Volatility-induced trend breaking: Unmasking weak cyclical signals in financial markets via stochastic resonance

Stochastic resonance (SR), the counterintuitive enhancement of weak signals by noise, is well-established in physics but rarely demonstrated empirically in finance. We analyze 47 years of S&P 500 data using wavelet decomposition and Hidden Markov Models (HMMs) to reveal a quarterly cyclical signal masked by market trends. At optimal volatility ([Formula: see text]), signal-to-noise ratio (SNR) increases by 47.66[Formula: see text]dB (95% CI: [46.11, 48.58][Formula: see text]dB, Cohen’s [Formula: see text], [Formula: see text]). We propose a novel mechanism: volatility breaks trend dominance through residence-time asymmetry collapse, decoupling trend from cycle. Kramers rate theory provides excellent validation ([Formula: see text]), confirming regime transitions follow thermally-activated escape dynamics. Out-of-sample validation on Gold futures (XAU/USD, 2008–2020) replicates the phenomenon ([Formula: see text], gain [Formula: see text] [Formula: see text]dB), establishing cross-asset universality. Results reframe market noise as information rather than risk.