Household Investments in Structured

Financial Products:Neglected Risks, Price Complexity, and Financial Literacy∗

Eric C. Chang†

Dragon Yongjun Tang‡

Miao (Ben) Zhang§

Mar 14, 2011

Abstract

Retail structured financial products provide a challenge for the economics offinancial innovation. Many products are overpriced and some even could havenegative expected return (e.g., Henderson and Pearson (2011)). We use householdportfolio holding data from Hong Kong to understand such puzzling investmentsin structured financial products. Neglected risk has strong explanatory powerfor household asset allocation in structured products. Allocation is lower whenthe investment is made more prudently with fully documented risk assessment.Investors allocation is related to product complexity. The effect of neglected riskon structured product investment is weaker for more financially literate investors.

∗We thank John Beshears, John Griffin, Bing Han, Gerard Hoberg, Alok Kumar, Yu-Jane Liu,William Goetzmann, Neil Pearson, Avanidhar Subrahmanyam, Wing Suen, Chun Xia, Hong Yan, ChuZhang, Hao Zhou, Ning Zhu for helpful discussions and useful comments. We thank seminar partici-pants at Singapore Management University, Peking University, University of Hong Kong, Hong KongPolytechnic University, University of Texas at Austin, SFM Conference at National Sun Yat-Sen Uni-versity, Emerging Market Finance Conference at Tsinghua University, and 2010 Financial ManagementAssociation Annual Meeting. Support from the Centre for Financial Innovation and Risk Managementand Asia Case Research Centre of the University of Hong Kong is acknowledged.†School of Economics and Finance, University of Hong Kong. Phone: (+852) 28578347. Email:

ecchang@business.hku.hk‡School of Economics and Finance, University of Hong Kong. Phone: (+852) 22194321. Email:

yjtang@hku.hk§McCombs School of Business, The University of Texas at Austin. Phone: (+1) 512-471-1674.

Email: miaozhang@mail.utexas.edu

Household Investments in Structured Financial

Products:Neglected Risks, Price Complexity, and Financial Literacy

Abstract

Retail structured financial products provide a challenge for the economics of

financial innovation. Many products are overpriced and some even could have

negative expected return (e.g., Henderson and Pearson (2011)). We use household

portfolio holding data from Hong Kong to understand such puzzling investments

in structured financial products. Neglected risk has strong explanatory power

for household asset allocation in structured products. Allocation is lower when

the investment is made more prudently with fully documented risk assessment.

Investors allocation is related to product complexity. The effect of neglected risk

on structured product investment is weaker for more financially literate investors.

JEL Classification: D81; G11 Keywords: Structured Financial Products; Household

Finance; Neglected Risks; Financial Literacy.

1 Introduction

Limited financial market participation is widely documented and challenges classic port-

folio allocation theories. Financial innovations aim to improve investors choice set. How-

ever, most financial products are not well received by investors at initial stage (Lerner

and Tufano (2011)). Households usually only include a small number of simple se-

curities in their portfolios and many do not invest in financial market at all. Retail

structured products, the latest financial innovation, are much more successful than their

predecessors in finding their way to household portfolios. Structured products were first

introduced in 2001, annual global issuance reached $400 billion by 2007.1 We use a

unique household portfolio allocation data to understand why individual investors are

so fond of structured products.

Household investment in structured products is puzzling from the perspectives of

typical investor preference and risk-return tradeoffs. First, evidence from U.S., U.K.,

Germany, Swiss, among others, shows that structured products are massively over-

priced (Henderson and Pearson (2011), Bergstresser (2009), and Wilkens and Stoimenov

(2007)). Investors would long such structured products with negative risk-adjusted re-

turn only if there is hedging benefits. However, structured product returns are positively

correlated with market return hence cannot be a hedge to typical individual investors.

Second, structured products are financial innovations with little historical performance

data and much ambiguity. Ambiguity-averse investors would avoid such investments.

Third, structured products have capped returns but substantial downside due to default

risk. Such feature does not match investors’ preference for positive skewness (Barberis

and Huang (2008), Kumar (2009)).

Structured products are derivative securities issued by financial companies, mostly

structured investment vehicles (SIVs). Their payoffs are linked to stock price or credit

quality of reference entities. The institutional part of the structured product market

consists of collateralized debt obligations (CDOs) which played a prominent role in the

credit crisis. Retail structured products are sold over-the-counter to individuals through

commercial banks and investment companies. There is no secondary market for trading

of structured retail products. Holders do not have updated valuation prior to maturity

date, unless the product is in early redemption due to default or knock-out events. Such

1Data is provided by www.structuredretailproducts.com. New issuance is substantially reducedduring the credit crisis. Bergstresser (2009) presents a detailed description of the retail market forstructured products. Even in the emerging market of China, ordinary individual investors buy retailstructured products although many of them do not own stocks (Fitch (2010)).

1

features (i.e., illiquidity, fixed maturity, bank transactions) are similar to certificates of

deposits (CD) except slightly higher interest rate for structured products.

One potential explanation for individual investors’ seemingly suboptimal investments

in structured products is that investors misunderstood them. For example, buyers might

have mistaken structured products as bank notes. Household investment mistakes have

been previously documented by, among others, Calvet, Campbell, and Sodini (2007).

Given that individual investors with bounded rationality make suboptimal investment

decisions, it is important to understand the root behavior leading to such decisions.

