Adaptive Evolutionary Neural Networks for Forecasting and Trading without a Data‐Snooping Bias

Abstract

In this paper, we present two Neural Network based techniques, an adaptive evolutionary Multilayer Perceptron (aDEMLP) and an adaptive evolutionary Wavelet Neural Network

European Exchange Trading Funds Trading with Locally Weighted Support Vector Regression

“…They seem to have low power and volatile behaviour through time (LeBaron (2000) and Qi and Wu (2006)). On the other hand, artificial intelligence models and heuristics have provided promising empirical evidence in trading applications (see amongst others Allen and Karjalainen (1999), Jasic and Wood (2004) and Sermpinis et al (2016)). However their atheoretic nature and the lack of sufficient robustness checks on their performance is generating scepticism among finance professionals and researchers.…”

Trading Macro-Cycles of Foreign Exchange Markets Using Hybrid Models

“…In recent years, there has been an increase in the popularity of using the combination (or ensemble) models, namely, using more than one model or weighted model for prediction and exchange rate forecasts (see [43][44][45][46][47][48][49]). The basic idea is that a combined model will be better able to capture the unique features and different patterns of the dataset and improve the predictive power.…”

“…Sermpinis et al [46] introduce a hybrid neural network architecture of Particle Swarm Optimization and Adaptive Radial Basis Function (ARBF-PSO) to forecast the EUR/USD, EUR/GBP and EUR/JPY exchange rates, which is found to outperform all other models in terms of statistical accuracy and trading efficiency for the three exchange rates. Sermpinis et al [47] benchmark the performance of two neural-network-based techniques against two traditional architectures in forecasting and trading, and find that the proposed architectures present superior forecasting and trading performance compared to the benchmarks. Rivas et al [48] use ANN together with Genetic Algorithm (GA) to implement a kind of environment that can perform time series predictions in forecasting currencies exchange.…”

The wavelet scaling approach to forecasting: Verification on a large set of Noisy data

“…There exist numerous forecasting procedures utilizing the wavelet transform, encompassing such diversified approaches as wavelet denoising of deterministic signals based on various thresholding rules followed by ARIMA model building (Alrumaih & Al-Fawzan, 2002;Ferbar, Čreslovnik, Mojškerc, & Rajgelj, 2009;Herwartz & Schlüter, 2017), wavelet multiresolution decomposition combined with principal component analysis and/or a separate modeling of the signal's smooths and details (Fernandez, 2008;Rua, 2011;Rua, 2017;Wong, Ip, Xie, & Lui, 2003;Zhang, Coggins, Jabri, Dersch, & Flower, 2001), linear and nonlinear multiscale models based on the Haar wavelet coefficients (Berger, 2016;Murtagh, Starck, & Renaud, 2004;Renaud, Starck, & Murtagh, 2003), forecasting coefficients of the wavelet expansion (called wavelet and scaling coefficients) followed by applying the inverse wavelet transform to the results (Chen, Nicolis, & Vidakovic, 2010;Kaboudan, 2005;Rostan & Rostan, 2018), artificial neural networks combined with the wavelet methodology either in the form of wavelet neural networks using wavelets as activation functions in radial basis function networks (Alexandridis & Zapranis, 2013;Sermpinis, Verousis, & Theofilatos, 2016) or through the use of wavelet coefficients as inputs to a neural network model (Murtagh et al, 2004;Minu et al, 2010;Ortega & Khashanah, 2014;Renaud et al, 2003), and, finally, modeling locally stationary wavelet processes (Fryźlewicz, Van Bellegem, & von Sachs, 2003). For a detailed discussion of these concepts, see, for example, Bruzda (2013) and Schlüter and Deuschle (2014).…”