Neural network implementation of fuzzy logic
Fuzzy Sets and Systems
Fuzzy logic, neural networks, and soft computing
Communications of the ACM
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
POPFNN: a pseudo outer-product based fuzzy neural network
Neural Networks
Predicting equity returns from securities data
Advances in knowledge discovery and data mining
Fuzzy adaptive output tracking control of nonlinear systems
Fuzzy Sets and Systems
Fuzziness: from epistemic considerations to terminological clarification
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Foundations of Neuro-Fuzzy Systems
Foundations of Neuro-Fuzzy Systems
Computers and Operations Research - Special issue: Emerging economics
Artificial Intelligence in Medicine
Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis
IEEE Transactions on Computers
Fast learning in networks of locally-tuned processing units
Neural Computation
Ovarian cancer diagnosis with complementary learning fuzzy neural network
Artificial Intelligence in Medicine
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Fuzzy associative conjuncted maps network
IEEE Transactions on Neural Networks
A novel brain-inspired neural cognitive approach to SARS thermal image analysis
Expert Systems with Applications: An International Journal
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
R-POPTVR: a novel reinforcement-based POPTVR fuzzy neural network for pattern classification
IEEE Transactions on Neural Networks
eFSM: a novel online neural-fuzzy semantic memory model
IEEE Transactions on Neural Networks
A novel application of a neuro-fuzzy computational technique in event-based rainfall-runoff modeling
Expert Systems with Applications: An International Journal
Evolving fuzzy neural networks for supervised/unsupervised onlineknowledge-based learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Noisy speech processing by recurrently adaptive fuzzy filters
IEEE Transactions on Fuzzy Systems
Design for Self-Organizing Fuzzy Neural Networks Based on Genetic Algorithms
IEEE Transactions on Fuzzy Systems
Neural networks in financial engineering: a study in methodology
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Learning to trade via direct reinforcement
IEEE Transactions on Neural Networks
GenSoFNN: a generic self-organizing fuzzy neural network
IEEE Transactions on Neural Networks
Stock Trading Using RSPOP: A Novel Rough Set-Based Neuro-Fuzzy Approach
IEEE Transactions on Neural Networks
Evidence theory based knowledge representation
Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Services
Agent-based computational investing recommender system
Proceedings of the 7th ACM conference on Recommender systems
Hi-index | 12.05 |
Financial volatility refers to the intensity of the fluctuations in the expected return on an investment or the pricing of a financial asset due to market uncertainties. Hence, volatility modeling and forecasting is imperative to financial market investors, as such projections allow the investors to adjust their trading strategies in anticipation of the impending financial market movements. Following this, financial volatility trading is the capitalization of the uncertainties of the financial markets to realize investment profits in times of rising, falling and side-way market conditions. In this paper, an intelligent straddle trading system (framework) that consists of a volatility projection module (VPM) and a trade decision module (TDM) is proposed for financial volatility trading via the buying and selling of option straddles to help a human trader capitalizes on the underlying uncertainties of the Hong Kong stock market. Three different measures, namely: (1) the historical volatility (HV), (2) implied volatility (IV) and (3) model-based volatility (MV) of the Hang Seng Index (HSI) are employed to quantify the implicit volatility of the Hong Kong stock market. The TDM of the proposed straddle trading system combines the respective volatility measures with the well-established moving-averages convergence/divergence (MACD) principle to recommend trading actions to a human trader dealing in HSI straddles. However, the inherent limitation of the MACD trading rule is that it generates time-delayed trading signals due to the use of moving averages, which are essentially lagging trend indicators. This drawback is intuitively addressed in the proposed straddle trading system by applying the VPM to compute future projections of the volatility measures of the HSI prior to the activation of the TDM. The VPM is realized by a self-organising neural-fuzzy semantic network named the evolving fuzzy semantic memory (eFSM) model. As compared to existing statistical and computational intelligence based modeling techniques currently employed for financial volatility modeling and forecasting, eFSM possesses several desirable attributes such as: (1) an evolvable knowledge base to continuously address the non-stationary characteristics of the Hong Kong stock market; (2) highly formalized human-like information computations; and (3) a transparent structure that can be interpreted via a set of linguistic IF-THEN semantic fuzzy rules. These qualities provide added credence to the computed HSI volatility projections. The volatility modeling and forecasting performances of the eFSM, when benchmarked to several established modeling techniques, as well as the observed trading returns of the proposed straddle trading system, are encouraging.