We conjecture that retail structured product buyers might be influenced by “normalcy

bias” or the tendency to neglect bad states which have not occurred previously. This

“neglected risks” premise is formally incorporated into a model of financial innovation

by Gennaioli, Shleifer, and Vishny (2011).

Empirical examination of investments in retail structured products is limited by data

availability. Obtaining transaction information from brokers or banks is difficult for con-

fidentiality reasons. The collapse of Lehman Brothers in September 2008 provides a rare

opportunity for data collection. From 2003 to 2008, Lehman Brothers underwrote a

series of “minibonds”, formally known as credit-linked notes (CLNs), and distributed

through commercial banks in Hong Kong and Singapore (some investors from other

places such as Taiwan and Mainland China also purchased through agents). Lehman

Brothers also served as swap counterparties for those minibonds. The bankruptcy of

Lehman Brothers triggered a credit event for minibonds and other structured products,

including “constellation” notes underwritten by Development Bank of Singapore (DBS)

and some privately placed equity-linked notes (ELNs). Individual structured product

investors in Hong Kong identified themselves and formed a group to handle their invest-

ment losses. We conducted individual interviews with those investors over the period

between January and June 2009. Detailed demographic and financial data were compiled

for our empirical analysis.

The data provides strong support to the view of “neglected risk” for structured

product investment. Bank regulation requires comprehensive assessment of risk profile

before individual investors can purchase structured products, and high risk products

cannot be sold to low risk-absorbing individuals. However, no risk assessment was

documented for 53.6% of our subjects when they signed the purchase agreement. Those

“no-doc” buyers invest 9-10% more of their financial wealth into structured products

than buyers with full documentation. We use two alternative measures for neglected

risk and find similar results.

2

Many investors claim that they were misled by distributing banks’ salespeople. Mis-

selling is conceivable given the commission-driven incentives (Inderst and Ottaviani

(2009)). The feasibility of mis-selling is facilitated by price complexity. Structured

products have highly complex payoff terms and conditions. Such complexity can be

a strategic choice in security design to exploit consumer surplus as shown by Carlin

(2009). We construct price complexity measures and find much higher investments in

more complex products. Therefore, product suppliers could have had predatory motives

to take advantage of buyers’ negligence.

One would expect investors to be cautious and prudent when disbursing their capi-

tal. Sophisticated investors are unlikely to forgo risk assessment (or sign a back-dated

or forged assessment) when investing in complex products. One driving force for such

puzzling act is financial literacy. Prior research has shown that financial literacy is re-

lated to under participation in stock market (van Rooij, Lusardi, and Alessie (2011))

and buying high-fee index funds (Choi, Laibson, and Madrian (2010)). We follow prior

studies and use cognitive ability and schooling to proxy for financial literacy. We also

design a new financial literacy measure using subject’s stock market return expectations.

Our empirical results are consistent across all three financial literacy measures: finan-

cially illiterate investors buy 7-10% more structured products. Moreover, the effect of

neglected risk only appears in the group of financially illiterate investors.

Our findings suggest that financial literacy plays a dual role in household finance.

First, higher financial literacy leads to better investment choice. Second, financially

literate investors are also less likely to be affected by behavioral bias. Our results contrast

to Cole, Sampson, and Zia (2011) who argue that financial literacy by itself has limited

effect for better investment outcomes. However, we do not claim that financial literacy

can substitute for incentives and supports, as we do not observe changes in financial

literacy for our subjects.

We make two contributions to the burgeoning literature on household finance. First,

we are the first to use actual portfolio holding to examine investments in novel financial

products. Our emerging market evidence complements prior studies using U.S. or Europe

data. Second, this is the first study to directly test and support theoretical models on

“neglected risk” and “price complexity”. We emphasize the role of financial literacy.

Consistent with Campbell’s (2006) conjecture, retail structured financial products are

used to exploit investors with low financial literacy in our Hong Kong sample. In the

same vein as Stango and Zinman (2009), our evidence on “neglected risk” provides

substance for financial sophistication.

3

The rest of the paper is organized as follows: We first provide a brief review of the

household finance literature in Section 2. A simple model of investment decision making

under local thinking is presented in Section 3. The retail structured product market and

product design is introduced in Section 4. Section 5 describes our sampling process and

presents the data. Empirical results and robustness check are reported in Section 6 and

7. Section 8 summarizes our findings and concludes.

2 Related Literature

The investment literature often assumes good behavior from all market players: security

issuers design a new product to improve social welfare, financial intermediaries truthfully

transmit information about the products, investors understand the product and execute

the best strategy. It is an empirical issue whether these conditions are met in reality.

The best evidence is from laboratory experiments and field experiments. For example,

Charness and Levin’s (2005) lab experiments show that investors over-extrapolate from

their former experience and tend to follow a suboptimal reinforcement strategy. Choi,

Laibson, Madrian and Metrick (2009) substantiate such result using individual 401(k)

investment data. Kaustia and Knupfer (2008) have similar findings for individual IPO

investors. Asparouhova, Bossaerts, Eguia and Zame (2009) show that investor’s cognitive

biases hinder information updating, lead to perceived ambiguity, and cause deviation

from rational decision making.

