Time-dependent scaling patterns in high frequency financial data

TL;DR

This paper introduces new measures to analyze how different time-scales influence the dynamics of high-frequency financial data, revealing intraday trends and non-stationary multifractal behavior.

Contribution

It proposes two novel time-varying measures for analyzing financial time series and applies them to intraday stock data to uncover dynamic scaling patterns.

Findings

Different time-horizons vary in contribution over the trading day

Financial data exhibit non-stationary multifractal properties

Persistent behavior in mid-session, anti-persistent at open/close

Abstract

We measure the influence of different time-scales on the dynamics of financial market data. This is obtained by decomposing financial time series into simple oscillations associated with distinct time-scales. We propose two new time-varying measures: 1) an amplitude scaling exponent and 2) an entropy-like measure. We apply these measures to intraday, 30-second sampled prices of various stock indices. Our results reveal intraday trends where different time-horizons contribute with variable relative amplitudes over the course of the trading day. Our findings indicate that the time series we analysed have a non-stationary multifractal nature with predominantly persistent behaviour at the middle of the trading session and anti-persistent behaviour at the open and close. We demonstrate that these deviations are statistically significant and robust.