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Author ORCID Identifier

https://orcid.org/0009-0008-0926-5693

Abstract

This study investigates how unemployment and market volatility interact with stock prices in the Indian context, framing the stock–labour–volatility nexus as a complex adaptive system (CAS) rather than a set of linear, time-invariant relationships. Using verified secondary data on unemployment, India VIX, and NSE stock indices for 2013–2023, we first apply simple and multiple regression as a descriptive baseline. Results show a strong negative association between unemployment and stock prices (R ≈ 0.824, R² ≈ 0.68, p < 0.05), consistent with Keynesian demand-side channels, while the linear VIX–stock relationship is weak and statistically insignificant (R² ≈ 0.07, p > 0.05), consistent with the expectation that volatility operates through non-linear, regime-dependent mechanisms not captured by OLS.

Importantly, we document and transparently disclose critical data-integrity issues encountered in the AI dashboard phase: the unemployment series used in the dashboard exhibited synthetic periodicity incompatible with real labour market data, and the VIX series showed implausible sustained levels suggesting transformation artefacts. These findings are treated as negative results that motivate a rigorous data reconstruction programme rather than as publishable outputs. A rolling-correlation analysis between VIX and returns (ranging from approximately −0.6 to +0.7), which was methodologically sound, provides empirical support for the regime-dependent, non-linear character of the VIX–market relationship. The paper concludes with a structured roadmap for future work, including lag-structure econometrics, non-linear time-series methods, agent-based modelling, and a re-engineered real-time analytical pipeline grounded in verified data sources.

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