This is a summary of links featured on Quantocracy on Friday, 09/04/2015. To see our most recent links, visit the Quant Mashup. Read on readers!
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Strategy Replication Nonlinear SVMs can systematically identify stocks with high and low future returns [Mintegration]Ive replicated the following academic paper from my favourite journal; Title: Nonlinear support vector machines can systematically identify stocks with high and low future returns Authors: Ramon Huerta, Fernando Corbacho, and Charles Elkan Journal: Algorithmic Finance (2013) 45-58 45, DOI 10.3233/AF-13016, IOS Press, http://algorithmicfinance.org/2-
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Economics, Mathematics, & Common Sense [Alphamaximus]Almost all calculations in finance involve using log returns rather than % returns. There are a number of reasons why log returns are preferred. Estimating beta doesnt seem like a special case. But when you go through the math, something doesnt quite add up. rS1M1Pt=?+?rM+?t=S0exp(r)=M0exp(rM)=S0+wM0 Because we want to zero out market risk, so want to s
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Benford’s Law [Factor Wave]Benford's Law states that in many naturally occurring groups of numbers, the small digits are seen disproportionately often. This is often applied to the leading digits of data but it is more general than that. This was first noticed by the astronomer Simon Newcomb (who also should be famous for an awesome beard!) in 1881 when he saw that the first pages in a library book of logarithms
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Backtesting Data Independence [John Orford]Light is the most precious resource to a photographer, everything you can do with your camera is budgeted by the amount of light available. Financial analysis is similarly constrained by the amount of data available. So more available data is always good. With Big 'O' Sharpe you can generate as much data as the data is granular. E.g. i