Quantitative Methods
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.
Smart rebalancing rules help portfolio managers capture more of the return that is inherent in their factor strategies. Concentrating on “priority best” trading improves factor portfolio performance.
Not all low volatility strategies are created equal. Many lack the diversification and risk control needed to guard against concentration and macro risk.
There is hope for investors seeking a robust emerging market equity strategy to complement their other equity investments.
As machine learning (ML) and data science become ever more integrated into finance, which factors should we consider for our ML-driven investment models and how should we select among them?
How can quant and fundamental analysts apply LLMs like ChatGPT? How effective a “copilot” can these technologies be?
For most investment managers, ChatGPT represents the starting whistle in a tech arms race many had hoped to avoid.
William Kinlaw, Mark Kritzman, and David Turkington offer advice on a wide range of asset allocation topics, backing up their recommendations with solid quantitative analysis.