A Multiple Rolling Turning Point Detection Method

Proceedings of The 4th International Conference on Advanced Research in Management, Business and Finance

Year: 2022



A Multiple Rolling Turning Point Detection Method

Riccardo Bramante, Silvia Facchinetti, Diego Zappa



Detecting time series turning points is crucial in the financial field where series are characterized by several changes in their trajectories. This paper proposes an extension of the method presented by (Bramante et al., 2019) and is based on a rolling test of hypothesis of a regression model slope change. The novel idea is to use entry and exit windows that contain more than one observation, thus contributing to a significant reduction of false signals and the corresponding probability of wrong decisions. To give evidence of the procedure’s performance in predicting turning points, we consider – as a preliminary analysis – a set of twenty stocks selected from the EURO STOXX 50 Index, covering the historical period 2010 – 2021. The model is run with different values of the main parameters, providing additional information in investment decision making.

keywords: Turning point detection, financial time series, time varying parameters, probability-based approach.