Above studies are on stocks or familiar investment vehicles. The findings may not

generalize to financial innovations such as structured products. We examine how indi-

vidual investors actually make allocation decisions over new illiquid financial products,

which is part of household finance that needs more empirical research as advocated by

Campbell (2006). Although Das and Statman (2009) argue that structured products can

help improve portfolio allocation, several recent studies suggest that retail structured

financial products are persistently overpriced by about eight percent (see Henderson

and Pearson (2008), and Bergstresser (2008)). A natural question is how the issuers

get investors to buy large amount of such overpriced products. Investors have little

prior knowledge about those investments. Theories on choice under ambiguity would

imply zero participation in such case. Hence, market frictions might have existed to defy

compliance with theoretical predictions. Subrahmanyam (2009a) shows that financial

intermediaries such as distributing banks may delay educating inexperienced individual

investors in order to earn more commissions. Moreover, Carlin and Manso (2009) argue

4

that firms may strategically use product complexity to extract consumer surplus. Our

empirical results will shed light on the existence of such frictions.

How can individual investors make best investment decisions in a market flourished

with financial innovations issued by strategic financial intermediaries? One answer is

market selection. Only those good at financial securities (financially literate) should

be participating. However, Hilgert, Hogorth and Beverly (2003), Agrew and Szykman

(2005), National Council on Economic Education’s report (NCEE 2005), show that

most Americans fail to understand basic financial concepts and conditions of financial

instruments, such as consumer loans and mortgages. More recently, Lusardi and Mitchell

(2006, 2008) report a wide-spread lack of ability on interest compounding among older

(50+) individuals in the U.S.. Lusardi and Tufano (2009) show a lack of knowledge on

debt among all U.S. citizens. Similar problems of low financial literacy are also found

in other countries.2

More importantly, lack of financial literacy influences individual suboptimal saving

and portfolio choices. For example, Lusardi and Mitchell (2006, 2008) find that, those

who have a better understanding of compound interest, inflation and diversification are

more likely to set up plans for retirement. On portfolio choice, less literate investors

are less likely to invest in stocks (van Rooij, Lusardi and Alessie (2007), Yoong (2007),

Christelis, Jappelli, and, Padula, (2008)), and less likely to choose mutual funds with

lower fees (Hastings and Tejeda-Ashton (2008)). Similarly, Campbell (2006) reports

that individuals with lower income and education level – characteristics that are closely

related to financial literacy – are less likely to refinance their mortgages during a period

of falling interest rates.

Further studies have shown the channels through which financial literacy works.

Dohmen, Falk, Huffman, and Sunde (2009) use more than 1000 adults in Germany and

find that investor’s IQ, which is a usual proxy for cognitive ability, is negatively related

to risk aversion and impatience. Grinblatt, Keloharju, and Linnainmaa (2009 a,b) also

find that high IQ investors are more likely to participate in stock market, and pick stocks

with higher returns using data from Finland. Another conceivable way to improve finan-

cial literacy is education. Haliassos and Bertaut (1995) argue that “education and the

free acquisition of information are important in overcoming the barrier to stockholding

erected by ignorance and misperceptions.” Similar results is found by Luigi and Jappelli

(2005) who show that education is positively correlated with individual awareness of

2See OECD (2005), Smith and Stewart (2008), Christelis, Jappelli, and Padula (2008), Moore (2003),Miles (2004).

5

stocks. In addition, Campbell (2006) suggests that education helps reduce households’

entry cost to stock market. He shows that educated households in Sweden diversify

their portfolio more efficiently, and can expect higher returns if they participate in stock

market. Woodward (2003) reports that college education is associated with a remark-

able $1,500 reduction in average broker fees for mortgage loans. Lusardi and Mitchell

(2006, 2008), Lusardi and Tofano (2009), Stango and Zinman(2009) also suggest that

more financial education is needed to improve investors’ financial literacy.

However, while it is easy to reach consensus on financial literacy, discontent exists on

the effectiveness of education. One discontent is argued by Heckman (2006) that the re-

lationship between cognitive and non-cognitive skills is complex, such that non-cognitive

skills and personality traits could cause people to endogenously create environments dur-

ing childhood that foster faster cognitive development. Education has less effect on cog-

nitive ability when it is given later, an may provide little help on their decision making.

Another discontent is about the debate on effectiveness of financial literacy education.

Bernheim, Garret, and Maki (2001) show that high school financial literacy training

programs will significantly increase individuals’ saving rates 5 years after graduation.

Bayer, Bernheim and Scholz (2009) provide evidence that frequent retirement seminars

increase both of individuals’ participation rates and contribution rates to savings plans.

However, Mandell and Klein (2009) find high school students who have taken financial

education do not demonstrate higher levels of financial literacy than those who have

not taken such courses. Moreover, Cole and Shastry (2009) suggest that one more year

of education will lead to 7.6% more chance to receive positive investment income. But

this effect does not come from mandatory financial literacy curriculum in schools, yet,

is due to individual’s cognitive ability to accomplish the education. The data on Hong

Kong household investments in structured financial products provide a good setting for

us to investigate above issues. We shed light on investor behavior in a new market of

illiquid securities (with plenty of ambiguity). Our results on financial literacy, cognitive

abilities, IQ, and education will help resolve some of the theoretical debates.

Our study follows a similar vein as by Choi, Laibson, and Madrian (2009). They

focus on index fund choice by individual investors. Different from their hypothetical

investment setting, our subjects made real investments and they might not have had

choices. Nevertheless, we both emphasize the importance of financial literacy.

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3 A Simple Model of Investments with Local Think-

ing

3.1 Introduction to the Model

Here we present a simple model of investors’ assessment about the profitability of invest-

ing in structured products, based on what they can recall from available information.

