Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/94900
Title: Industry-based equity premium forecasts
Authors: Silva, Nuno 
Keywords: Equity premium prediction; Combination of forecast; Particle filter; Industries
Issue Date: 2018
Publisher: Emerald Publishing Limited
Serial title, monograph or event: Studies in Economics and Finance
Volume: 35
Issue: 3
Abstract: Purpose This paper aims to study whether the industry indexes predict the evolution of the broad stock market in the USA. Design/methodology/approach The study uses industry indexes to predict the equity premium in the USA. It considers several types of predictive models: constant coefficients and constant volatility, drifting coefficients and constant volatility, constant coefficients and stochastic volatility and drifting coefficients and stochastic volatility. The models are estimated through the particle learning algorithm, which is suitable for dealing with the problem that an investor faces in practice, given that it allows the investor to revise the parameters as new information arrives. The individual forecasts are combined based on their past performance. Findings The results reveal that models exhibit significant predictive ability. The models with constant volatility exhibit better performance, at the statistical level, but the models with stochastic volatility generate higher gains for a mean–variance investor. Practical implications This study’s findings are valuable not only for finance researchers but also for private investors and mutual fund managers, who can use these forecasts to improve the performance of their portfolios. Originality/value To the best of the knowledge of the author, this is the first paper that uses particle learning and combination of forecasts to predict the equity premium in the USA based on industry indexes. The study shows that the models generate valuable forecasts over the long time span that is considered.
URI: http://hdl.handle.net/10316/94900
ISSN: 1086-7376
DOI: 10.1108/SEF-10-2016-0256
Rights: openAccess
Appears in Collections:I&D CeBER - Artigos em Revistas Internacionais
FEUC- Artigos em Revistas Internacionais

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