Introduction

Market sentiment oscillates in cycles: periods of optimism giving way to pessimism and back. Wavelet analysis—a time-frequency decomposition technique—reveals cyclical patterns in sentiment. Combining wavelet analysis of historical returns with real-time social sentiment enables traders to identify current position in the mood cycle and anticipate regime shifts.

Wavelet Decomposition

Apply continuous wavelet transform to historical asset returns, decomposing into frequency components. Identify dominant cycles (e.g., 30-week mood cycles). For each time period, determine where in the cycle the market is located (early cycle, peak, trough, recovery). Compare to current social sentiment to validate cycle position. When sentiment diverges from expected cycle position, expect reversion.

Cycle-Based Trading

When the market is at cycle peaks but sentiment remains elevated, position for reversal. When at cycle troughs but sentiment remains negative, position for recovery. Cycle-aware positioning improves risk-adjusted returns by anticipating sentiment reversals.

Conclusion

Wavelet analysis of market cycles combined with real-time sentiment reveals position in mood cycles, enabling anticipatory positioning.