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Erschienen in:
Introduction to Asset Allocation Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

6. Factor Investing I

verfasst von : Henrik Lumholdt

Erschienen in: Strategic and Tactical Asset Allocation

Verlag: Springer International Publishing

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Abstract

So far, we have discussed investments from an asset class point of view. In this and the following chapter, we will zoom in on certain characteristics of segments of asset classes which have been shown to earn excess returns. This is the topic of factor investing. This type of analysis is not new. But the idea of making such factors directly investable through specialized vehicles is a relatively recent innovation which is causing a minor revolution in the asset management industry. The chapter is structured as follows:

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Vorheriges Kapitel Optimizing the Strategic Asset Allocation
Nächstes Kapitel Factor Investing II
Fußnoten
1
Indeed, one might go as far as to state that style features are all that matter to bonds. Conceptually, as bonds become shorter and their credit risk is reduced, they converge on T-bills which are generally perceived as the closest thing to a risk-free asset available. The style features of bonds therefore arguably explain all of their risk premium, unlike in the case for stocks.
 
2
The GPFG was established in 1990 to manage the wealth accruing from the country’s petroleum exploration and production. The mandate of the Fund is to preserve the wealth for future generations and, apart from transfers to the Norwegian state, targeted at around 4% of its annualized real returns, it is managed independently of the government budget. The benchmark for the long-term management of the GPFG is decided by the Ministry of Finance and after reforms in 1998 generally implies a 60/40 percent allocation to stocks and bonds. Its active management is undertaken by Norges Bank Investment Management (NBIM), an arm of the central bank of the country.
 
3
See also the Appendix to the book.
 
4
This result follows from linear algebra. Two vectors are orthogonal, or perpendicular, if their scalar product is zero. If the vector of weights is orthogonal to a vector of 1’s (6.6), to the vector of sensitivities (6.7) and to the vector of expected returns (6.8), then the vector of expected returns will be a linear combination of the vector of 1’s and the vector of sensitivities. See further Ross (1976).
 
5
See further, for example, Cutler et al. (1989), Chen (1991), Cheung and Ng (1998) and Flannery and Protopapadakis (2002).
 
6
Graham and Dodd: Security Analysis, McGraw-Hill. The first edition is from 1934, but there are many later editions.
 
7
See, for example, Dichev (1998), Horowitz et al. (2000), Chan et al. (2000), Amihud (2002), Schwert (2003), Van Dijk (2011) and Dimson et al. (2011).
 
8
See, for example, Chan and Tong (2000), Hameed and Kusnadi (2002), Forner and Marhuenda (2003), Glaser and Weber (2003), Hon and Tonks (2003) and Muga and Santamaria (2007).
 
9
This is consistent with the findings in such studies as, for example, Fama and Bliss (1987) and Campbell and Shiller (1991) which also rejected the expectations hypothesis.
 
10
See, for example, Vazza and Kraemer (2017), Fons (1987) and Altman (1989).
 
11
See, for example, Houweling et al. (2005), Driessen (2005), Chen et al. (2007), Lin et al. (2011), Dick-Nielsen et al. (2012) and Acharya et al. (2013).
 
12
This was done using linear programming where the objective is to maximize the multifactor portfolios exposures to the four factors subject to a series of constraints. The resulting portfolios are therefore different from a simple weighted average of the individual factor portfolios. See further Israel and Ross (2017).
 
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Metadaten
Titel
Factor Investing I
verfasst von
Henrik Lumholdt
Copyright-Jahr
2018
Buch
Strategic and Tactical Asset Allocation

Print ISBN: 978-3-319-89553-6

Electronic ISBN: 978-3-319-89554-3

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-89554-3_6