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A Tutorial for Factor Models and Investing Menu and widgets The volatility effect arose from empirical evidence from Blitz (see paper in link above) that stocks with low volatility earn high risk-adjusted returns.  In fact, they find that these risk-adjusted returns are higher than the market.  This work completely violates the notion set forth by traditional CAPM theory, which states that in order for an investor to be compensated with higher returns, he must as*ume more risk.  This is astounding.  They go on to prove that the volatility effect is itself a factor uncorrelated with the other factors (size, value, momentum, beta), and can prove to be an attractive investment strategy.  In this post, I aim to replicate Blitz’s methodology and obtain similar results. Blitz an*lyzes end-of-month returns from December 1985 to January 2006 on data from FTSE World Developed index (global large-cap).  To simulate size and value, they get fundamental data from Compustat, Worldscope, and Thomson Reuters.  Since I don’t have access to any of these data sources, I must try to replicate the results using free providers (Yahoo).  For size and value factors, I can use the additive over time.  Log-returns were used in all of their results. At the end of every month, Blitz constructs equally weighted decile portfolios by ranking stocks on the past 3-year volatility of weekly returns.  To obtain the value, momentum, and size factors (suggested by Fama-French), they also create decile portfolios ranked on book-to-market ratio (value), past 12-month minus 1-month total return (momentum), and free float market value (size).  Portfolios are For performance evaluation, they calculate monthly returns and look at the average, standard deviation, and Sharpe ratio.  In order to disentangle the volatility premium from other factors, they perform both a factor regression ex-post and a double-sorting methodology ex-ante.  The double sorting methodology first sorts constituents on one factor, then on the volatility factor.  It then samples equally


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Title: Factor Investing – A Tutorial for Factor Models and Could be improved
Description: Reference: http://ssrn.com/abstract=980865 The Volatility Effect Introduction The volatility effect arose from empirical evidence from Blitz (see paper in link above) that stocks with low volatility earn high risk-adjusted returns.  In fact, they find that these risk-adjusted returns are higher
H1: 10. The Volatility Anomaly – BlitzIs it informative enough?
H2: IntroductionIs it informative enough?
H3:  DataIs it informative enough?

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Comments on: 10. The Volatility Anomaly – Blitz

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About – Factor Investing

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This blog is meant for educational purposes only.  I am curious in factor investing, and wanted to share my experiences learning about it with others.  I wanted to put together a tutorial to teach others about it as well.  If you have any questions, please comment!  Thanks for visiting! Contact: jhobartkuhn at gmail dot com

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0: An Introduction to Factor Models – Factor Investing

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References: http://www.optimalam.com/blog/andrew-ang-factor-investing http://www.investopedia.com/articles/investing/112813/introduction-factor-investing.asp Introduction: Factor models attempt to decompose an et's risk/return characteristics into separate sources.  The reason for doing so is so that investors can fully understand where an et's risk and return is coming from.  In doing so, an investor might be able to diversify away the bits of the risk profile of…

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0: An Introduction to Factor Models

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Introduction:

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1: A Single Factor Model – CAPM – Factor Investing

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Single Factor Model: The single factor model is related to the Capital et Pricing Model (CAPM), which explains that investors need to be compensated for two main things: time value and risk.  The time value portion of the return is captured by a risk-free rate.  The risk of a security is captured by a risk measure…

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1: A Single Factor Model – CAPM

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Single Factor Model:

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Prerequisites:

/2-an- [censorship] essment-of-cross-sectional-tests-of-the-capm/:
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2: An essment of Cross-Sectional Tests of the CAPM – Factor Investing

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References: http://schwert.ssb.rochester.edu/f532/f532hw112.htm http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html https://github.com/hkuhn/multifactor-models.git This section will answer questions to a related homework set written by G. William Schwert, a professor of Finance and Statistics at Rochester.  The code base exists on my github in section 2:  Homework Set #1: 1a. Consider the cross-sectional regression: $latex R_{i} = gamma_{0} + gamma_{M} eta_{i,M} + epsilon_{i}$ where…

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2: An essment of Cross-Sectional Tests of the CAPM

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 Homework Set #1:

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