This repository contains code and data for the paper "Scaling Law in Correlations of Returns in the S&P500". In this paper, we investigate the emergent phenomena of the market-mode adjusted pairwise correlations of returns over different time scales and discover two scaling properties.
Raw data is of the format:
2005-11-01 00:00:00,21.39
2005-11-01 00:01:00,21.39
2005-11-01 00:02:00,21.39
2005-11-01 00:03:00,21.39
2005-11-01 00:04:00,21.39
2005-11-01 00:05:00,21.39
2005-11-01 00:06:00,21.39
The data used in this study is collected from the S&P500 market data over almost 20 years (2004-2020). The data is preprocessed and stored in the Preprocessed directory. The Correlations directory contains the market-mode adjusted pairwise correlations of returns over different time scales (τ) for each year. The Histograms directory contains the scaled and zero-shifted distributions of the c_i,j (τ)’s for each year.
All the code used for preprocessing, analysis, and plotting is stored in the main directory. The scripts are run in this order: startup_indexer.py, correlation_indexer.py, histogram_indexer.py. The Plotters folder provides a suite of plotting and visualization notebooks for the paper.
The data folder structure is as follows:
<Working Directory>
|
|-- Preprocessed
|----- data_{year}.npy
|
|-- Correlations
|----- corr_res_{year}_{tau}.pkl
|
|-- Histograms
|----- hist_{mode}_{year}_{tau}.pkl
The paper can be accessed through this link: https://arxiv.org/pdf/2212.12703.pdf
If you use any of the data or code presented in this repository, please cite the following paper:
@article{scaling_law_sp500,
title={Scaling Law in Correlations of Returns in the S\&P500},
author={Authors},
journal={Journal},
year={Year}
}