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
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
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