What markets can learn from statistical learning

September 5, 2015

Understanding statistical learning is critical in modern markets, even for non-quants. Statistical learning works with complex datasets to forecast returns or to estimate the impact of specific events. The choice of methods is key: they range from simple regression to complex machine learning. Simplicity can deliver superior returns if it avoids “overfitting” (gearing models excessively to specific past experiences). Success must be measured in “out-of-sample” predictive power, after a model has been selected and estimated.

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asset management Asset Purchase Program banks bond returns bond yields bubbles capital regulation central counterparty clearing China collateral commodities credit returns default ECB economic data EM equity returns euro area crisis Fed financial crisis FX FX interventions FX returns government debt government deficits herding inflation information insurances Japan leverage liquidity machine learning market turmoil price factors regulation risk management shadow banking statistical methods term structure trend following variance risk premium VIX volatility ZLB
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