Analysis of temporal pattern, causal interaction and predictive modeling of financial markets using nonlinear dynamics, econometric models and machine learning algorithms

This paper presents a novel predictive modeling framework for forecasting the future returns of financial markets. The task is very challenging as the movements of the financial markets are volatile, chaotic, and nonlinear in nature. For accomplishing this arduous task, a three-stage approach is proposed. In the first stage, fractal modeling and recurrence analysis are used, and the efficient market hypothesis is tested to comprehend the temporal behavior in order to investigate autoregressive properties. In the second stage, Granger causality tests are applied in a vector auto regression environment to explore the causal interaction structures among the indexes and identify the explanatory variables for predictive analytics. In the final stage, the maximal overlap discrete wavelet transformation is carried out to decompose the stock indexes into linear and nonlinear subcomponents. Seven machine and deep learning algorithms are then applied on the decomposed components to learn the inherent patterns and predicting future movements. For numerical testing, the daily closing prices of four major Asian emerging stock indexes, exhibiting non-stationary behavior, during the period January 2012 to January 2017 are considered. Statistical analyses are performed to ascertain the comparative performance assessment. The obtained results prove the effectiveness of the proposed framework.