Assistant Professor of Economics Javier Pereira recently presented a paper at the 24th International Conference on Computing in Economics and Finance of the Society for Computational Economics. The meeting took place at Università Cattolica del Sacro Cuore in Milan, Italy.
“Market Stability with Machine Learning Agents,” co-authored with Professor of Economics Chris Georges, uses an agent-based model of a financial market to illustrate how attention to forecast model selection by traders affects asset price volatility and financial market stability. Traders in this model are critical of their own forecasting models of asset returns and perform ongoing model selection to improve them.
The authors find via simulation that the addition of model selection and other regularization methods to the traders’ learning algorithms reduces but does not eliminate overfitting and resulting excess volatility.
These results suggest that even a high degree of attention to overfitting on the part of traders who are engaged in data mining may not entirely eliminate destabilizing speculation. The results are also consistent with recent empirical findings that suggest “pockets of predictability” in asset returns.
