Detecting time series turning points is crucial in the financial field where series are characterized by several changes in their trajectories. This paper proposes a multiple rolling test of hypothesis of a regression model slope change where the entry and exit windows 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.
Bramante, R., Facchinetti, S., Zappa, D., A Multiple Rolling Turning Point Detection Method, International Conference on Advanced Research in Management, Business and Finance, Diamond Scientific Publication, Vilnius (Lituania) 2022: 36-43 [https://hdl.handle.net/10807/224818]
A Multiple Rolling Turning Point Detection Method
Bramante, Riccardo;Facchinetti, Silvia;Zappa, Diego
2022
Abstract
Detecting time series turning points is crucial in the financial field where series are characterized by several changes in their trajectories. This paper proposes a multiple rolling test of hypothesis of a regression model slope change where the entry and exit windows 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.
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