Machine Learning Takes Aim at Black-Scholes

Quants are embracing the idea of ‘model-free’ pricing and deep hedging.

In 2008, a team of quants at JP Morgan set out to automate the hedging of one of the firm’s derivatives portfolios. The effort was quickly abandoned. The hedging strategy—which relied on computing risk sensitivities, known as Greeks—required constant manual adjustments to account for transaction costs and other market frictions, which are not captured in classical quantitative models.

Some years later, they tried again, this time using machine learning. The new system eschews conventional

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Cutting through the hype surrounding the FDTA rulemaking process

A bill requiring US regulators and institutions to adopt a machine-readable data framework for reporting purposes applies to entity identifiers, but not security identifiers, in a crucial difference, writes Scott Preiss, SVP and global head of Cusip Global Services.

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