Relative Sentiment and Machine Learning for Tactical Asset Allocation

36 Pages Posted: 5 Nov 2019 Last revised: 25 Jan 2022

See all articles by Raymond Micaletti

Raymond Micaletti

Relative Sentiment Technologies, LLC

Date Written: October 25, 2019


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: or

Raymond Micaletti (Contact Author)

Relative Sentiment Technologies, LLC ( email )

The Views 3
Palmas Del Mar
Humacao, 00791
Puerto Rico

Do you have negative results from your research you’d like to share?

Paper statistics

Abstract Views
PlumX Metrics