Forecasting Performance of Nonlinear Models for Intraday Stock Returns

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Investigation of market efficiency and Financial Stability between S&P 500 and London Stock Exchange: Monthly and yearly Forecasting of Time Series Stock Returns using ARMA model (2016, background): “The empirical results indicated that nonlinear models outperformed linear models [18].”
Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and Particle Swarm Optimization (2013, methodology): “(2011) won the NN3 Forecasting Competition problem with an innovative approach based on the use of median for combining MLP forecasts and Matias and Reboredo (2012) forecast successfully with NNs and other nonlinear models intraday stock market returns.”
Back propagation neural network with adaptive differential evolution algorithm for time series forecasting (2015, background): “However, time series data in real-world settings commonly have nonlinear features under a new economic era (Lee & Tong, 2012; Liu & Wang, 2014a, 2014b; Matias & Reboredo, 2012).”