Relative Sentiment and Machine Learning for Tactical Asset Allocation

35 Pages Posted: 5 Nov 2019 Last revised: 26 Nov 2019

Date Written: October 25, 2019

Abstract

We examine Sentix sentiment indices for use in tactical asset allocation. In particular, we construct monthly relative sentiment factors for the U.S., Europe, Japan, and Asia ex-Japan by taking the difference in 6-month economic expectations between each region's institutional and individual investors. These factors (along with one-month forward equity returns) then serve as inputs to a wide array of machine learning algorithms. Employing combinatorial cross-validation and adjusting for data snooping, we find relative sentiment factors have robust and significant predictive power in all four regions; that they surpass both standalone sentiment and time-series momentum in terms of informational content; and that they demonstrate the ability to identify the subsequent best- and worst-performing global equity markets from along a cross-section. The results are consistent with previous findings on relative sentiment, discovered using unrelated datasets.

Keywords: Market Timing, Smart Money, Investor Sentiment, Relative Sentiment, Time-Series Momentum, Tactical Asset Allocation, Sentix

JEL Classification: G10, G11, G14, G17

Suggested Citation

Micaletti, Raymond, Relative Sentiment and Machine Learning for Tactical Asset Allocation (October 25, 2019). Available at SSRN: https://ssrn.com/abstract=3475258

Raymond Micaletti (Contact Author)

Columbus Macro, LLC ( email )

103 Nesbitt Road
Suite 200
New Castle, PA 16105
United States

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