Enhancing risk-adjusted performance of stock market intraday trading with Neuro-Fuzzy systems

Whilst the interest of many former studies on the application of AI in finance is solely on predicting market movements, trading practitioners are predominantly concerned about risk-adjusted performance. This paper provides new insights into improving the time-varying risk-adjusted performance of trading systems controlled by Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Systems (ANFIS) or Dynamic Evolving Neuro Fuzzy Systems (DENFIS). Contrary to most former studies which focus on daily predictions, we compare these models in an intraday stock trading scenario using high-frequency data. Firstly, we propose a dynamic extension of the popular moving average rule and enhance it with a model validation methodology using heat maps to analyse favourable profitability in specific holding time and signal regions. Secondly, we study the effect of realistic constraints such as transaction costs and intraday trading hours, which many existing approaches in the literature ignore. Thirdly, unlike most former studies that only aim to minimise statistical error measures, we compare this approach with financially more relevant risk-adjusted objective functions. To this end, we also consider an innovative ANFIS ensemble architecture which on an intraday level dynamically selects between different risk-adjusted models. Our study shows that accounting for transaction costs and the use of risk-return objective functions provide better results in out-of-sample tests. Overall, the ANN model is identified as a viable model, however ANFIS shows more stable time-varying performance across multiple market regimes. Moreover, we find that combining multiple risk-adjusted objective functions using an ANFIS ensemble yields promising results.