Neural Networks in Finance and Investing: Using Artificial Intelligence to Improve Real World Performance
IEEE Transactions on Neural Networks
Artificial neural networks with evolutionary instance selection for financial forecasting
Expert Systems with Applications: An International Journal
KOSPI time series analysis using neural network with weighted fuzzy membership functions
KES-AMSTA'08 Proceedings of the 2nd KES International conference on Agent and multi-agent systems: technologies and applications
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Self-adaptive neuro-fuzzy inference systems for classification applications
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
Fuzzy min-max neural networks. I. Classification
IEEE Transactions on Neural Networks
Parkinson's disease classification using gait characteristics and wavelet-based feature extraction
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
This paper presents a methodology to forecast the direction of change in the daily Korea composite stock price index (KOSPI) using input features that are derived from KOSPI and KRW/USD exchange rates by technical indicators and the non-overlap area distribution measurement method based on the neural network with weighted fuzzy membership functions (NEWFM). To extract input features used in NEWFM, three numbers of technical indicators, including RSI (Relative Strength Index), CCI (Commodity Channel Index), and CPP (Current Price Position), are selected in the first step. In the second step, thirteen numbers of input features and an input feature derived by three numbers of technical indicators (RSI, CCI, and CPP) and a technical indicator (CPP) are extracted from the daily KOSPI and KRW/USD exchange rates, respectively. In the final step, the minimized numbers of input features are selected by the non-overlap area distribution measurement method from fourteen numbers of input features to forecast the next day's direction of change in the daily KOSPI. The best performance result of NEWFM is 59.21% using thirteen numbers of input features selected by the non-overlap area distribution measurement method.