Quantitative Methods
This book fills a unique gap between the CFA curriculum and the growing demand to find model-driven investment management solutions.
As complex ML models become more prevalent in investment management, their tendency to overfit to specific historical conditions poses a growing risk to investment outcomes.
Navigating FX market volatility requires more than traditional analysis. Liquidity-aware models and machine learning techniques can provide an edge in detecting and forecasting abnormal returns.
Our Conversations with Frank Fabozzi, CFA series aims to bring leading experts in finance and economics into dialogue to explore critical issues shaping the industry's future.
Discover the trends and ideas shaping the future of finance with the Research and Policy Center's Top 10 List.
These research results should give investors greater confidence in the robustness of factor premiums, reinforcing their utility in crafting effective investment strategies.
Risk managers must look at market and model risk through a single lens to see the complete picture of their market-related investment and trading risks, as well as management costs, complexities, time, and regulatory requirements.
Why wouldn't you include factor strategies in your completion portfolio? A review of the two commonly used approaches for managing institutional assets highlights their symbiotic nature.
Shorter settlement periods are meant to protect market participants. But the new T+1 settlement cycle for US equities may have undesirable knock-on effects for financial market participants around the world, including index fund managers.
Despite some current limitations, AI stands to offer significant advantages versus traditional approaches to identifying and predicting financial crises.