The Death of Diversification Has Been GreatlyExaggerated

Ilmanen, Antti; Kizer, Jared

doi:10.3905/jpm.2012.38.3.015

Cited by 95 publications

(59 citation statements)

References 22 publications

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“…Blitz (2012) argued that investing in factors should be a strategic decision because of the long-term investment horizon required to harvest the premiums. Bender, Briand, Nielsen, and Stefek (2010) and Ilmanen and Kizer (2012) also made the case for strategic allocations to factors, stressing the diversification benefits. Ang (2014) devoted an entire book to factor investing.…”

mentioning

confidence: 99%

Factor Investing in the Corporate Bond Market

Houweling¹,

ZundertJeroen²

2017

Financial Analysts Journal

121

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This article examines the performance of size, low-risk, value, and momentum factor portfolios in the corporate bond market. A factor portfolio is constructed by sorting bonds on a specific characteristic: Size contains bonds of small companies, based on the market value of their outstanding bonds; low risk contains short-maturity bonds with a high credit rating; value selects bonds whose credit spread is high relative to a model-implied fair spread; and momentum consists of bonds with high past returns. In addition to these individual factors, in our study we analyzed a multi-factor portfolio that combines the four factors. We found that both single-factor and multi-factor portfolios generate economically meaningful and statistically significant alphas.Our study belongs to the empirical asset pricing literature 1 documenting that factor portfolios carry a premium beyond the traditional asset class premium, as postulated by the CAPM. Even though this literature has existed for decades, it has focused predominantly on equities. The best-documented factors in the equity literature are low risk (starting with Haugen and Heins 1972), value (Basu 1977), size (Banz 1981), and momentum (Jegadeesh and Titman 1993). For corporate bonds, the evidence is both more limited and more recent. The documented factors are low risk (e.g., Ilmanen, Byrne, Gunasekera, and Minikin 2004; Frazzini and Pedersen 2014) and momentum (e.g., Pospisil and Zhang 2010; Jostova, Nikolova, Philipov, and Stahel 2013). Evidence on other factors is scarce. We are aware of only two papers on value (L'Hoir and Boulhabel 2010;Correia, Richardson, and Tuna 2012) and none on size. The existing studies on factors in the corporate bond market each focus on one particular factor, whereas in our study, we analyzed the size, low-risk, value, and momentum factors using a consistent methodology with a single dataset. OurWe offer empirical evidence that size, low-risk, value, and momentum factor portfolios generate economically meaningful and statistically significant alphas in the corporate bond market. Because the correlations between the single-factor portfolios are low, a combined multifactor portfolio benefits from diversification among the factors: It has a lower tracking error and a higher information ratio than the individual factors. Our results are robust to transaction costs, alternative factor definitions, alternative portfolio construction settings, and constructing factor portfolios on a subsample of liquid bonds. Finally, allocating to corporate bond factors provides added value beyond allocating to equity factors in a multi-asset context.

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mentioning

confidence: 99%

Factor Investing in the Corporate Bond Market

Houweling¹,

ZundertJeroen²

2017

Financial Analysts Journal

121

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“…Indeed, the notion of asset class has been losing its relevance in investment management since the financial crisis of 2008, when existing asset class-based allocations failed to provide diversification (e.g., Ilmanen and Kizer 2012).…”

Section: Testing Asset Classes or Factors?mentioning

confidence: 99%

“…We follow Bender et al (2010), Ilmanen and Kizer (2012), and Bird et al (2013) to build market, size, value, term, and default factors. The price and total return summary statistics for the factor portfolios are described in Table 14.…”

Section: Global Factor-based Reference Portfoliomentioning

confidence: 99%

Searching for a listed infrastructure asset class using mean–variance spanning

Blanc-Brude¹,

Whittaker²,

Wilde

2017

Financ Mark Portf Manag

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This study examines the portfolio-diversification benefits of listed infrastructure stocks. We employ three different definitions of listed infrastructure and tests of mean-variance spanning. The evidence shows that viewing infrastructure as an asset class is misguided. We employ different schemes of infrastructure asset selection (both traditional asset classes and factor exposures) and discover that they do not provide portfolio-diversification benefits to existing asset allocation choices. We also find that defining and selecting infrastructure investments by business model as opposed to industrial sectors can reveal a very different investment profile, albeit one that improves the mean-variance efficient frontier since the global financial crisis. This study provides new insights into defining and benchmarking infrastructure equity investments in general, as well as into the extent to which public markets can be used to proxy the risk-adjusted performance of privately held infrastructure investments.

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“…The concept of constant-volatility (also known as volatility-targeting or volatility-timing) has been first highlighted by Fleming et al (2001Fleming et al ( , 2003 and more recently by Kirby and Ostdiek (2012), Ilmanen and Kizer (2012) and Hallerbach (2012). This series of papers documents that volatility-timing can result in desirable properties for the portfolio like lower turnover and larger Sharpe ratio.…”

Section: Constant-volatility and Time-series Momentum Strategiesmentioning

confidence: 99%

“…Fleming, Kirby and Ostdiek (2001), Ilmanen and Kizer (2012), Kirby and Ostdiek (2012) and Hallerbach (2012) highlight the benefits of volatility-timing, while Barroso and Santa-Clara (2015) and Daniel and Moskowitz (2015) examine its effect on the performance of cross-sectional equity momentum strategies. Fleming et al (2003) investigate the performance and turnover benefits for a mean-variance portfolio from using more efficient estimates of volatility.…”

Section: Introductionmentioning

confidence: 99%

Improving Time-Series Momentum Strategies: The Role of Trading Signals and Volatility Estimators

Baltas

Kosowski

2012

SSRN Journal

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Motivated by studies of the impact of frictions on asset prices, we examine the effect of key components of time-series momentum strategies on their turnover and performance from 1974 until 2013. We show that more efficient volatility estimation and price trend detection significantly reduce portfolio turnover and therefore rebalancing costs. The poor performance of time-series momentum strategies during the post-2008 period is explained by an increased level of pairwise correlations. We propose a novel correlation-based leverage-adjustment to the strategy's weighting scheme and show that it improves performance by safeguarding against tail risk, even after accounting for realistic transaction costs. JEL CLASSIFICATION CODES: E37, G11, G15, F37

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The Death of Diversification Has Been GreatlyExaggerated

Cited by 95 publications

References 22 publications

Factor Investing in the Corporate Bond Market

Factor Investing in the Corporate Bond Market

Searching for a listed infrastructure asset class using mean–variance spanning

Improving Time-Series Momentum Strategies: The Role of Trading Signals and Volatility Estimators

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