A new behavioral finance mean variance framework

Purpose - The author proposes an update to the mean variance (MV) framework that replaces a constant risk aversion parameter using a dynamic risk aversion indicator. The contribution to the literature is made through making the static risk aversion parameter operational using an indicator of market sentiment. Results suggest that Sharpe ratios improve when the author replaces the traditional risk aversion parameter with a dynamic sentiment indicator from the behavioral finance literature when allocating between a risky portfolio and a risk-free asset. However, results are mixed when using the behavioral framework to allocate between two risky assets. Design/methodology/approach - The author includes a dynamic risk aversion parameter in the mean variance framework and back test using the traditional and updated behavioral mean variance (BMV) framework to see which framework leads to better performance. Findings - The author finds that the behavioral framework provides superior performance when allocating between a risky and risk-free asset; however, it under performs when allocating between risky assets. Research limitations/implications - The research is based on back testing; therefore, it cannot be concluded that this strategy will perform well in real-time circumstances. Practical implications - Portfolio managers may use this strategy to optimize the allocation between a risky portfolio and a risk-free asset. Social implications - An improved allocation between risk-free and risky assets that could lead to less leverage in the market. Originality/value - The study is the first to use such a sentiment indicator in the traditional MV framework and show the math.