Return Predictability from Industry Network Effects: Evidence from Rolling Window Adaptive Lasso

56 Pages Posted: 27 Apr 2021

See all articles by Wentao Li

Wentao Li

Saïd Business School, University of Oxford

Date Written: July 30, 2020

Abstract

The literature argues that industry network effects imply the predictability of industry returns, while the return predictability can estimate the industry network in reverse. The paper employs rolling window adaptive lasso regressions to test the robustness of return predictions, showing the inherent model instability of the industry network and raising caveats for using the adaptive lasso method to estimate the industry network with a long time series. The out-of-sample predictability is examined by creating long-short trading portfolios based on the predictions. With non-parametric tests, the paper discovers that the out-of-sample predictability exists but is primarily due to the momentum effect. The adaptive lasso portfolios perform significantly worse than the momentum portfolio before the adjustment of risks. Based on the multifactor models, the trading portfolios also fail to continuously generate significant alphas for risk-averse investors.

Keywords: return predictability, network effects, market efficiency, adaptive lasso

JEL Classification: D85, G12, G14, G17

Suggested Citation

Li, Wentao, Return Predictability from Industry Network Effects: Evidence from Rolling Window Adaptive Lasso (July 30, 2020). Available at SSRN: https://ssrn.com/abstract=3834723 or http://dx.doi.org/10.2139/ssrn.3834723

Wentao Li (Contact Author)

Saïd Business School, University of Oxford ( email )

Park End Street
Oxford, OX1 1HP
Great Britain

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