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Wing Lon Ng
Researcher at University of Essex
Publications - 32
Citations - 330
Wing Lon Ng is an academic researcher from University of Essex. The author has contributed to research in topics: Fuzzy logic & Order (exchange). The author has an hindex of 10, co-authored 32 publications receiving 303 citations.
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Analysing financial contagion and asymmetric market dependence with volatility indices via copulas
Yue Peng,Wing Lon Ng +1 more
TL;DR: In this article, the authors explore the cross-market dependence between five popular equity indices (S&P 500, NASDAQ 100, DAX 30, FTSE 100, and Nikkei 225) and their corresponding volatility indices (VIX, VXN, VDAX, VFTSE, and VXJ).
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Precise time-matching in chimpanzee allogrooming does not occur after a short delay
TL;DR: It is suggested that some apparent patterns of time-matched reciprocity may arise merely due to the law of large numbers, and a statistical test is introduced which takes this into account when aggregating grooming durations over a window of time.
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Improving risk-adjusted performance in high frequency trading using interval type-2 fuzzy logic
Vince Vella,Wing Lon Ng +1 more
TL;DR: An innovative approach to design an interval type-2 model which is based on a generalisation of the popular type-1 ANFIS model to improve the risk-adjusted performance with minimal increase in the design and computational complexity is proposed.
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Enhancing risk-adjusted performance of stock market intraday trading with Neuro-Fuzzy systems
Vince Vella,Wing Lon Ng +1 more
TL;DR: A dynamic extension of the popular moving average rule is proposed and enhanced with a model validation methodology using heat maps to analyse favourable profitability in specific holding time and signal regions and it is shown that accounting for transaction costs and the use of risk-return objective functions provide better results in out-of-sample tests.
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Intraday high-frequency FX trading with adaptive neuro-fuzzy inference systems
Abdalla Kablan,Wing Lon Ng +1 more
TL;DR: An adaptive neuro-fuzzy inference system (ANFIS) for financial trading, which learns to predict price movements from training data consisting of intraday tick data sampled at high frequency, outperforms standard strategies such as buy-and-hold or linear forecasting.