Institute of Advanced Investment Management

Testing an AI-Assisted Research Workflow for Multi-Asset Pullback Strategy Discovery

Soňa Beluská, Quantpedia

This study develops a systematic short-term reversal strategy, capturing the tendency of an asset to partially retrace after a brief run of down days, across six liquid ETFs spanning equities, fixed income, currencies, gold, and commodities from 2006 to 2025. It pairs a 200-day moving-average trend filter with a multi-day pullback trigger, volatility-adjusted sizing, and equal-weighted allocation. Uniquely, nearly all of the analysis was performed using ChatGPT and Claude, so the paper doubles as a test of how useful those tools are for real quantitative research.

The best specification (200-day MA trend filter, 2-day pullback, 1-day hold) delivered a Sharpe of ~0.95, nearly double the best passive benchmark, despite being invested less than half the time. The mean-reversion edge is concentrated almost entirely in the first day after entry.

A more conservative 3-day pullback version is recommended for institutional use: comparable returns to buy-and-hold but with a drawdown roughly 2.4x smaller (-13.69% vs -33.28%) and a best-in-class Calmar ratio, while deploying only ~21% of capital.

Sub-period tests show no evidence of alpha decay, the most recent window (2021–2025) was the strongest on record (Sharpe 1.25), and win rates stayed above 56% throughout.

On the AI comparison: Claude better understood the research intent and produced more coherent written analysis and article drafts, while ChatGPT over-optimized and produced nicer-looking graphs. Both reached the same strategy results, but the authors stress that precise instructions and a final independent human check remain essential.

Link to full article: https://quantpedia.com/testing-an-ai-assisted-research-workflow-for-multi-asset-pullback-strategy-discovery/

In:

, , ,

Other news

  • Boundaries of Time Series Momentum

    Matti Suominen and Erik Hjalmarsson Time-series momentum is one of the most reliable and heavily backtested anomalies in quantitative finance, serving as a foundational alpha source for managed futures and trend-following strategies. This paper uncovers a structural vulnerability that every practitioner must account for: momentum returns persist reliably during normal business cycles, but break down…

  • Do LLM “Crowds” Produce Investment Signals? An Empirical Test

    Steven Edwards This paper tests whether aggregating stock selections from a large, philosophically diverse ensemble of LLM personas can produce genuine investment signals beyond passive benchmark exposure. The author built 100 distinct investor pe rsonas spanning value, momentum, growth, ESG, quantitative, and contrarian philosophies, each independently selecting fifty US-listed equities. The picks were backtested across…

  • Testing an AI-Assisted Research Workflow for Multi-Asset Pullback Strategy Discovery

    This study builds a short-term mean-reversion strategy across six liquid ETFs spanning equities, fixed income, currencies, gold, and commodities (2006–2025), using a 200-day trend filter and a multi-day pullback trigger. Beyond the strategy itself — which delivers strong risk-adjusted returns while invested only ~21% of the time — the paper tests ChatGPT and Claude as…