We are happy to share a new working paper “The Limited Virtue of Complexity in a Noisy World” by Álvaro Cartea, Qi Jin, and yuantao shi. Highlights: • In a high-dimensional factor space, increasing model complexity under proper regularization can enhance the predictability of asset returns; however, the Sharpe ratio of a portfolio of assets and the R-squared of the prediction of the asset returns decrease monotonically and are convex as the noise level in factors increases. • When only a subset of factors is observed, there is an optimal level of complexity beyond which incorporating additional factors can degrade portfolio performance due to the effect of noise in the factors. • They underscore a limited virtue of complexity in financial forecasting, where the performance of portfolios depends on the noise level in factors, and where more complex models do not necessarily lead to better performance when factors are not perfectly observed. Link to paper: https://lnkd.in/gE8RFqCs
Adjunct Professor of Financial Engineering, NYU Tandon School
2wVery interesting! Is there connection between your conclusions and AIC/BIC often used for finding an optimal number of predictors in econometric studies?