The importance of statistical programming for investment managers

November 1, 2016

Almost every portfolio manager uses some form of quantitative analysis. Most still rely on Excel spreadsheets, but this popular tool constrains the creativity of analysis and struggles to cope with large data sets. Statistical programming in R and Python both facilitates and widens the scope of analysis. In particular, it allows using high-frequency data, alternative data sets, textual information and machine learning. And it greatly enhances the display and presentation of analytical findings.

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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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