Cloud-Native Accounting Measurement: A Serverless Machine Learning Architecture for Integrating Climate Risk into Real-Time Equity Valuation
The measurement of climate risk and its influence on accounting-based equity valuation has become a critical mandate in empirical financial research. Traditional methodologies utilize log-linear valuation models and historical panel data to observe how investors adjust their relative valuation weights over long-run horizons. However, relying on annual reporting cycles and aggregated global temperature anomalies fails to capture the high-frequency market-accounting dynamics that occur during acute climate shocks. This paper proposes a cloud-native architectural framework utilizing Amazon Web Services to construct a real-time, serverless machine learning pipeline for accounting measurement. By deploying asynchronous Python middleware integrated with eXtreme Gradient Boosting algorithms, the proposed system programmatically ingests live spatiotemporal climate telemetry and high-frequency financial statement data. The system dynamically calculates the elasticities of market prices to the book value of equity and earnings, instantly adjusting corporate equity valuations in response to environmental volatility. Preliminary architectural evaluations demonstrate that decoupling data ingestion from traditional batch-processing significantly accelerates the empirical validation of the market-accounting relation, providing accounting researchers with a deterministic, highly scalable technological foundation for pricing climate risk.