Article Details
  • Published Online:
    October  2025
  • Product Name:
    The IUP Journal of Accounting Research & Audit Practices
  • Product Type:
    Article
  • Product Code:
    IJARAP051025
  • DOI:
    10.71329/IUPJARAP/2025.24.4.92-112
  • Author Name:
    Linni Wilson and D Vennila
  • Availability:
    YES
  • Subject/Domain:
    Finance
  • Download Format:
    PDF
  • Pages:
    92-112
Volume 24, Issue 4, October-December 2025
Influence of Mental Accounting Biases on Derivative Trading Decisions: An Empirical Study
Abstract

The paper explores the influence of mental accounting biases (MAB) on derivative trading decisions (DTD) with specific focus on the mediating role of risk perception (RPE) among traders in Kochi, Kerala. Derivative trading, which involves high risks and substantial market variations, is inclined towards cognitive biases, especially mental accounting, where decisions are made irrationally and with no regard for other classifications but with certain financial divisions. The study evaluates the effects of these biases in trading decisions and whether risk perception mediates this relationship. A sample of 250 traders was taken, using the SEM technique, and hypotheses on the existence of risk perceptions were examined. The study indicates that reducing MAB may contribute to a more objective and efficient management of derivative contracts and underlines the negative consequences of such biases and the impact of risk perception. These findings have implications for traders, financial institutions and policymakers as they stress the importance of addressing traders’ self-control biases when designing and implementing interventions to boost the effects of their decision-making.

Introduction

The ever-shifting environment that defines financial markets means that traders tend to be guided by the use of heuristics and resultant cognitive biases. Mental accounting is another well-known self-control failure in the financial domain introduced by Thaler (1985), which focuses on the irrationality that people use when categorizing money and evaluating their resources based on specific and often unconnected classification systems.