Global Allocation Research Report
Manage Drawdowns Without Losing Upside Potential
Allocates ETFs using Advanced ML
Provides Unique, Actionable Insights
Augments and Sharpens your Decisions
Dynamically Allocates Global ETFs Using Machine Learning (ML)
The Global Allocation Research report uses the Laplace Advanced Machine Learning (ML) platform to shift to safe assets during turbulent markets and to riskier assets when all is clear. Holdings are 100% transparent, consisting entirely of low-cost, highly liquid ETFs. Published on the last trading day of the month, this report predicts the volatility and return for each of the world’s major asset classes and ranks the relative attractiveness of all eleven S&P 500 sectors for the subsequent month. The report discusses the machine learning (ML) factors driving the platform’s emotion-free allocation recommendation, providing readers unique insights to sharpen your investment decisions and discuss with clients.
Research Report Contents
Global ETF Allocation Strategy
- Recommends allocations within a portfolio of 15 low cost ETFs, each representing one of the world’s major asset classes.
- Augment your existing research and use to adjust the tactical portion of client portfolios.
S&P 500 Sector Insights
- Predicts relative attractiveness for the next month for all 11 sectors of the S&P 500 using advanced machine learning.
- Use to overweight or underweight sectors in order to improve performance relative to the S&P 500.
" I've been very impressed by the comprehensive approach used to ensure the algorithm is robust under a wide range of market situations. The depth of analysis and statistical rigor gives me the confidence I need to use it with my clients. "