How Good Can Heuristic-Based Forecasts Be? A Comparative Performance of Econometric and Heuristic Models for UK and US Asset Returns

92 Pages Posted: 22 Jul 2014 Last revised: 26 Jun 2017

See all articles by Massimo Guidolin

Massimo Guidolin

Bocconi University - Department of Finance

Alexei G. Orlov

Securities and Exchange Commission

Manuela Pedio

Bocconi University - CAREFIN - Centre for Applied Research in Finance

Date Written: June 25, 2017

Abstract

This paper systematically investigates the sources of differential out-of-sample predictive accuracy of heuristic frameworks based on internet search frequencies and a large set of econometric models. The volume of internet searches helps gauge the degree of investors' time-varying interest in specific assets. We use a wide range of state-of-the-art models, both of linear and non-linear type (regime-switching predictive regressions, threshold autoregressive, smooth transition autoregressive), extended to capture conditional heteroskedasticity through GARCH models. The predictor variables investigated are those typical of the literature featuring a range of macroeconomic and market leading indicators. Our out-of-sample forecasting exercises are conducted with reference to US, UK, French and German data, both stocks and bonds, and for 1- and 12-months-ahead horizons. We employ several forecast performance metrics and predictive accuracy tests. Internet-search-based models are found to perform better than the average of all of the alternative models. For several country-asset-horizon combinations, particularly for UK bond returns, our heuristic models compare favorably with sophisticated econometric methods. The heuristic models are also shown to perform well in forecasting realized volatility. The baseline results are supported by several extensions and robustness checks, such as using alternative search keywords, controlling for Fama-French and Cochrane-Piazzesi factors, and implementing heuristic-based trading strategies.

Keywords: predictive regressions, forecasting, behavioral finance, heuristics, investor attention, information demand, Google search volume index, web-search-based forecasts

JEL Classification: G17, G02, G12, C53, E44

Suggested Citation

Guidolin, Massimo and Orlov, Alexei G. and Pedio, Manuela, How Good Can Heuristic-Based Forecasts Be? A Comparative Performance of Econometric and Heuristic Models for UK and US Asset Returns (June 25, 2017). Available at SSRN: https://ssrn.com/abstract=2469120 or http://dx.doi.org/10.2139/ssrn.2469120

Massimo Guidolin

Bocconi University - Department of Finance ( email )

Via Roentgen 1
Milano, MI 20136
Italy

Alexei G. Orlov (Contact Author)

Securities and Exchange Commission ( email )

100 F Street NE
Washington, DC 20549
United States

Manuela Pedio

Bocconi University - CAREFIN - Centre for Applied Research in Finance ( email )

Via Sarfatti, 25
Milan, 20136
Italy

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