In particular, if investor cannot perfectly recall all the possible scenarios related to the

profitability, we call this investor has local thinking (see Gennaioli and Shleifer 2010 for

a model applied to general cases).

Assume structured products have two sources of risks, common risks, and hidden

risks. Assume all investors can observe common risks and are able to link common risks

to profitability of the products. We explore the cases when investors cannot observe

hidden risks, or cannot link hidden risks to profitability of the products even they observe

hidden risks. Before we go to details about the model, here we summarize four cases

that we will analyze in this section:

• Case I, benchmark case, where investors can perfectly observe and link both risks

to the scenario of losing money in structured products;

• Case II, neglected risks case, where investors due to some reasons (ie. did not go

through risk profile assessment) neglected the hidden risks;

• Case III, financially illiterate investor case, where investors, no matter observing or

not observing hidden risks, cannot link hidden risks to profitability of the products

due to lack of financial literacy;

• Case IV, complex product case, where less complex part of the product is modeled

as source of common risks, and more complex part of the product is modeled as

source of hidden risks. We conjecture that the complex part is so complicated such

that all investors will have problem understanding hidden risks.

3.2 The Model

The state of world is defined as 2 dimensional probability space composed of profitability

and risks of the structured products. More precisely, we have X = {lose, earn} ×{common risks, hidden risks}.

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Denote h1 = (lose, ·); h2 = (earn, ·);d1 = (·, common risks); d2 = (·, hidden risks).

as 4 scenarios. Particularly, h1 means the scenario when investor suffer lose in invest-

ments in structured product. Therefore, we have in total 4 elements in the world:

(h1 , d1 ), (h1 , d2 ), (h2 , d1 ), (h2 , d2 ). The probabilities of each state is assumed as:

A1: Pr(h2 , d1 ) > Pr(h1 , d2 ) > Pr(h1 , d1 ) > Pr(h2 , d2 )∑Pr(hi , dj ) = 1 , i , j = 1 , 2

Denote b ≥ 1 as the maximal number of scenarios that the investor can recall per

hypothesis. So, b can be either 1 or 2. b = 1 : imperfect recall; b = 2 : perfect re-

call. Assume investors, based on his acquired information about the risks, use Bayesian

update to obtain his posterior of probability that h1 occurs.

3.2.1 Case I: Benchmark

In this case we assume investor observe both types of risks, and can perfectly recall

scenarios related to h1. Then we have the true probability on losing in investments in

structured products is:

Pr(h1 | {d1 , d2}) = Pr(h1∩{d1 ,d2 })Pr(h1∩{d1 ,d2 })+Pr(h2∩{d1 ,d2 })

= Pr(h1 ∩ {d1 , d2})= Pr(h1 ∩ {d1}) + Pr(h1 ∩ {d2})

3.2.2 Case II: Neglected Risks

We proxy for neglected risks by the case that investors’ risks profile is not not assessed

before investing (update to more general argument later). Since the risk profiled is not

assessed, investor only obtain common risks of the product, but not the hidden risks.

Therefore, his obtained data is {d1}, and perceived the probability that h1 occurs as:

Pr(h1| {d1}) =Pr(h1 ∩ {d1})

Pr(h1 ∩ {d1}) + Pr(h2 ∩ {d1})

From A1, we know that Pr(h1 | {d1}) < Pr(h1 | {d1 , d2}) Investor who did not go

through risk profile assessment underweight the downside of structured product due to

the lack of information.

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3.2.3 Case III: Financially Illiterate Investors

For financially illiterate investors, but due to his lack of financial literacy, he can only

recall the common risks of the product in his mind, but not the hidden risks, even if he

was provided the whole data of the risks (ie. his risk profile has been assessed). Due to

this imperfect recall, his perceived probability of h1 is:

PrFinIll(h1 | {d1 , d2}) =Pr(h1 ∩ {d1})

Pr(h1 ∩ {d1}) + Pr(h2 ∩ {d1})= Pr(h1 | {d1})

3.2.4 Case IV: Complex Products

Given structured products has several components, we conjecture that risks from the

more complex parts of the product are more likely to be left outside of investors’ recall.

For example, investors maybe quite familiar with the default risks embedded in the

collateral part of structured product, which looks similar to a bond, but may not be

able to recall the counterparty risks from credit default swap structure, which is in

general new to households. Therefore, the complex parts of the security could serve as

sources for hidden risks. The more complex the structure is, the less obvious the related

hidden risks are. In particular, if the product is extremely complex, then investors’

perceived probability of losing money in the product h1 is:

PrComp(h1 | {d1 , d2}) =Pr(h1 ∩ {d1})

Pr(h1 ∩ {d1}) + Pr(h2 ∩ {d1})= Pr(h1 | {d1})

3.3 Hypotheses

Our model is derived from the more general model about local thinking in Gennaioli,

and Shleifer (2010). But in their paper, how much can investors (imperfectly) recall

from available information is exogenously assumed. In this paper, we explore deeper

on what conditions prevent investors from perfectly recall, and propose two possible

candidates–financial illiteracy and complexity of products. Based on the results from

the model, we form four hypotheses as follow, and empirically examine these hypotheses

in later sections.

H0: Structured products are overpriced, and should not be incorporated into household

portfolio;

9

H1: Investors who did not go through risk profile assessment incorporate more pro-

portion of structured products into their portfolio (neglected risks bias effect);

H2: Effects of neglected risks bias is stronger among financially illiterate investors;

H3: Investors invest more in structured products with more complex payoff structure.

