Institute of Advanced Investment ManagementInstitute of Advanced Investment Management
Recent Research
IAIM is excited to share the insights from our latest research, which provides a comprehensive overview of the trends and challenges facing the finance industry. Our report draws on data from a range of sources, including surveys of investment professionals, market analysis, and academic research, to provide a detailed analysis of the key drivers shaping the industry. The findings highlight the growing importance of technology and digital innovation. We believe in the need for continued innovation and collaboration in the face of ongoing disruption. We believe that this research provides valuable insights for investors, asset managers, and other stakeholders, and we look forward to sharing these insights with the wider community.
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 […]
Read more: Boundaries of Time Series MomentumDo 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 […]
Read more: Do LLM “Crowds” Produce Investment Signals? An Empirical TestTesting 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.
Read more: Testing an AI-Assisted Research Workflow for Multi-Asset Pullback Strategy DiscoveryGuardrails 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.
Read more: Guardrails Make the Researcher: What an AI Agent Got Right (And Wrong) Replicating Nine Equity AnomaliesHow 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.
Read more: How Wise is the Crowd? Bias and Edge in Prediction MarketsReconstructing 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.
Read more: Reconstructing a Century of U.S. Corporate Bonds