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P2.T8.707. The biases of illiquid markets (Ang)

Nicole Seaman

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Learning objectives: Evaluate the characteristics of illiquid markets. Examine the relationship between market imperfections and illiquidity. Assess the impact of biases on reported returns for illiquid assets. Describe the unsmoothing of returns and its properties.


707.1. According to Andrew Ang, illiquidity can arise due to the following market imperfections: Clientèle effects and participation costs, transaction costs, search frictions, asymmetric information, price impact or funding constraints. He characterizes the effects of these imperfections as "illiquidity." In regard to the CHARACTERISTICS of illiquid markets, based on Ang's research, which of the following statements is TRUE (such that the other statements are generally false)?

a. Normally liquid markets periodically become illiquid
b. Most individuals hold the majority of their wealth in liquid or highly liquid assets
c. Most asset classes are liquid such that genuinely illiquid markets tend to be small and temporary
d. Technology has virtually eliminated the following frictions: transaction costs, search friction, asymmetric information, price impact, and funding constraints

707.2. Andrew Ang makes an important, provocative statement when he writes "Reported illiquid asset returns are not returns." He claims that people overstate the expected returns and understate the risk of illiquid assets, and he attributes this to three key biases. According to Ang, each of the following is a bias that overstates the expected returns (and/or understates the risk) of illiquid assets EXCEPT which is not accurate?

a. Survivorship bias can inflate returns by 4.0% or more
b. Infrequent sampling (aka, infrequent trading) artificially reduces risk and risk-related metrics such as volatility, correlation and beta
c. Turnover bias decreases the typical time between transactions and tends to artificially increase the expected return by 5.0% or more
d. Selection bias (aka, reporting bias) is a distortion of the sample that artificially increases (ie, overestimates) alpha and artificially decreases (ie, underestimates) beta

707.3. To adjust the infrequent trading bias introduced that is introduced into reported returns, we can "unsmooth" or "de-smooth" the reported returns. Ang suggests this is a filtering problem: "Filtering algorithms are normally used to separate signals from noise. When we’re driving on a freeway and talking on a cell phone, our phone call encounters interference—from highway overpasses and tall buildings—or the reception becomes patchy when we pass through an area without enough cell phone towers. Telecommunication engineers use clever algorithms to enhance the signal, which carries our voice, against all the static. The full transmission contains both the signal and noise, and so the true signal is less volatile than the full transmission. Thus standard filtering problems are designed to remove noise. The key difference is that unsmoothing adds noise back to the reported returns to uncover the true returns."

The essence of unsmoothing of returns is illustrated by Ang's formulas 13.1, 13.2 and 13.4 below:

In these formulas r*(t) is the reported (aka, observed) return and r(t) is the true but unobserved return. Importantly, as is almost always the case in finance, the model used in this particular unsmoothing process makes key assumptions. However, if the assumptions are correct, then each of the following statements about the unsmoothing process is true EXCEPT which is false?

a. Unsmoothing affects only risk estimates and not expected returns
b. Unsmoothing has no effect if the observed returns are uncorrelated.
c. The true returns implied by the "transfer function" and equation 13.2, r(t), should have zero autocorrelation and generally should not be themselves forecastable
d. Due to the autocorrelation assumption, |φ| <1, the variance of the true returns will be less than the variance of the observed returns; i.e., variance[r(t)] < i.e., variance[r*(t)]

Answers here:
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Some input here from my side (referring to question 707.3) for prospective Part II candidates without going into further detail about the theory. In November 2016 GARP tested the concept of unsmoothing returns in detail (answers 'a' and 'b' to questions 707.3 have been very similar as given above!). Just make sure that you prepare this properly. It is highly likely to show up in one of the 2017 exams again I suppose.

David Harper CFA FRM

David Harper CFA FRM
Staff member
@emilioalzamora1 Thank you for this feedback :), I didn't realize nor quite would have expected GARP to test this yet (The Ang readings are new, usually it takes a year or so for new readings to "season"). Ang's book is really special, it is well-written, it has great sources, and it's already impacted my own thinking about investing.

I'm also glad to hear this because it (at least to a degree) validates our approach of writing practice questions against the LOs in a sequential manner. This is so time-consuming, but otherwise I wouldn't have written any questions about "un-smoothing" (because, to tell the truth, while I have quite a bit of time series modeling practice, including a bit of filtering, I had not encountered the Geltner-Ross-Zisler queried above, so like so many FRM concepts, I myself am learning about the concept by way of the act of writing the question and then, thankfully, also getting some forum conversation follow-up). It's just really good to know that GARP is following up on (at least some of) these fresh LOs. Thanks!
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Brilliant stuff, David! These exchanges make the forum and interaction with you so unique!

(By the way, this smoothting/unsmoothing topic has been briefly touched on in the after-exam Part II post Nov 2016 created by Nicole as well.)

Andrew Ang is definitely the! representative and most experienced guy out there when it comes to factor investing (beyond W. Buffett). Unsurprisingly he has jumped ship from academia and was hired by Black Rock two years ago. Excellent guy.

Therefore, it is not really surprising to me if GARP tests this topic here and there!

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David Harper CFA FRM

David Harper CFA FRM
Staff member
@emilioalzamora1 Awesome, thank you for your enthusiasm, it's infectious :) (thanks for pointing out it's also mentioned in Nicole's note)
Thank you for Andrew Ang's link at blackrock. I take the blackrock blog (for the week in risk) but I totally missed that he's there. Now I see why you say it's not surprising!!