4 Market for Retail Structured Financial Products

Structured financial products, characterized by customized payoff streams and illiquid

secondary market, have become increasingly popular investment vehicles. The most

well known structured product is probably collateralized debt obligations (CDOs) which

are the key driver of the recent credit market boom (2005-2007) and bust (2007-2009).

(See Brunnermeier (2009) and Coval, Jurek, and Stafford (2009) for overviews.) How-

ever, given the extremely high requirement of minimal investment in CDOs, individual

investors can hardly afford to purchase such products. Structured financial products,

characterized by customized payoff streams and illiquid secondary market, have become

increasingly popular investment vehicles. The most well known structured product is

probably collateralized debt obligations (CDOs) which are the key driver of the recent

credit market boom (2005-2007) and bust (2007-2009). (See Brunnermeier (2009) and

Coval, Jurek, and Stafford (2009) for overviews.) However, given the extremely high

requirement of minimal investment in CDOs, individual investors can hardly afford to

purchase such products. As such, structured products targeting retail investors were

created to meet investors’ needs. A typical way is to add CDOs (or other derivatives)

into the collateral pool of retail structured products, and then sell the retail structured

products with a much lower minimal investment threshold.

Retail structured products has been sold to individual investors ever since mid 1990s

in Europe, but become noticeable in Hong Kong only after 2003. In the February of

2003, Hong Kong Securities and Futures Commissions (SFC) relaxed prospectus rules for

unlisted securities, and ignited the retail structured product market. Before the change,

issuers of structured products need to register for both programme prospectus and issue

prospectus for each issue, even if a series of issues belong to the same programme (eg.

minibond 3, minibond 5, ...). Under the new rule of “dual prospectus”, issuers only

need to register for programme prospectus for the first issue. For the later issues, issuers

simply register for issue prospectus but do not need to register for programme prospectus.

This largely reduced the cost for issuers to issue products. Another reason for the

10

spring up of retail structured products in Hong Kong is because of the low interest rate

around 2003. Due to the low interest rate, bank depositors are eager to find substitutes

for saving. The high coupon rate along with the seemingly “safe” feature of some

structured products made them attractive to retail investors. These structured products

target retail users typically by using well-known companies or popular share issues as

reference entities. Some are transparently speculative but others, can be designed to

seem conservative in their headline terms, like “minibond” issued by Lehman Brothers.

Figure 1 illustrates the global sales of retail structured products from 2002 to 2009.

During the emerging period from 2002 to 2007, the sales of retail structured products in

Hong Kong has risen from USD0.6 billion to USD44.3 billion. During the credit crisis

period of 2007 to 2009, structured product market drop all over the world. But what

surprises us is that the market in Hong Kong dropped much more than that in any

other places. In 2009, Hong Kong structured product market faced a 78.7% drop, which

is much larger than that in Europe (11.4%), Asia Pacific (37.1%), and North America

(44.7%). One potential explanation could be ascribed the fall of Lehman Brothers.

Before its bankruptcy, Lehman was one of the most successful in this market with a 35

percent market share and over 33,000 Hong Kong buyers (see Lejot (2008)). Besides its

negative impact to the market, Lehman’s bankruptcy has also ignited a conflict between

structured product investors and the product distributors. In fact, investors in Hong

Kong, Singapore and Taiwan were shocked when they were informed of their holdings in

retail structured financial product were issued or related to the failed Lehman Brothers.

At the time of Lehman Bankruptcy on September 15, 2008, HKD20.173 billion

structured products associated with Lehman were still outstanding in the market from

43,707 investment accounts.3 Two types of structured products are affected by Lehman

bankruptcy: credit-linked note (CLN) and equity-linked note (ELN). The most pub-

licized is “minibond” CLN issued by Lehman Brothers. Another noteworthy CLN is

“constellation” issued by Development Bank of Singapore (DBS). Appendix III pro-

vides detailed issuance information on minibond and constellation. The investment in

these three groups of products take 97% of the total investment in Lehman Brothers

related products.

Figure 2 shows the structure of CLNs and ELNs. CLNs are medium-term notes with

first-to-default feature. Their payouts are based on a group of companies’ (“reference

entities”) credit performance. Those notes normally have 3 to 5 years investment horizon

3“List of information/ documents requested by Members”, Hong Kong legislative Council,www.legco.gov.hk/yr08-09/english/hc/papers/hc1013cb2-100-3-e.pdf

11

with coupon rates slightly higher than quarterly bank deposit rates. However, the risks

of CLNs come from multiple sources. Take minibond series 35 as an example. The first

risk is from underlying collateral. When investors purchase the minibond, issuer will use

the proceeds collected from investors to buy high quality assets (often to be AAA rated

CDOs) as underlying collateral for the minibond. When there is an event of default

for collaterals, minibond will be redeemed early at the price based on the proceeds

of selling the collateral assets (so called “early redemption amount”), which may be

significantly below the principal amount of the minibond outstanding. The second risk

is swap counterparty risk. The issuer signs swap contracts to hedge currency risk and

interest rate risk. Swap counterparty takes the yields from the underlying collaterals and

provides fixed coupon payment to the investors. But when default of swap counterparty

occurs, minibond will also be redeemed at the early redemption amount. Finally, the

investors’ position as insurer in the swap leads to another risk. The swap is based on

the credit performance of the reference entities (normally 5 to 8 names).4 For the case

of minibond, the credit rating for these reference entities may range from AA+ to BBB.

