THE INFLUENCE OF BEHAVIORAL FINANCE ON MANAGERIAL DECISION-MAKING: HOW COGNITIVE BIASES AFFECT CORPORATE FINANCIAL STRATEGIES

Behavioral finance has become a critical factor influencing corporate financial strategies, particularly through cognitive biases that distort managerial decision-making. Traditional financial theories assume rationality in corporate financial decisions, but empirical evidence suggests that biases such as overconfidence, loss aversion, and herd mentality significantly impact investment choices, risk-taking, and market stability. This study examines the extent to which these biases affect corporate financial strategies, focusing on firms operating between 2017 and 2021. Using a quantitative approach, secondary financial data from 200 firms was analyzed through statistical models, including chi-square tests, t-tests, and Pearson correlation analysis. The findings reveal that overconfidence bias does not significantly correlate with excessive risk-taking (χ² = 1.16, p = 0.884), contradicting previous research. However, loss aversion strongly impacts investment rejection rates (t = -15.42, p = 3.10e-07), leading to conservative financial strategies that hinder growth. Moreover, herd mentality exhibits a near-perfect correlation with market volatility (r = 0.995, p = 0.00047), confirming its role in amplifying systemic financial risks. A multiple regression model further demonstrates that loss aversion and overconfidence negatively affect capital allocation, while herd mentality contributes to volatility (R² = 1.000). These findings underscore the necessity for structured financial decision-making frameworks, bias-awareness training, and regulatory policies that encourage independent financial analysis. This research contributes to the evolving discourse on behavioral finance by providing empirical evidence on its implications for corporate governance and investment strategies. It recommends corporate leaders integrate behavioral insights into risk management models and financial forecasting to mitigate bias-driven inefficiencies.