Structural Breaks as Investment Signals: BFAST vs. CUSUM in Quitos Stock Market During COVID-19 Pandemic

This study investigates the potential of structural break detection in stock price time series as a tool for investment decision-making in emerging markets. Operating under the hypothesis that structural breaks reflect shifts in underlying price trends, we conduct an empirical analysis of investment performance in the Quito Stock Exchange (QSE) using monthly average prices from 2013 to 2022 for the ten most actively traded companies, selected for their transaction volume and sectoral representativeness. Importantly, this period coincided with the COVID-19 pandemic, providing a natural context to explore how structural breaks behave under heightened market volatility. Two algorithmsCUSUM and BFASTare applied and compared in terms of their ability to identify actionable breakpoints and generate profitable buy/sell signals. Results show that BFAST, originally developed for remote sensing applications, consistently outperforms CUSUM: it detects a higher proportion of successful signals, yields stronger average returns over a six-month evaluation window (+17% in the financial sector and+18.75% in the productive/commercial sector), and achieves superior risk-adjusted performance as measured by Sharperatios. Statistical validation using the Wilcoxon signed-rank test confirms the significance of BFASTs advantage (p = 0.004).Taken together, these findings position BFAST as a robust and economically relevant tool for financial time-series analysis, extending its utility beyond traditional domains and offering investors a methodologically sound framework for decision-making in volatile market environments.