This article examines the reliability of Japanese candlestick patterns in signaling trend reversals across distinct market regimes. The dataset consists of 500 daily observations collected from publicly available financial market data. Focusing on key patterns such as the Hammer, Bullish Engulfing, Shooting Star, Bearish Engulfing, and Doji, the analysis combines automated pattern detection in Python with standard technical indicators, including moving averages, the Relative Strength Index, the Moving Average Convergence Divergence, and Bollinger Bands. Confirmation rates are compared between stable periods and crisis episodes, using logistic models and statistical tests to assess differences in predictive performance. The results show that candlestick patterns display moderate reliability in stable markets but lose a substantial part of their signaling power during crisis periods, when volatility and price discontinuities increase. This deterioration is consistent with insights from behavioral finance, where heightened uncertainty amplifies cognitive biases and weakens the informational content of technical signals. The findings support the use of candlestick patterns only within a broader framework that combines trend, momentum, and volatility filters, rather than as standalone decision tools, particularly when market conditions are unstable.
Rania Loubaris (Sat,) studied this question.