Quantitative Research Process AI Prompts

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.

Assess whether this factor or strategy is crowded and estimate the associated risks. Factor / strategy: {{factor}} Market data: {{data}} 1. Why crowding matters: A crowded trade occurs when many investors hold similar positions. When they simultaneously unwind — due to redemptions, losses, or regulatory changes — the factor experiences a 'crowding unwind': rapid, correlated losses that are NOT predicted by the factor's historical distribution. 2. Crowding metrics: Short interest approach: - For a long-short factor: are the 'short' securities heavily short-sold by many investors? - Short interest ratio: short_shares / average_daily_volume. High = potential crowding. - Change in short interest: rising short interest → increasing crowding Institutional ownership concentration: - Are the 'long' positions heavily owned by a similar set of quant funds? - 13-F filing analysis: overlap in top holdings across quant fund portfolios - High overlap = high crowding risk Return correlation with known crowded factors: - Regress the factor return on returns of known crowded strategies (AQR QMOM, etc.) - High correlation → this factor may be susceptible to the same crowding events Factor return autocorrelation: - Crowding can create short-term momentum in factor returns (everyone piling in) - Followed by sharp reversals when the crowd exits - Look for: negative autocorrelation at 1-week lag following periods of high positive autocorrelation 3. Crowding risk indicators to monitor: - Pairwise correlation among factor-long stocks (rising = crowding) - Volatility of factor returns (rising = crowding or unwind in progress) - Trading volume in factor-long stocks (spiking = potential unwind) - Factor drawdown relative to historical distribution (severe = possible crowding unwind) 4. Historical crowding unwind events: - Quant Quake (August 2007): quantitative equity strategies suffered simultaneous drawdowns due to forced deleveraging - The unwinding was rapid (3–5 days) and not explained by macro fundamentals - August 2007 style analysis: regress this factor's returns on the quant quake period — was it affected? 5. Portfolio implications: - Position size adjustment: reduce exposure to highly crowded factors - Diversification: ensure the portfolio's factor exposures are not all correlated with the same crowded strategies - Stop-loss policy: pre-define the drawdown level at which crowding unwind is suspected and exposure is reduced Return: crowding metrics for each indicator, comparison to historical crowding events, monitoring dashboard specification, and portfolio adjustment recommendations.

Step 1: Hypothesis — state the economic or behavioral rationale for why this signal should predict returns. Write it down before looking at any return data. What would falsify this hypothesis? Step 2: Data protocol — define the asset universe with point-in-time construction, the signal computation with exact timing lags, the data sources, and the three-way train/validate/test split. Do not touch the test set. Step 3: Signal construction and training set IC — compute the signal and evaluate IC, ICIR, and quintile performance on the training set only. If IC is not promising (ICIR < 0.3), return to Step 1 before proceeding. Step 4: Parameter selection on validation set — select any free parameters (lookback windows, thresholds) on the validation set. Document the parameter search space and all results, not just the best. Step 5: Multiple testing adjustment — apply Bonferroni or BHY correction given the number of hypotheses tested in your research program. Require t-statistic > 3.0 for a new signal to be considered genuine. Step 6: Transaction cost and capacity analysis — estimate annualized turnover, total cost per unit of turnover, and net IC after costs. Estimate AUM capacity before market impact exceeds the gross alpha. Step 7: Final evaluation on test set — evaluate the fully specified signal on the test set exactly once. Report all metrics: IC, ICIR, quintile spreads, net Sharpe. Compare to the validation results — large divergence suggests overfitting. Step 8: Research report — write a complete research memo: hypothesis and rationale, data and methodology, training and validation results, multiple testing adjustments, transaction cost analysis, test set results, risks and limitations, and recommendation (implement / further research / reject).