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Predicting Stock Market Returns by Combining Forecasts

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Author Info
Laurence Fung (Research Department, Hong Kong Monetary Authority)
Ip-wing Yu (Research Department, Hong Kong Monetary Authority)
Abstract

The predictability of stock market returns has been a challenge to market practitioners and financial economists. This is also important to central banks responsible for monitoring financial market stability. A number of variables have been found as predictors of future stock market returns with impressive in-sample results. Nonetheless, the predictive power of these variables has often performed poorly for out-of-sample forecasts. This study utilises a new method known as "Aggregate Forecasting Through Exponential Re-weighting (AFTER)" to combine forecasts from different models and achieve better out-of-sample forecast performance from these variables. Empirical results suggest that, for longer forecast horizons, combining forecasts based on AFTER provides better out-of-sample predictions than the historical average return and also forecasts from models based on commonly used model selection criteria.

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Publisher Info
Paper provided by Hong Kong Monetary Authority in its series Working Papers with number 0801.

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Length: 30 pages
Date of creation: Mar 2008
Date of revision:
Handle: RePEc:hkg:wpaper:0801

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Related research
Keywords: Forecasting; Model combination; Model uncertainty;

Find related papers by JEL classification:
G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
G12 - Financial Economics - - General Financial Markets - - - Asset Pricing
C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation

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