This is a summary of links featured on Quantocracy on Thursday, 06/28/2018. To see our most recent links, visit the Quant Mashup. Read on readers!
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Julia Build any time resolution using 1 minute data [Flare 9x]Reliable data makes for more accurate models. It is not the end of the world if there are minor discrepancies although data does need to be representative to build models and make good assumptions. Common data errors are known to be found at market closing times. We want the auction price not the last price. Last price might be some fluff trade with 1 lot. We want the real close or the auction
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Excerpt, Part I: Quantitative Investment Portfolio Analytics In R [Capital Spectator]Heres an excerpt from my new book, Quantitative Investment Portfolio Analytics In R: An Introduction To R For Modeling Portfolio Risk and Return, which was published last week. In this two-part excerpt of Chapter 5, well look at a basic procedure for downloading factor premia from Professor Ken Frenchs web site to run a simple factor analysis using R code. Ill publish the second half
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Explaining the Beta Anomaly [Alpha Architect]The superior performance of low-beta and low-volatility stocks was documented in the literature back in the 1970s by Fischer Black (in 1972) among others even before the size and value premiums were discovered. The low-beta/low-volatility anomaly has been demonstrated to exist in equity markets around the globe. Ive already written about the low-volatility (i.e., low-risk or
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Bootstrapping time series data [Quant Dare]For those of us working with time series, the autocorrelation function (ACF) is a fundamental tool to understand how the values in a series correlate with others certain distance away. Indeed, we could even say that autocorrelation plots (a.k.a correlogram) are probably the most common visualizations in econometrics and time series analysis. This is why functions to compute and plot the ACF are