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Posted By: Advisor Perspectives (http://www.valuewalk.com/author/advisorperspectives/)Posted date: August 18, 2015 03:20:48 PMIn: Top Stories (http://www.valuewalk.com/category/top-stories/)No Comments (http://www.valuewalk.com/2015/08/why-you-shouldnt-trust-most-financial-research-3/#comments)

Why You Shouldn’t Trust Most FinancialResearch

Why You Shouldn't Trust Most Financial ResearchAugust 18, 2015

by Michael Edesess and Kwok L. Tsui

PDF (http://www.advisorperspectives.com/articles/2015/08/18/whyyou

shouldnttrustmostfinancialresearch.pdf) | Page 2

(http://www.advisorperspectives.com/articles/2015/08/18/whyyou

shouldnttrustmostfinancialresearch/2)

A paper (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2249314) by

Campbell R. Harvey, Yan Liu, and Heqing Zhu with the unexciting title “…and the

CrossSection of Expected Returns” has been drawing a lot of attention recently. This

is not only because of the paper’s startling revelation that at least 315 “factors” of

investment returns have been discovered through multiple regressions and published

in hundreds of articles and working papers. That phenomenon was already

characterized in a 2011 paper (http://www.nber.org/papers/w16972) by John H.

Cochrane and called a “zoo” of factors. It is also because Harvey et al.’s work sounds

the same alarm for finance research that was sounded in 2005 for medical research in

a famous article by John P. A. Ioannidis, “Why Most Published Research Findings Are

False (http://journals.plos.org/plosmedicine/article?

id=10.1371/journal.pmed.0020124).”

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Harvey et al. claim that “most claimed research findings in financial economics are

likely false.” I’ll explain how they arrived at that conclusion by looking at the ongoing

search for factors that influence investment returns. Let’s begin by understanding the

statistical tests that are used to determine whether or not a purported factor is the

result of random “luck.”

What are factors?

A factor is any item of data that can be put into correspondence with the rates of

return on a security or a portfolio of securities.

Let’s call that item of data x, and let’s call the monthly return on the security or

securities r. Imagine two columns of numbers: the values of x and the values of r.

Each value of x, the factor, corresponds to a value of r, a monthly rate of return. The

value of x and the value of r might both be for the same month or for the same

security. Or x and r might be for different months.

A factor could be a macroeconomic variable,

like monthly inflation, interest rates or

unemployment. In that case, a monthly

return on the portfolio or security could be

put into a correspondence with, say, the value

of the macroeconomic variable in the

preceding month. Imagine a column of

monthly returns on a portfolio next to a

column of unemployment rates in the

previous month.

Alternatively, a factor could be a feature of the corporate security itself or portfolio of

securities. For example, a factor could be the size of the corporation, the corporation’s

price/earnings ratio or book value/market value ratio. Imagine a column of rates of

return on all stocks in a given month next to a column of the price/earnings ratio of

the company issuing the stock.

Whatever the factor is, the idea is to see if it bears a numerical relation to the

corresponding rates of return when the two variables are lined up that way. A

correlation coefficient would be one way to see if the data exhibits a numerical

relationship. An equivalent method – one that has become standard – is to run a

regression of the rates of return on the “factor” variable.

A regression is what is known to high school students – at least it was in my high

school – as a leastsquares fit. It results in a line with a slope and a yintercept (Figure

1).

Figure 1.

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The slope, often labeled with a “beta,” is supposed to represent the relationship. If the

beta is zero, there is no apparent relationship (at least not a linear one). If beta is large

– either a large positive or a large negative slope – there is a relationship.

PDF (http://www.advisorperspectives.com/articles/2015/08/18/whyyou

shouldnttrustmostfinancialresearch.pdf) | Page 2

(http://www.advisorperspectives.com/articles/2015/08/18/whyyou

shouldnttrustmostfinancialresearch/2)

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