Research — 16 Apr, 2024
Data Arbitrage with Proprietary Dividend Forecasts - Historically Precise Updates Led to U.S. Outperformance
By Frank Zhao and Mengmeng Ao
Sell-side forecasts tend to focus on the top- and bottom-line and are often slow to reflect new dividend policies. Our empirical results have shown that S&P Global Market Intelligence’s Dividend Forecasting dataset has historically captured these dividend revisions in both a precise and timely manner, providing investors with an informational edge. This publication details how practitioners can leverage the dataset for equity investing in the U.S. market when the in-house FQ1 forecasts diverge from their sell-side counterparts.
Key findings in the U.S. equity market between January 2012 and June 2023 are:
- Historically Precise & Timely Updates: In-house FQ1 dividend forecasts were closer to the eventual dividend actuals than the sell-side consensuses in 88% of the 6,300+ divergence events.
- Short-Term Outperformance: Firms that had the most positive divergence outperformed the market by 126 basis points after one-week with a win hit ratio of 59%. Firms that had the most negative divergence underperformed the market by 172 basis points after one month with a win hit ratio of 57%.
- Long-Term Outperformance: A long-short monthly rebalancing strategy using the divergence signal yielded 3.81% annually with an information ratio of 0.69, after accounting for commonly used stock selection strategies.
- Low Correlations: The divergence signal was weakly correlated with commonly used strategies including those based on dividend actuals. The correlations ranged from -0.15 to 0.23.
Explore the data used to conduct this research
The Dividend Forecasting dataset contains independent dividend amount and date estimates for 28,000+ global stocks, ETFs and ADRs up to five years in the future. A global team of 40 dividend analysts deliver precise forecasts of the size and timing of payments based on bottom-up fundamental research, the latest company news and insight from a proprietary advanced analytics model. Investment banks, hedge funds, quants and asset managers utilize these forecasts to confidently price derivatives, enhance their investment strategies and better understand dividend risk.
This dataset consists of comprehensive global estimates based on projections, models, analysis, and research. This dataset can be used to evaluate earnings estimates to select stocks and manage investment performance and to track the direction and magnitude of upgrades and downgrades and more.