# Reduced-order autoregressive dynamics of a complex financial system: a PCA-based approach

arXiv: https://arxiv.org/abs/2212.12044 · HTML: https://mlsanity.com/paper/2212.12044/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 47 references that are in the map.
- First posted on arXiv 22 Dec 2022.

## Builds on (most cited first, 2 of 2)

- [Extension of PCA to Higher Order Data Structures: An Introduction to Tensors, Tensor Decompositions, and Tensor PCA](https://mlsanity.com/paper/1803.00704/) (2018)
- [Machine learning model to project the impact of Ukraine crisis](https://mlsanity.com/paper/2203.01738/) (2022)

## Built on by (most cited first, 1 of 1)

- [A comparison between Recurrent Neural Networks and classical machine learning approaches In Laser induced breakdown spectroscopy](https://mlsanity.com/paper/2304.08500/) (2023)

## Abstract (arXiv)

This study analyzes the dynamic interactions among the NASDAQ index, crude oil, gold, and the US dollar using a reduced-order modeling approach. Time-delay embedding and principal component analysis are employed to encode high-dimensional financial dynamics, followed by linear regression in the reduced space. Correlation and lagged regression analyses reveal heterogeneous cross-asset dependencies. Model performance, evaluated using the coefficient of determination (R2), demonstrates that a limited number of principal components is sufficient to capture the dominant dynamics of each asset, with varying complexity across markets.