| The use of data mining and neural networks for forecasting stock market returns D Enke, S Thawornwong Expert Systems with applications 29 (4), 927-940, 2005 | 740 | 2005 |
| Forecasting daily stock market return using dimensionality reduction X Zhong, D Enke Expert systems with applications 67, 126-139, 2017 | 537 | 2017 |
| Predicting the daily return direction of the stock market using hybrid machine learning algorithms X Zhong, D Enke Financial innovation 5 (1), 1-20, 2019 | 300 | 2019 |
| The adaptive selection of financial and economic variables for use with artificial neural networks S Thawornwong, D Enke Neurocomputing 56, 205-232, 2004 | 269 | 2004 |
| An adaptive stock index trading decision support system WC Chiang, D Enke, T Wu, R Wang Expert Systems with Applications 59, 195-207, 2016 | 191 | 2016 |
| Intelligent technical analysis based equivolume charting for stock trading using neural networks T Chavarnakul, D Enke Expert Systems with Applications 34 (2), 1004-1017, 2008 | 181 | 2008 |
| Time series classification using deep learning for process planning: A case from the process industry N Mehdiyev, J Lahann, A Emrich, D Enke, P Fettke, P Loos Procedia Computer Science 114, 242-249, 2017 | 152 | 2017 |
| Stock market prediction with multiple regression, fuzzy type-2 clustering and neural networks D Enke, M Grauer, N Mehdiyev Procedia Computer Science 6, 201-206, 2011 | 125 | 2011 |
| Evaluating forecasting methods by considering different accuracy measures N Mehdiyev, D Enke, P Fettke, P Loos Procedia Computer Science 95, 264-271, 2016 | 114 | 2016 |
| A comprehensive cluster and classification mining procedure for daily stock market return forecasting X Zhong, D Enke Neurocomputing 267, 152-168, 2017 | 113 | 2017 |
| Volatility forecasting using a hybrid GJR-GARCH neural network model SA Monfared, D Enke Procedia Computer Science 36, 246-253, 2014 | 113 | 2014 |
| Determination of rule patterns in complex event processing using machine learning techniques N Mehdiyev, J Krumeich, D Enke, D Werth, P Loos Procedia Computer Science 61, 395-401, 2015 | 96 | 2015 |
| A hybrid stock trading system for intelligent technical analysis-based equivolume charting T Chavarnakul, D Enke Neurocomputing 72 (16-18), 3517-3528, 2009 | 95 | 2009 |
| Stock market prediction using a combination of stepwise regression analysis, differential evolution-based fuzzy clustering, and a fuzzy inference neural network D Enke, N Mehdiyev Intelligent Automation & Soft Computing 19 (4), 636-648, 2013 | 75 | 2013 |
| A machine learning trading system for the stock market based on N-period Min-Max labeling using XGBoost Y Han, J Kim, D Enke Expert Systems with Applications 211, 118581, 2023 | 74 | 2023 |
| An intelligent hybrid trading system for discovering trading rules for the futures market using rough sets and genetic algorithms Y Kim, W Ahn, KJ Oh, D Enke Applied Soft Computing 55, 127-140, 2017 | 73 | 2017 |
| Developing a rule change trading system for the futures market using rough set analysis Y Kim, D Enke Expert Systems with Applications 59, 165-173, 2016 | 68 | 2016 |
| A hybrid neuro-fuzzy model to forecast inflation D Enke, N Mehdiyev Procedia Computer Science 36, 254-260, 2014 | 57 | 2014 |
| Neural networks as a decision maker for stock trading: a technical analysis approach S Thawornwong, D Enke, C Dagli International Journal of Smart Engineering System Design 5 (4), 313-325, 2003 | 56 | 2003 |
| An expert advisory system for the ISO 9001 quality system HT Liao, D Enke, H Wiebe Expert Systems with Applications 27 (2), 313-322, 2004 | 55 | 2004 |
Cihan H. DagliProfessor of Engineering Mangement and Systems EngineeringVahvistettu sähköpostiosoite verkkotunnuksessa umsystem.edu