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Our research has shown that certain factors have historically been closely associated with stock outperformance. Empirically strong and pervasive across time and geography, the alpha of these factors can be traced to a combination of behavioral inefficiencies, market structure, and risk premia. Our Quantitative Investment Group (QIG) seeks to exploit these factors, managing client assets in a variety of systematic approaches, including emerging market, low volatility, small cap, and alternative strategies.
We have an experienced, talented team of people with diverse, eclectic skills who are investors first, but then apply model-based, systematic quantitative investment tools to their insights.
We have found that stocks with a high degree of valuation uncertainty are more sensitive to investor sentiment and quality metrics, while stocks with a low degree of valuation uncertainty are more sensitive to valuation ratios and discounted cash-flow models. Our dynamic factor-weighting model assigns different factor weights to different groups of stocks, depending on their profile. The model also takes into account the evolution of a stock’s attributes over time. For example, as a stock moves from early growth to maturity or through market environments (from low to high uncertainty), we adjust factor weights accordingly. We believe this approach diversifies our factor exposure, improves our alpha models, and has the potential to reduce drawdown risk.
Collaboration with the firm’s macro, asset allocation, derivatives, and fundamental research teams encourages innovation within QIG and helps to challenge and validate our alpha models.
By combining the analysis of top-down factors with bottom-up signals, we have created a proprietary set of nontraditional alpha factors, improving our ability to produce excess returns in a variety of market environments.
Research input | QIG analysis |
Macro | Top-down factors
|
Global industry analysts | Bottom-up signals
|
Multi-asset | Top-down factors
|
Equity portfolio teams | Bottom-up signals
|
Our model assigns risk attributes to each stock and predicts how combinations of stocks may contribute to overall portfolio risk. But our model also goes several steps further, seeking:
We aim to distinguish intentional risks (arising from our alpha models) from unintentional risks (arising from style exposure or residual risk).
We integrate long-term return forecasts with our proprietary risk models, while also assessing the likelihood of short-term price movements. This multi-horizon alpha-modeling process seeks to ensure that each portfolio reflects an attractive combination of return potential, risk, and cost. Complete transparency in our research and portfolio management infrastructure enables this level of detail.
Less efficient market segments with large universes: | Emerging-market equities |
Areas where risk management and portfolio construction may provide an edge: | Low-volatility strategies |
Strategies that break the bonds of the long-only constraint in an effort to more efficiently express views on excess return potential: | Alternatives |
Wellington clients can invest in many of our investment approaches through separate accounts and an array of funds. For more information about investing in a separate account or US fund, please contact us.
All figures are for the Wellington Management Group of companies as at 31 December 2022
Our actively managed equity approaches span disciplines, geographies, industries, market capitalizations, and styles in order to meet our clients' objectives.
We use systematic analysis to detect and exploit factors that influence equity prices
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URL References
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By
Andrew Heiskell
Nicolas Wylenzek