If any of these reference entity goes bankrupt, fails to pay its liability, or is restructured,

minibond will be redeemed at an amount based on selling of the subordinate debt of

that troubled reference entity. In this case, investors may lose most of their investments.

We summarize the payoff function (gross return) of CLNs, take minibond series 35 for

example, as follow:

f(x) =

1 + it : if issuer exercise call option before maturity date;

x : if early redemption event occurs;

rj : if credit event occurs to reference entity j;

1 + 5.6% : if nothing happens.

Here it is the cumulative coupon rate before the day issuer exercise call option; x is the

value of collateral regarding to one share of CLNs when early redemption event occurs;

rj is the recovery rate of the subordinated notes of the reference entity to which credit

event occurs.

For equity-linked notes, as illustrate in Figure 2, investors also suffer from the un-

derlying collateral risk and swap counterparty risk. The key difference in the structure

4Reference names for Minibond Series 35 are: HSBC Bank PLC (Aa2/AA-), Hutchison WhampoaLimited (A3/A-), MTR Corporation Limited (Aa2/AA), the People’s Republic of China (PRC) (A1/A),Standard Chartered Bank (A3/A), Sun Hung Kai Properties Limited (A1/A) and Swire Pacific Limited(A3/A-). The credit ratings shown next to each reference entity are those applicable to the referenceobligation as on 11 January 2008–shortly before the minibond is issued, as published by Moody’sInvestors Service and/or Standard & Poor’s.

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of ELNs with that of CLNs is that the swap is linked to the stock price of a basket of

(normally 3 to 6) companies. Figure 3 shows how the payoff of ELNs is linked to the

stock price of the reference companies. Take Pyxis Series 21, an ELN issued by Lehman

Brothers in May 2007, for example. The investment horizon of the note is 2.5 years.

Coupon will be paid every half a year after issuance at the observation dates. During

each of the second to fifth observation dates, there are four auto-calls by the issuer. If

the closing price of each reference stock on observation date is at or above 96% of its

fixing price (equal to the stock price when the note is issued), the note will be redeemed.

This auto-call structure bundled with the fixed coupon rate put a “cap” on the payoff.

In the best scenario, investor will get a 20% return when the note matures. However,

when the stock price of any linked companies falls below 75% of the fixing price on any

day within the 2.5 years, investor will have to wait until the maturity date to get back

the principal investment. Moreover, when default of the underlying collateral or swap

counterparty occurs, the note will also have to be redeemed early at an amount based

on the proceeds of selling the collateral, which may be significantly below the principal.

Unlike those structured products examined by Henderson and Pearson (2008), retail

structured financial products are not listed on any exchange in Hong Kong. All trans-

actions are executed over the counter at distributing banks. Once issued, most of the

structured products are not priced until maturity or when knock-out events, such as

credit event for CLNs, occur. There is no way to track the performance and market

value of such products. Hence, it is difficult for retail investors to form expectation

about the risks and returns of such products. There is no secondary market for those

products. Initial investors likely have to hold the products till maturity. The relatively

long maturity, 3 to 5.5 years for CLNs and 2 years for ELNs, makes investment in such

products even riskier. Overall, it seems difficult for investors to get a good handle of such

investments. We use survey data to explore the key motives for investors to purchase

these products.

5 Data and Sample Description

5.1 Data Collection

We collect data from investors of Lehman related structured products through individual

interviews. The interviewers are University of Hong Kong students, mostly Cantonese

speakers. The interview will go over a list of items on a questionnaire designed by our-

13

selves. The interviews were conducted during the 11 times of the large protests and

gatherings by investors between January 15 and June 18, 2009. Our sample consists

of data from 783 structured product investors. The interviewers randomly selected the

interviewees and asked questions face-to-face. Our questionnaire has three sections: in-

vestment decision environment, investor financial background, and investor demographic

characteristics. On March 14, 2009, we revised our questionnaire by adding questions on

family monthly income, homeownership, whether they are familiar with salesman, and

a question on simple calculation, without changing the original questions. The sample

is roughly evenly distributed: 430 investors surveyed before March 14 and 353 investors

surveyed after March 14, 2009.

In order to examine sample selection issue, we further interviewed a group of in-

vestors who did not invest in Lehman related structured products as control sample.

Those interviews were conducted between July 24 and August 10, 2009. We used simi-

lar questionnaire, with minor change on the questions in investment decisions. We chose

to conduct the surveys in 11 districts of Hong Kong where most of the Lehman struc-

tured product investors live to control for geographic factors. We randomly selected

75 investors in those areas, such as from streets, parks, or from railway stations, and

obtained similar information on demographic, financial, and investment characteristics.

Figure 4 illustrates a pattern of co-movement between total investments in minibonds

from the subjects in our sample and Hang Seng Index (HSI), the stock market index in

Hong Kong, from July 2, 2003 to June 30, 2008. Presumably investors have more to

invest in structured products when equity market condition is good. Notably, as shown

in Appendix III, the largest group comes from investors of minibond series 35B issued on

February 22, 2008, at a time financial crisis was going strong. However, as by Souleles

(2009) that when market condtion goes down, investors are more likely to shun away

from purchasing securities for the purpose of hedging.

