Asset allocation under predictability and parameter uncertainty using LASSO

We consider a short-term investor who exploits return predictability in stocks and bonds to maximize mean-variance utility. Since the true parameters are unknown, we resort to portfolio optimization in form of linear regression with LASSO in order to mitigate problems related to estimation errors. As standard cross-validation relies on the assumption of i.i.d. returns, we propose a new type of cross-validation that selects $$ \lambda $$ λ from simulated returns sampled from a multivariate normal distribution. We find an inverse U-shaped relationship between the selected $$ \lambda $$ λ and the expected utility, and we show that the optimal value of $$ \lambda $$ λ declines as the number of observations used to estimate the parameters increases. We finally show how our strategy outperforms some commonly employed benchmarks.