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Data sets – Volatility-sorted portfolios

Data sets – Volatility-sorted portfolios

02-07-2019 | Data sets

This dataset file contains two volatility-sorted datasets going back to 1929.

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  • Pim  van Vliet, PhD
    Pim
    van Vliet, PhD
    Head of Conservative Equities
  • Jan de Koning
    Jan
    de Koning
    Portfolio Manager Core Quant Equities
  1. Volatility-sorted decile portfolios and the Conservative vs Speculative portfolios. The portfolios are equally weighted and based on the largest 1,000 US stocks. The conservative formula selects within the 500 lowest volatility stocks, the 100 stocks with highest net payout ratio and positive price momentum. The Conservative Formula is presented in the book 'High Returns from Low Risk: A Remarkable Stock Market Paradox’ and rigorously tested in the article ‘The Conservative Formula: Quantitative Investing made easy’.

  2. Double sorted volatility portfolios and a long-short VOL hedge factor. The VOL factor is value-weighted and based on all US stocks, similar to the Fama-French 2x3 double-sorted portfolios. The portfolio is based on the intersections of two portfolios formed on market equity and three portfolios formed on the past 36-month return volatility. The market equity breakpoint is the median NYSE market equity, and the volatility breakpoints are the 30th and the 70th NYSE percentiles.

Robeco believes that investors can profit from the low-risk effect. For clients all over the world we manage over USD 20 bln in Conservative Equities strategies since 2006. An influential study by Van Vliet and David Blitz published in the Journal of Portfolio Management in 2007 presented international evidence to show that stocks with low volatility earn high risk-adjusted returns. The low-volatility effect is perhaps the largest anomaly in finance, challenging the basic trade-off between risk and return. Yet it remains one of the least utilized factor premiums in financial markets. With this dataset we give you the opportunity to investigate this fascinating investment concept further.