Alpha Research Framework AI Prompt

Design a rigorous alpha research process for evaluating new investment signals.

Research question: {{hypothesis}} (e.g. 'Do stocks with improving earnings revision momentum outperform over the next month?')

1. Hypothesis formation (before looking at data):
   - State the economic intuition: WHY should this signal predict returns?
     - Risk premium explanation: investors demand compensation for bearing this risk
     - Behavioral explanation: systematic investor error that is exploitable
     - Structural explanation: market friction or institutional constraint creates opportunity
   - A signal without economic intuition is more likely to be a false positive
   - Write down the hypothesis before touching the data

2. Universe and data definition:
   - Define the asset universe: which assets, with what inclusion/exclusion criteria?
   - Define the signal: exactly how is it computed? What data inputs? What timing lag?
   - Data sources: where does each input come from? Is it point-in-time?
   - Survivorship bias: is the universe constructed using only assets that existed at each historical date?

3. Research protocol to prevent data snooping:
   - Split data into 3 periods BEFORE any analysis:
     - Training set (50%): hypothesis development, initial signal construction
     - Validation set (25%): parameter selection and signal refinement
     - Test set (25%): final evaluation ONCE, never used before the final test
   - Never look at the test set until the signal is fully specified
   - Document all analysis decisions and the order in which they were made

4. Signal evaluation hierarchy:
   Level 1: Statistical evidence
   - IC, ICIR, t-statistic (require t > 3.0 given prior testing in the field)
   - Quintile portfolio analysis: is the relationship monotonic?

   Level 2: Economic and institutional reality
   - Is the signal implementable? (Available in real time, not too slow to compute)
   - Survives transaction costs? (Net IC > 0 after realistic costs)
   - Capacity: at what AUM level does market impact eliminate the alpha?

   Level 3: Robustness
   - Consistent across time periods, market regimes, geographies?
   - Survives reasonable parameter perturbations?
   - Different from known factors? (Not just a proxy for value, momentum, or quality)

5. The research log:
   - Keep a contemporaneous log of every analysis run, its motivation, and its result
   - Include failed experiments: they constrain the hypothesis space for future work
   - This log is evidence against data snooping allegations

Return: hypothesis statement with economic rationale, data protocol, three-way split design, evaluation criteria, and research log template.