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

Riccardo Bramante, Silvia Facchinetti, Diego Zappa

Risultato della ricerca: Contributo in rivistaArticolo in rivistapeer review

2 Citazioni (Scopus)


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.
Lingua originaleEnglish
pagine (da-a)426-433
Numero di pagine8
RivistaStatistical Analysis and Data Mining
Stato di pubblicazionePubblicato - 2019


  • Turning point detection
  • financial time series
  • time varying parameters


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