Does a lot help a lot? Forecasting stock returns with pooling strategies in a data‐rich environment

DOI10.1002/for.2475zbMath1397.62399OpenAlexW2619229661MaRDI QIDQ4687660

Fabian Baetje

Publication date: 12 October 2018

Published in: Journal of Forecasting (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/for.2475

zbMATH Keywords

principal componentsfactor modelsforecast combinationreturn predictability

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Applications of statistics to actuarial sciences and financial mathematics (62P05)

Cites Work

• Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation
• Approximately normal tests for equal predictive accuracy in nested models
• Are more data always better for factor analysis?
• Forecasting the yield curve in a data-rich environment: a no-arbitrage factor-augmented VAR approach
• Forecasting economic time series using targeted predictors
• The three-pass regression filter: a new approach to forecasting using many predictors
• To Combine Forecasts or to Combine Information?
• Predicting the Equity Premium with Dividend Ratios
• Tests for Parameter Instability and Structural Change With Unknown Change Point
• Forecasting Using Principal Components From a Large Number of Predictors
• Estimating and Testing Linear Models with Multiple Structural Changes
• In-Sample or Out-of-Sample Tests of Predictability: Which One Should We Use?
• Common risk factors in the returns on stocks and bonds
• Determining the Number of Factors in Approximate Factor Models
• Prediction by Supervised Principal Components

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