Evidence and Behaviour of Support and Resistance Levels in Financial Time Series

TL;DR

This paper presents a heuristic algorithm to identify support and resistance levels in financial time series, demonstrating their significance in trend reversal, bounce likelihood, and decay over time, contributing to price predictability.

Contribution

The paper introduces a novel heuristic method for discovering SR levels that significantly influence price reversals and decay in financial data.

Findings

SR levels can reverse price trends statistically significantly.

Higher bounce counts at SR levels increase likelihood of future bounces.

SR levels decay over time, reducing bounce probability.

Abstract

This paper investigates the phenomenon of support and resistance levels (SR levels) in financial time series, which act as temporary price barriers that reverses price trends. We develop a heuristic discovery algorithm for this purpose, to discover and evaluate SR levels for intraday price series. Our simple approach discovers SR levels which are able to reverse price trends statistically significantly. Asset price entering SR levels with higher number of price bounces before are more likely to bounce on such SR levels again. We also show that the decay aspect of the discovered SR levels as decreasing probability of price bounce over time. We conclude SR levels are features in financial time series are not explained simply by AR(1) processes, stationary or otherwise; and that they contribute to the temporary predictability and stationarity of the investigated price series.