Working Paper: CEPR ID: DP12692
Authors: Allan Timmermann
Abstract: Our review highlights some of the key challenges in fi nancial forecasting problems along with opportunities arising from the unique features of fi nancial data. We analyze the difficulty of establishing predictability in an environment with a low signal-to-noise ratio, persistent predictors, and instability in predictive relations arising from competitive pressures and investors learning. We discuss approaches for forecasting the mean, variance, and probability distribution of asset returns. Finally, we cover how to evaluate fi nancial forecasts while accounting for the possibility that numerous forecasting models may have been considered, leading to concerns of data mining.
Keywords: No keywords provided
JEL Codes: No JEL codes provided
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| low signal-to-noise ratio in financial data (C58) | weak predictors (C29) |
| weak predictors (C29) | parameter estimation errors (C51) |
| competitive nature of financial markets (G19) | weak predictors (C29) |
| weak predictors (C29) | forecast accuracy (C53) |
| persistent predictors (C41) | biased estimates in return predictions (C51) |
| external market conditions (L19) | model effectiveness (C52) |
| model instability (C62) | forecast reliability (C53) |
| excessive model fitting (C52) | misleading conclusions about predictability (C53) |