A research paper from BlackRock shows that idealized zero - net - investment
factor portfolios constructed using Fama - French approach * can have much lower long - term correlations:
Not exact matches
A growing number of investors are seeking to
construct portfolios that simultaneously capture the 1) long - term
factor premia (value, momentum, size etc.) and 2) have attractive ESG profiles.
Specifically, they
construct portfolios that scale exposure to a stock
factor portfolio or a currency carry trade by the inverse of expected variance.
SUMMARY It's difficult to rationalise why there should be excess returns from high quality stocks The Quality
factor needs to be
constructed beta - neutral to achieve positive returns Exposure to the Quality
factor is an attractive hedge for an equity - centric
portfolio INTRODUCTION The concept of
How many, and which,
factors should investors include when
constructing multi-factor smart beta
portfolios?
They address how to: (1) specify the risk
factors driving returns in global financial markets; (2) estimate
factor returns and volatilities; and, (3)
construct an optimal
portfolio of
factors.
Each month for investment grade and high yield bond market segments separately, they
construct an equally - weighted long - only
portfolio consisting of the 10 % of bonds with the highest exposure to each
factor.
There was a surge in quantitative money managers, many using Barr Rosenberg's Barra Risk
Factor Analysis platform to
construct portfolios.
Specifically, they
construct portfolios that scale exposure to a stock
factor portfolio or a currency carry trade by the inverse of expected variance.
It may also be used to forecast performance, particularly for quantitative
portfolio managers who
construct risk models that include the likely
factors that influence price changes.
This lends support to arguments for using realized volatility to
construct a low volatility
factor portfolio for preferred stocks.
To
construct a low volatility
factor portfolio, it is common to select securities that had low realized volatility over a pre-specified period and hold the
portfolio for the subsequent n months.
What I've found is no matter how much careful thought goes into
constructing a low - cost
portfolio, there is no way to account for the biggest swing
factor — investor behaviour.
Their main performance metric is 7 -
factor hedge fund alpha, which corrects for seven risks proxied by: (1) S&P 500 Index excess return; (2) difference between Russell 2000 Index and S&P 500 Index returns; (3) 10 - year U.S. Treasury note (T - note) yield, adjusted for duration, minus 3 - month U.S. Treasury bill yield; (4) change in spread between Moody's BAA bond and T - note, adjusted for duration; and, (5 - 7) excess returns on straddle options
portfolios for currencies, commodities and bonds
constructed to replicate trend - following strategies in these asset classes.
IB Asset Management has undertaken research and back - testing to decide on the fundamental
factors and rules used to
construct this
portfolio.
For many years, active fund managers and institutional investors have often used a
factor - based approach either to strategically
construct portfolios or to tilt their
portfolios toward well - known risk
factors, such as low volatility, value, momentum, dividend, size, and quality, to capture the
factor risk premium.
Figure 2, Panel A, plots the historical excess return and historical volatility, and Panel B the five - year expected return and expected volatility, at year - end 2016 for a number of common
factors in the US market,
constructed as long — short
portfolios.
In the next few blogs, we will detail our approach to and back - tested results of employing credit spread (value) and volatility as
factors in order to systematically
construct a
portfolio of U.S. investment - grade corporate bonds.
below is the SIP breakdown for the Rs. 10,000 / - I invest each month (I have
constructed my
portfolio considering
factors like inception date of the fund, reputation, performance, risk - reward ratio, investment horizon 20 + years, diversification, tax benefits, overlap, industry concentration, downside protection, upside capture etc).
Factor - based hypothetical
portfolios were
constructed using the Developed Markets (ex-US) universe as defi ned by Hartford Funds, which currently covers approximately 1,500 companies across 22 countries.
As you can see,
factors are not only valuable building blocks for
constructing portfolios, but also useful tools for gauging
portfolio performance.
We illustrate the opportunities for investing in real - world
factor - based strategies by
constructing six very simple long - only investable
portfolios: value, low beta, profitability, investment, momentum, and size.
To simulate the small - value
factor in the international markets, we
construct the value
portfolio from small - cap stocks above the 70th percentile in their respective region (Japan, United Kingdom, and Europe ex UK) by book - to - market ratio, and the growth
portfolio from small - cap stocks below the 30th percentile in their respective region.
Appendix C: Smart Beta Strategy Construction Methodology The
factor - based smart beta
portfolios, except the size strategy, are
constructed from the large - cap universes only.
We
construct factor portfolios to measure and study
factor returns.
Appendix A:
Factor Portfolio Construction Methodology To
construct our
portfolios in the United States we use the universe of US stocks from the CRSP / Compustat database.
We
construct these six
factor portfolios in accordance with widely accepted academic practice.
Factor investing is a strategy for
constructing portfolios based on macroeconomic
factors (such as credit, inflation, and liquidity) and style
factors (cap - size, balance - sheet strength, value, momentum, and volatility) to improve returns while constraining risks.
Portfolios were
constructed by investing equal amounts of capital in the top decile of companies represented by each value
factor and then rebalancing monthly to equally weight the evolving constituents of the top decile.
Hartford Multifactor Low Volatility International Equity Index (LLVINX or the «Index») seeks to address risks and opportunities within developed (excluding the US) and emerging market stocks by selecting equity securities exhibiting low volatility and
constructing the
portfolio in a way that is designed to improve overall exposure to value, momentum, quality and size
factors.
(In real life, you should consider a broad range of qualitative and quantitative
factors and will probably want to
construct a more sophisticated
portfolio.)
Their Five -
Factor Model has revolutionized how
portfolios are
constructed and analyzed, and was one of the primary reasons for Eugene Fama being awarded the 2013 Nobel Prize in Economics.
Following standard practice, the authors first divide the universe into large and small stocks, and then partition the large - and small - stock subsets by
factor strategy — value, momentum, low beta, quality, and illiquidity — to
construct high - characteristic and low - characteristic
portfolios weighted by market capitalization.