This paper develops a novel spectral framework for financial market analysis inspired by the analytic structure of the Riemann Hypothesis (RH). Specifically, it integrates tools from Random Matrix Theory (RMT), Fourier decomposition, and explicit-formula analogues from number theory to construct an interpretable and empirically testable model of market dynamics. Using daily data from the NIFTY 50 index and 29 constituent stocks over the period January 2018 to December 2025 (1,970 observations), along with 50 Reserve Bank of India (RBI) Monetary Policy Committee (MPC) events, the study produces three key results. First, the cross-sectional correlation matrix exhibits a bulk eigenvalue distribution consistent with the Marchenko–Pastur law, indicating that most correlations are noise-driven, with only four statistically significant factors. Second, return distributions show strong non-normality, with high excess kurtosis and negative skewness, reinforcing the need for non-Gaussian modeling approaches. Third, an event-driven trading strategy based on post-announcement mean reversion achieves a substantially higher risk-adjusted performance (Sharpe ratio ≈ 2.0) compared to buy-and-hold, albeit with lower market exposure. A central contribution of the paper is the construction of an explicit “RH–Finance dictionary,” mapping number-theoretic objects (zeta zeros, primes, explicit formula) to financial analogues (market cycles, macroeconomic events, and price decomposition into trend, cycles, and impulses). This framework enables a structured decomposition of price dynamics and provides a foundation for explainable AI systems in finance. While the RH analogy is heuristic rather than formal, it generates concrete, testable modeling structures, including spectral filtering, correlation cleaning, and event-based signal extraction. The findings highlight both the promise of spectral methods in financial modeling and the limitations imposed by small event samples and the absence of surprise-based event decomposition. This work contributes to the intersection of quantitative finance, econophysics, and analytic number theory, and is intended as a foundation for future research in spectral and explainable financial intelligence systems
Ketanjit Ningthoujam (Sat,) studied this question.