Predicting Stock Market Returns by Combining Forecasts

Laurence Fung and Ip-wing Yu
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Laurence Fung: Research Department, Hong Kong Monetary Authority
Ip-wing Yu: Research Department, Hong Kong Monetary Authority

No 801, Working Papers from 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.

Keywords: Forecasting; Model combination; Model uncertainty (search for similar items in EconPapers)
JEL-codes: C13 G11 G12 (search for similar items in EconPapers)
Pages: 30 pages
Date: 2008-03
New Economics Papers: this item is included in nep-ecm, nep-fmk, nep-for and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:hkg:wpaper:0801

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