5.2 Sample Description

Table I presents descriptive statistics of our key variables (definitions are given in Ap-

pendix I). Respondents report the name of the structured products they purchased and

the proportion of their total financial wealth that they invested in the structured prod-

ucts. Their average monthly income is HKD16,499. On average, each investor invests

60% of financial wealth in such products. Only 25% of the subjects ever bought lottery

tickets. Investors on average hold 7% of stocks, 82% own properties. About 54% of the

14

investors went through risk profile assessment documentation, 37% were not familiar

with the salesperson. Interestingly, when we compare our sample with two major survey

sample in Hong Kong5, we find that investors in our sample are in general older and

contains more women than men. But in terms of education and financial characteristics,

investors in our sample are quite similar to the other two samples. Appendix II reports

the details of this comparison.

Our sample contains all of the three main structured products that are related to

Lehman Brothers, namely Minibond, Constellation and equity-linked notes (ELN here-

after). The differences between ELN investors and CLN investors are substantial. ELN

investors are better educated, with 2 more years of education on average, and more afflu-

ent than CLN investors in both total financial wealth and family monthly income. The

average self-reported investment proportions by investors of each group are all above

50%. Financial and demographic characteristics show that these investors are basically

senior and poorly educated people. The average age is above 55; more than 70% of them

are retired, and only 15% attended college. About two thirds of them cannot do simple

interest rate compounding calculation.

A key variable for our analysis is whether investor has gone through risk profile as-

sessment done by distributing banks before purchasing the structured products. About

54% of investors did not go through it. By giving up this risk profile assessment, investor

is potentially losing a best chance to understand what kind of risks are embedded in

the structured products, especially the risks related to extreme economy scenarios (like

bankruptcy of Lehman Brothers). Investors’ connection with salesperson and neigh-

borhood seems to capture their neglected risk bias, too. If investor is more closely

acquainted with salesperson, he or she may obtain more information about the risk of

the structured products from chatting with the salesperson. It is the similar case when

he or she participate in neighborhood activities – he may have a higher probability of

getting information about some risk he neglected. In our sample, 66% investors are

acquainted with the salesperson. The salesperson could be either a close friend of the

investor or a client manager that has helped the investor for several years. 42% investors

engaged in neighborhood activities.

Another set of variables which is equal important is measures of financial literacy.

Aside from the ability of interest compounding and schooling, we form another measure

5“2006 Population By-census” report conducted by Hong Kong Census and Statistics Departmentfrom July to August 2006, and “Retail Investor Survey 2009” conducted by Hong Kong Exchange andCleaning Limited from November to December 2009.

15

of financial literacy rational rational expectation approach. We adopt investor’s self-

reported expectation of Hong Kong stock market annual return, and use it as a proxy

for financial literacy. Among the 353 interviewees we asked for their expectation, 159

cannot answer. The histogram of answers from the other 194 investors is plotted in

Figure 5. Unsurprisingly, investors tend to choose sentimental numbers such as 0% (25

responses), 5% (30 responses), 10% (48 responses), 20% (20 responses), but there is

also wide dispersion among the answers. Panel A of Figure 6 shows that the wealth

invested in structured financial products is high in groups sorted on their stock return

expectation. The investment proportions are all higher than 50%. However, those who

can give more reasonable expectation to Hong Kong stock market annual return (the

third group) on average put less proportion of wealth in purchasing structured financial

products.

Panel B of Figure 6 shows that the proportion of financially literate investors de-

creases as the investors’ investment proportion increases. Among those who have in-

vested less than half of their wealth in structured products, there are significantly more

literate investors than non-literate investors. However, this difference decreased and re-

versed in the group of people who invested more than half of their wealth in structured

products. Panel C of Figure 6 shows that investment proportion in structured products

first increase and then decrease as we move from low income investors to high income in-

vestors. Investors of middle income level invest more proportion of wealth in structured

products. Within each group, the financially illiterate investors invest more proportion

of their wealth than literate investors.

6 Empirical Results on Allocation

6.1 Neglected Risks Bias and Investment Allocation

Table II reports the effect of neglected risks on investment proportion in structured

financial products. Our key measure for neglected risk captures whether investor had

gone through risk profile assessment before purchasing the products. By giving up this

risk profile assessment, investor is potentially losing a best opportunity to understand

what kind of risks are embedded in the structured products, especially the risks re-

lated to extreme economy scenarios (like global financial crisis or bankruptcy of Lehman

Brothers). Mode 1 reports a positive significant univariate effect of giving up risk profile

assessment on investment proportion in structured products. Neglected risks bias by

16

itself gives adjusted R2 as large as 2%.

Mode 2 of Table II shows the baseline effect of household investors’ background and

market condition on investments. Not surprisingly, higher income family, and family

that owns their own house put less proportion of their wealth in structured products.

Interestingly, interest rate, which is captured by 1 year HIBOR,6 is significantly neg-

atively correlated with investment proportion. this is consistent with our conjecture

that investors misunderstood structured products as low risk savings. Therefore, when

interest rate goes down, structured products, bearing the feature of constant cash flows,

become a good alternative.

Mode 3 of Table II reports the incremental effect of neglected risks bias. By compar-

ing mode 3 and mode 2, we have the adjusted R2 increased by 3%. The marginal effect

of neglected risk bias increased to 10%. On average, if investors did not go through risk

profile assessment, they would buy 10% more of structured products. To provide more

robustness on the effect of neglected risks bias on investments in structured products,

we adopt two more alternative measures of neglected risks. Investors’ lack of connection

with salesperson or neighborhood seems to capture their neglected risks bias, too. If

investor is more closely acquainted with salesperson, he or she may obtain more infor-

mation about the risk of the structured products from chatting with the salesperson.

