabia
Muhammad Fazal Ijaz: Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Korea

Mathematics, 2021, vol. 9, issue 21, 1-31

Abstract: People continuously hunt for a precise and productive strategy to control the stock exchange because the monetary trade is recognised for its unbelievably different character and unpredictability. Even a minor gain in predicting performance will be extremely profitable and significant. Our novel study implemented six boosting techniques, i.e., XGBoost, AdaBoost, Gradient Boosting, LightGBM, CatBoost, and Histogram-based Gradient Boosting, and these boosting techniques were hybridised using a stacking framework to find out the direction of the stock market. Five different stock datasets were selected from four different countries and were used for our experiment. We used two-way overfitting protection during our model building process, i.e., dynamic reduction technique and cross-validation technique. For model evaluation purposes, we used the performance metrics, i.e., accuracy, ROC curve (AUC), F-score, precision, and recall. The aim of our study was to propose and select a predictive model whose training and testing accuracy difference was minimal in all stocks. The findings revealed that the meta-classifier Meta-LightGBM had training and testing accuracy differences that were very low among all stocks. As a result, a proper model selection might allow investors the freedom to invest in a certain stock in order to successfully control risk and create short-term, sustainable profits.

Keywords: stock exchange; stock market; ensemble; cross-validation; LDA; hist gradient boosting; securities exchange; CatBoost (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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