Key Points
- EAM could generate added alpha for active management to reclaim its edge over passive.
- The status quo has become a permanent trap for active managers without structural change.
- Ensemble Active Management has the potential to persistently outperform passive investing.
- Success Rates for actively managed funds are low, with average relative return underperforming.
- Ensemble Methods are designed to solve the Bias–Variance Conflict, providing a more accurate predictive engine.
- EAM Portfolios have had an average Success Rate of 77.1% against their benchmarks.
- EAM Portfolios have persistently outperformed their passive benchmarks.
- The EAM Portfolio owned 50 stocks compared to the multi-manager portfolio’s 563, generating superior investment returns.
I. Introduction
In recent years, there has been a consensus that passive management is the heir apparent to traditional active management, but this paper presents a different perspective. It introduces Ensemble Active Management (EAM) as a potential solution for active management to reclaim its edge over passive. The paper also addresses the need for structural change to break free from the permanent trap that active managers find themselves in.
II. Defining the Problem
The paper looks at the performance data for all 1,813 US equity mutual funds and evaluates their success rates. It reveals that the average success rate for the industry was only 41.6% and that active managers have been trending in the wrong direction in terms of performance.
The Results: Overall Assessment
The data shows that most funds have not been able to achieve their mandate, leading to accelerating net outflows from actively managed US equity funds. The paper also evaluates the “Alpha Gap” and finds that active’s relative underperformance is indeed structural.
III. Integrating Best Practices for Predictive Analytics into Investment Management
Ensemble Methods are introduced as a subcategory of machine learning and are designed to solve the Bias–Variance Conflict. The paper explains how Ensemble Active Management works and its three-step approach to building EAM Portfolios, providing the key to unlocking structural, incremental alpha.
IV. EAM Model Portfolios: Performance Validation
The paper presents the performance validation of EAM Model Portfolios, revealing their success rates and relative performance compared to benchmarks and actively managed fund peer groups. It shows that the EAM Portfolios have persistently outperformed their passive benchmarks and greatly outpaced traditional actively managed mutual funds.
V. Implications for the Industry
The analysis confirms the active management industry’s failure to beat its passive benchmarks and introduces Ensemble Active Management as a viable blueprint to improve investment decision making. The paper suggests that embracing EAM will require existing investment firms to change but emphasizes that the change is achievable.
Appendix 1
The paper presents a case study of the Netflix Prize, demonstrating the power of Ensemble Methods in practice and how they led to significant improvements in predictive accuracy.
Appendix 2
Statistical Comparison: EAM Portfolio vs. Corresponding Multi-Manager Portfolio is presented, showing the superior performance of the EAM Portfolio in terms of investment returns and risk-adjusted returns.
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Author : Editorial Staff
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