Relative sentiment is an indicator that measures the positions, flows, and attitudes of institutional investors compared to those of individual investors–where institutions typically consist of large asset managers, insurance companies, pension funds, and endowments. In some instances, however–depending on the dataset and the asset class under consideration–institutions might also include hedge funds, CTAs, and other large speculators.
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.
The application of machine learning models to Sentix relative sentiment data appears to extract more predictive information than our original, simplistic approach was capable of.
The weight of the evidence suggests we recently exited a secular bull market driven by high real earnings growth and have entered a secular bear market driven by high inflation. The takeaway is that while investors have become highly conditioned to buy the dip, the current dip is occurring with relative sentiment significantly bearish (i.e., retail likes equities more than institutions). Historically, that has not been a great time to buy equities.
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