False Reality Bias in Treasury Management: A Behavioral Game Theory, Big Data, and Predictive Modeling Approach

This study investigates the False Reality Bias in treasury management—a cognitive distortion that causes small and medium-sized enterprises (SMEs) to equate positive bank balances with real financial stability. Using financial data from 50 Spanish meat-processing SMEs, the research introduces two behavioral-finance indices: the Liquidity Misperception Index (PEL) and the Liquidity Misconfidence Index (ICEL). Results reveal that 41% of firms overestimate liquidity (PEL = 1.21) and 40% display excessive confidence (ICEL > 1.3), both strongly associated with liquidity distress. Econometric tests confirm that firms with PEL > 1.2 are 4.48 times more likely to experience liquidity crises. Machine-learning models achieved over 80% accuracy in predicting distress. Behavioral interventions—such as AI-assisted cash-flow simulations—reduced misperceptions by 34.7% (p < 0.01). These results demonstrate that cognitive biases systematically distort treasury decisions but can be corrected through predictive behavioral modeling, offering actionable insights for SMEs, policymakers, and financial institutions seeking to improve financial resilience.