Title: Asset pricing with quantile machine learning
Authors: Martin Hronec - Faculty of Social Sciences, Charles University in Prague (Czech Republic) [presenting]
Jozef Barunik - UTIA AV CR vvi (Czech Republic)
Abstract: A large scale empirical test is performed for an asset pricing model based on agents with quantile utility preferences instead of the standard expected utility. Using machine learning methods, we predict quantiles of individual stock returns obtaining the whole forecasted distributions. We document heterogeneity in models parameters across different quantiles. We show that forecasting all quantiles together, using multi-task deep learning is better than forecasting quantiles individually. The forecasting models allow us to construct portfolios based on the whole distribution instead of just a conditional mean. We show the economic value added of looking at the whole forecasted distribution by forming quantile-based long-short portfolios, as well as favourably forecasting value-at-risk.