Pricing weather risk: How precipitation affects stock prices and returns in Tunisia?

Purpose The present study examines the impact of precipitation on the Tunisian stock market, considering its vulnerability to droughts and the potential for rainfall-driven returns. Design/methodology/approach Using daily data from 2020 to 2023, our research employs ordinary least squares, quantile regression, Shannon transfer entropy and neural networks (NNs) to analyze the relationship between precipitation and Tunisian stock market indices, including sector-specific indices (financial, banks, insurance, financial services and industrials). Findings Quantile regression analysis highlights heterogeneous responses across sectors and market conditions, emphasizing the importance of considering bullish and bearish periods. Most indices show a positive relationship with precipitation during bullish periods (higher quantiles) and a negative or insignificant relationship during bearish periods. Transfer entropy confirms a unidirectional information flow from precipitation to stock returns, especially within the financial and financial services sectors, indicating predictive value. We find that only the seasonal component of rainfall exhibits a more pronounced influence, particularly on the financial and sub-financial sectors. Research limitations/implications These findings highlight the importance of incorporating both rainfall and its seasonal variations in financial analyses and risk management strategies within the Tunisian context. Originality/value It is novel to examine the impact of precipitation’s seasonal effect on the stock exchange.