Estimating reward & risk with machine learning improves portfolio performance by up to 28%
已发布 23 二月, 2022
In a study published in KeAi’s The Journal of Finance and Data Science, Michael Pinelis and David Ruppert of US’ Cornell University used macroeconomic data to forecast the monthly prevailing volatility in the US stock market, in addition to the expected return. Previous studies have focused on just the monthly expected return, or predicted both components with simple linear models.