Chaos Measure Dynamics | Multifactor Financial Market Model | Presentation at NODYCON 2023
Channel: VoglData
Date: 2023-06-20

This video contains my live presentation at the NODYCON 2023, Third International Nonlinear Dynamics Conference. The conference is held in Rome at the Sapienza Università di Roma from June 18-22, 2023. 

The topic of the presentation is my paper "Co-Integrated Chaos Measure Dynamics in a Multifactor Model for Financial Market Predictions".

Abstract. 
To answer the question if chaos changes over time, we apply rolling windows to wavelet-denoised logarithmic S&P500 returns (2000-2020) and calculate consecutive chaos measures (e.g. Hurst-, maximum Lyapunov exponent or sample entropy). We demonstrate time-variation of the chaos measure series, indicating chaos instability or inherent chaotic time variations of the underlying (hyper-) chaotic deterministic S&P500 return system. Further, we show these chaos measure series to be co-integrated following a vector error correction model (VECM) based Johansen test. An optimised selection of these series is used as input features for a dynamic factor model (DMF) realised as deep learning multilayer perception (DL-MLP) neural network to predict the original S&P500 price series out-of-sample. The approach is validated by performance metrics (e.g. explained variance score) and the residuals are shown to be non-autocorrelated and ~iid. Thus, the approach provides a novel multifactor model for practical market price predictions from a closed-system view. 

Keywords. 
time-varying chaos, chaos instability, co-integration, dynamic factor model, deep learning neural network financial predictions

JEL.
 G1, C01, C02, C22, C18

MSC.
 65P20, 37N30, 65P40, 91-10



00:08 Introduction
05:16 Graphics of time-varying chaos measures
06:50 Co-Integration approach
07:28 Forecasting with DL-MLP model
08:17 Concluding Remarks


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