International Research Journal of Education and Technology

This study investigates the influence of robo-advisory platform adoption on retail investor risk tolerance using a longitudinal secondary data framework spanning ten years (2015–2025). Employing Pearson correlation analysis and OLS multiple regression modelling across three behavioural proxy variables — retail equity allocation percentage, net equity fund inflows, and retail market participation rates — with macroeconomic controls including market volatility (VIX), interest rates, GDP growth, and equity market returns, the study finds robust evidence that robo-advisory growth significantly and independently increases retail investor risk exposure. Robo-advisory Assets Under Management (RA-AUM) emerged as the strongest and most consistent predictor across all three regression models (β = 0.61, 0.67, and 0.53 respectively; all p < 0.01), with model R² values ranging from 0.87 to 0.93. Market volatility consistently dampened risk tolerance across models, while a statistically significant interaction effect confirms that robo-advisors partially buffer investor behaviour against volatility shocks. These findings advance the understanding of algorithmic financial intermediation as a behavioural force operating at population scale, with implications for platform design, financial regulation, and investor education policy in emerging and developed markets alike.

Keywords: robo-advisors, retail investor risk tolerance, fintech adoption, behavioural finance, equity allocation, multiple regression, algorithmic financial advice, emerging markets