Publication

Implementing statistical criteria to select return forecasting models: What do we learn?

1999
Journal Articles

Abstract

Statistical model selection criteria provide an informed choice of the model with best external (i.e., out-of-sample) validity. Therefore they guard against overfitting ("data snooping"). We implement several model selection criteria in order to verify recent evidence of predictability in excess stock returns and to determine which variables are valuable predictors. We confirm the presence of in-sample predictability in an international stock market dataset, but discover that even the best prediction models have no out-of-sample forecasting power. The failure to detect out-of-sample predictability is not due to lack of power.

Official source

https://infoscience.epfl.ch/entities/publication/24b28512-a05d-4b44-adb2-ed150d77ae13

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1999
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/24b28512-a05d-4b44-adb2-ed150d77ae13

About this result

Related concepts (32)

Model selection

Model selection is the task of selecting a model from among various candidates on the basis of performance criterion to choose the best one. In the context of learning, this may be the selection of a statistical model from a set of candidate models, given data. In the simplest cases, a pre-existing set of data is considered. However, the task can also involve the design of experiments such that the data collected is well-suited to the problem of model selection.

Feature selection

Feature selection is the process of selecting a subset of relevant features (variables, predictors) for use in model construction. Stylometry and DNA microarray analysis are two cases where feature selection is used. It should be distinguished from feature extraction. Feature selection techniques are used for several reasons: simplification of models to make them easier to interpret by researchers/users, shorter training times, to avoid the curse of dimensionality, improve data's compatibility with a learning model class, encode inherent symmetries present in the input space.

Market anomaly

A market anomaly in a financial market is predictability that seems to be inconsistent with (typically risk-based) theories of asset prices. Standard theories include the capital asset pricing model and the Fama-French Three Factor Model, but a lack of agreement among academics about the proper theory leads many to refer to anomalies without a reference to a benchmark theory (Daniel and Hirschleifer 2015 and Barberis 2018, for example). Indeed, many academics simply refer to anomalies as "return predictors", avoiding the problem of defining a benchmark theory.

Related publications (21)

Implementing statistical criteria to select return forecasting models: What do we learn?

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