The problem related to the identification of a change in time series trajectories plays a crucial role in many contexts. In this paper, we propose a flexible and computationally efficient procedure for turning point identification based on hypothesis testing applied to the difference between two consecutive slopes in a rolling regression framework. Along with the description of the methodology, to measure the performance of the method we have applied it to the S&P 500 Stock Index and its subsector indices. By using an in-sample/out-of-sample approach we compare results with the profit/losses we could obtain by using themoving average crossover strategy. Results show that the operating signals obtained by our proposal may better enable financial analysts to make profitable decisions. Finally we present an extensive simulation study to show the weaknesses and strengths of the proposal under different expected returns and volatility scenarios.

Bramante, R., Facchinetti, S., Zappa, D., Online detection of financial time series peaks and troughs: A probability‐based approach*, <<STATISTICAL ANALYSIS AND DATA MINING>>, 2019; 12 (5): 426-433. [doi:10.1002/sam.11411] [https://hdl.handle.net/10807/224947]

Online detection of financial time series peaks and troughs: A probability‐based approach*

Bramante, Riccardo;Facchinetti, Silvia
;
Zappa, Diego
2019

Abstract

The problem related to the identification of a change in time series trajectories plays a crucial role in many contexts. In this paper, we propose a flexible and computationally efficient procedure for turning point identification based on hypothesis testing applied to the difference between two consecutive slopes in a rolling regression framework. Along with the description of the methodology, to measure the performance of the method we have applied it to the S&P 500 Stock Index and its subsector indices. By using an in-sample/out-of-sample approach we compare results with the profit/losses we could obtain by using themoving average crossover strategy. Results show that the operating signals obtained by our proposal may better enable financial analysts to make profitable decisions. Finally we present an extensive simulation study to show the weaknesses and strengths of the proposal under different expected returns and volatility scenarios.
2019
Inglese
Bramante, R., Facchinetti, S., Zappa, D., Online detection of financial time series peaks and troughs: A probability‐based approach*, <<STATISTICAL ANALYSIS AND DATA MINING>>, 2019; 12 (5): 426-433. [doi:10.1002/sam.11411] [https://hdl.handle.net/10807/224947]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/224947
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