Singapore FinTech Association

Recorded Webinar: Navigating the Build vs Buy Dilemma: Cloud Strategies for Accelerating Quantitative Research

For many quantitative trading firms and asset managers, building a self-provisioned historical market data environment remains one of the most time-consuming and resource-intensive steps in establishing a new research capability. Sourcing data, normalising symbologies, handling corporate actions and maintaining infrastructure can take months and absorb significant budget before a single model is tested. At the...

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Testing an Assumption: Do AI Signals Really Decay?

Alpha decay is one of the foundational assumptions in quantitative finance. The empirical literature, beginning with McLean and Pontiff’s 2016 study of 97 anomalies and replicated and refined across multiple subsequent studies, has repeatedly found that returns degrade out-of-sample and degrade further once published. The assumption sits inside almost every institutional model risk framework as...

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TEST Event page 2

Now in its 15th year the TradingTech Summit London brings together the European trading technology capital markets industry and examines the latest changes and innovations in trading technology and explores how technology is being deployed to create an edge in sell side and buy side capital markets financial institutions.

AI in Capital Markets Handbook 2026

AI adoption in capital markets has moved into a more disciplined phase. The priority is now controlled deployment: where AI can be used safely, where it can deliver measurable value, and how outputs can be governed, monitored and evidenced. The 2026 edition of the AI in Capital Markets Handbook examines how AI is being applied...