Future Research Agenda
1. Future Research Agenda
The implicit contract framework generates testable predictions that remain incompletely evaluated in the existing literature. We organize future research around three priorities: (1) direct tests of the framework’s predictions, (2) extensions to new contexts, and (3) methodological advances.
1.1 Testing the Implicit Contract: Core Predictions
Skill-Drift-Performance Interactions
The framework’s central prediction (P1) is that skill moderates the drift-performance relationship. While existing evidence supports this prediction, more rigorous tests are needed.
Research Question: Can we identify ex ante skill measures that predict which managers will generate positive alpha from drift? Approach: Construct skill proxies (education, prior experience, industry specialization) and test whether these predict drift profitability. A valid skill measure should predict both: (i) higher absolute alpha; and (ii) more positive drift-performance relationships. Machine learning methods could identify non-linear skill indicators that traditional regressions miss.Market Discipline Dynamics
Prediction P6 states that funds experiencing outflows following poor drift will revert toward stated styles. This market discipline mechanism is central to the implicit contract’s enforcement.
Research Question: How quickly does market discipline operate, and what factors accelerate or slow the feedback loop? Approach: Estimate the lag between drift, performance, flows, and subsequent style reversion using vector autoregression or local projection methods. Cross-country variation in disclosure frequency provides natural experiments for testing whether faster information flow accelerates discipline.Reputation Effects
Prediction P5 concerns reputational enforcement: managers with higher stakes should drift less. This prediction has received limited direct testing.
Research Question: Do manager characteristics associated with reputation (tenure, visibility, ownership stake) predict drift behavior controlling for skill? Approach: Exploit manager turnover as a natural experiment. New managers (low reputation capital) should drift more than experienced managers in the same fund, controlling for fund characteristics. Similarly, managers approaching retirement may engage in “end-game” drift that experienced managers would avoid.1.2 Extensions to New Contexts
The implicit contract framework was developed for traditional equity mutual funds. Several emerging contexts provide opportunities to test its boundary conditions.
ESG and Sustainability Mandates
ESG funds represent a natural laboratory for testing the implicit contract because sustainability mandates are often more explicit than traditional style mandates.
Research Questions:- Do ESG funds exhibit “ESG drift” (deviation from sustainability objectives)?
- Is ESG drift tolerated or punished by flows?
- Does the skill-drift interaction operate for ESG dimensions?
The implicit contract framework predicts that ESG drift should be tolerated when skilled managers identify mispriced sustainable securities, but punished when drift reflects greenwashing. Testing this prediction requires developing ESG-specific drift measures analogous to Active Share.
Robo-Advisors and Algorithmic Portfolios
Algorithmic portfolio management provides a test of whether the implicit contract’s behavioral mechanisms matter. Robo-advisors should exhibit less drift because they lack the behavioral biases (disposition effects, overconfidence) that affect human managers.
Research Question: Do algorithmic portfolios exhibit less drift than human-managed funds, and what are the performance consequences? Approach: Compare style consistency between robo-advisor portfolios and matched human-managed funds. If algorithms drift less and this constrains value-creating flexibility, the implicit contract’s optionality mechanism receives support. If algorithms drift less with no performance cost, behavioral explanations for human drift gain support.Emerging Markets
Geographic concentration in U.S. markets limits generalization. Emerging markets provide variation in regulatory regimes, investor sophistication, and governance structures.
Research Question: Does the implicit contract operate differently in markets with weaker investor protection or less developed monitoring infrastructure? Approach: Exploit cross-country variation in disclosure requirements, board independence rules, and investor composition. The framework predicts that drift should be more value-destroying in markets with weaker discipline mechanisms.1.3 Methodological Advances
Causal Identification
Existing evidence on drift-performance relationships is largely correlational. Causal identification requires exogenous variation in drift.
Research Question: What is the causal effect of style drift on fund performance? Approach: Identify natural experiments that induce exogenous drift. Candidates include: (i) regulatory changes that force style shifts (e.g., concentration limits); (ii) benchmark reconstitutions that mechanically affect Active Share; (iii) fund mergers that combine different styles. Instrumental variable and regression discontinuity designs could provide cleaner causal estimates.Machine Learning for Drift Detection
Traditional drift measures rely on researcher-specified style categories. Machine learning offers potential for data-driven style classification.
Research Question: Can unsupervised learning identify style categories and drift patterns that predict performance better than traditional measures? Approach: Apply clustering algorithms to holdings data to identify emergent style categories. Use NLP on fund documents (prospectuses, shareholder letters) to detect textual signals of style change. Benchmark ML-detected drift against RBSA and HBSA in terms of performance prediction.1.4 Research Agenda Summary
Table summarizes the proposed research agenda with priority rankings.
Table: Future Research Agenda: Testing the Implicit Contract
| Market discipline dynamics | High | Measure feedback speed |
|---|---|---|
| ESG drift and greenwashing | High | Test optionality in ESG context |
| Robo-advisor comparison | High | Behavioral vs. rational drift |
| Causal identification | Medium | Natural experiments |
| Reputation effects | Medium | Manager turnover tests |
| ML drift detection | Medium | Data-driven classification |
The research agenda is organized around testing the implicit contract framework rather than cataloging gaps. Each proposed study directly tests a mechanism or prediction from our theoretical framework. This organization ensures that future research accumulates evidence for or against a unified theory rather than producing isolated empirical findings.
1.5 Implications for the Implicit Contract
The proposed research agenda has clear implications for evaluating the implicit contract framework:
If skill identification succeeds: The framework’s prediction that drift creates value for skilled managers receives strong support. Policy implications favor targeting interventions at low-skill managers rather than restricting drift generally. If market discipline operates quickly: The framework’s reliance on flow-based enforcement is validated. Policy can rely on disclosure rather than substantive constraints. If ESG/robo-advisor contexts differ systematically: The framework’s boundary conditions are clarified. Different regulatory approaches may be optimal for different fund types. If causal estimates differ from correlations: The framework requires refinement. Selection effects (skilled managers choosing to drift) versus treatment effects (drift causing performance) have different policy implications.The implicit contract framework provides a unified lens for interpreting diverse empirical findings. Future research should test this framework directly rather than accumulating findings without theoretical structure.