Structural Breaks as Investment Signals: BFAST vs. CUSUM in Quito’s Stock Market During COVID-19 Pandemic
Keywords:
BFAST, CUSUM, Time Series Analysis, COVID-19, Structural Break, Stock MarketsAbstract
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 algorithms—CUSUM and BFAST—are 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 Sharpe
ratios. Statistical validation using the Wilcoxon signed-rank test
confirms the significance of BFAST’s 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 in-
vestors a methodologically sound framework for decision-making
in volatile market environments.
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