Institute of Advanced Investment Management
Recent Investing Research
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 research assistants, finding AI can meaningfully speed up quant work when instructions are precise and results are independently verified.
Guardrails Make the Researcher: What an AI Agent Got Right (And Wrong) Replicating Nine Equity Anomalies
An autonomous AI research agent was tasked with replicating nine published U.S. equity anomalies on clean, survivorship-free data. On a faithful build, none survive out-of-sample — and the lone apparent survivor turned out to be the agent’s own construction error. The real lesson is that an AI researcher is only as trustworthy as the guardrails that catch its mistakes.
How Wise is the Crowd? Bias and Edge in Prediction Markets
Using tick-level data from Polymarket and Kalshi, this study finds the classic favorite-longshot bias is largely a ‘Yes Bias,’ that the most capitalized traders systematically underperform smaller ones, and that the most vocal participants add no informational edge. It offers a reproducible method for denoising prediction-market probabilities.
Reconstructing a Century of U.S. Corporate Bonds
A new 128-year database of U.S. corporate bonds (1895–2022) — over 100,000 bonds and 7 million observations — uncovers a sizable, statistically significant credit risk premium that shorter modern samples fail to detect. The findings support strategic allocations to corporate credit for long-horizon investors and establish a new benchmark for empirical asset pricing in fixed income.