It is similar case when he or she participate in neighborhood activities–he may have

a higher probability of getting information about some risks he neglected. Table III

shows that the neglect risks bias effect remains, either measured by investors’ relation

with salesperson (mode 1-3), or by neighborhood activity engagement (mode 4-6). The

magnitude of the marginal effect is around 6% to 10%.

6.2 Effect of Sell Side Product Complexity

From above discussion, there is clear evidence that investors’ neglected risks bias affects

their investment decision making. Financial institutions should be able to anticipate

this bias. But how can they gain profits from this bias is a practical issue. Recent

literature (Carlin 2009) show that institutions may strategically make their products

complex to extrapolate consumer surplus. Creating complexity, in our setting, prevents

investors from fully understanding the structure of the products. Therefore, there could

be a higher possibility that investors misunderstood the risks behind the products, and

6Hong Kong interbank offer rate: the annualized offer rate banks offer for a specified period rangingfrom overnight to one year.

17

neglected risks bias are induced. We examine this issue by analyzing the complex feature

of structured products in our sample.

Our first measure of complexity is the dispersion of credit rating of reference entity

of CLNs. Normally, a CLN will have 3 to 8 reference entities, which are typically big

companies either in Hong Kong or outside. Any of the reference entities has credit event

(e.g. default), the CLN will be redeemed and at the value equal to the subordinate bond

of the defaulted reference entity, which could be much less than principal. Therefore,

been able to understand the credit rating criteria becomes important to CLN investors.

However, for issuers of CLN, choosing six reference entities with credit rating as AA-,

A+, A, A-, BBB+, BBB, put s much more complexity to investors than choosing six ref-

erence entities with rating A for each. We conjecture that larger dispersion of the credit

rating will lead to more complexity, and a higher chance to induce investors’ neglected

risks bias. Table IV, mode 1-3, report this effect. Mode 1 shows that higher dispersion

of credit rating has a strong positive effect on investment proportion in structured prod-

ucts. Mode 2 and mode 3 focus on interaction terms. The effect goes a little weak, but

remains. The R2 increased to 11%.

Our second measure of complexity is the currency tranche. Each series of CLN

has at least two tranches: HKD tranche, and USD tranche. Since in Hong Kong,

HKD is dominating the market, why would institutions combine exchange risk with the

CLN which is already complex in structure. We use currency as another measure of

complexity, and found similarly positive and significant results.

6.3 Financial Literacy and Neglected Risks

Given there is a chance that institutions may strategically use price complexity to in-

duce investors’ neglected risk bias, will investors’ financial literacy help attenuate the

bias effects? We adopt three measures of financial illiteracy and examine the effect

interactions terms of financial illiteracy and neglected risks. All the three interaction

terms are significant as shown in Table V. In mode 2, adding the coefficient for neglected

risk and coefficient for interaction term, we got neglected risk is 1.5 times stronger for

investors who do not understand compound interest rate is higher than simple interest

rate. Similar from the other two financial illiteracy measures in mode 3-4, and mode

5-6. Table VI reports the joint effect of regressing investment proportion in structured

products on neglected risks bias, price complexity, and financial illiteracy. When putting

together, we can explain 15% of variation of investment proportion.

18

7 Robustness Checks and Alternative Interpretations

Our above results on neglected risks, financial illiteracy, and product complexity could

be driven by a specific group of investors or product. In this section, we explore whether

those effects vary across different sample selection criterion. By doing so we can verify

the robustness of our prior findings as well as explore new implications within subgroups.

7.1 Subgroup by Age

We separate our sample into two groups by age. Korniotis and Kumar (2009) examine

the role of age in investment performance, and show that older people are better in

understanding financial knowledges but no better in timing the market. Neglected risks

and financial literacy may play different role under different conditions. Indeed, as shown

in Table VII, we find that effect of neglected risks, measured by risk profile not assessed,

are stronger for the group of investors aged below 57. Those effects are insignificant or

weak for investors aged 57 or above. Similar results for the effect of financial illiteracy,

measured by education below high school. These findings are consistent with Korniotis

and Kumer (2009) in the sense that older investors acquired more financial knowledge

from experience, and are investing with more caution even if they did not go through risk

profile assessment or have low education background. Notably, the R2s for each subgroup

regression (11% and 12%) are close the R2 for the regression with whole sample (13%

in mode 5 of Table VI). This supports that our separation of the sample is not biased.

7.2 Subgroup by Household Income

We also examine effect of neglected risks, and financial illiteracy in subgroups by their

household incomes. Vissing-Jorgensen (2003) discusses whether irrational behavior would

disappear with wealth. On the other hand, traditional economic theory suggests that

wealthier investors tend to be less risk averse. In this analysis, we separate our sample

by the median of household monthly income in our sample, which is 10,100 $HKD7.

Mode 1 to 3 report the effect on the households that have income below 10,100 $HKD,

and mode 4 to 6 report the effect on the households that have income above 10,100 $

HKD. The effect of neglected risks does not show too much difference on low income

7According to Hong Kong By-Census 2006, the median of household monthly income in Hong Kongis 17,300 $HKD. See Appendix II for details.

19

households and high income households. However, the effect of financial illiteracy is

much stronger among high income households than from low income households. One

possible explanation could be the illiterate wealthy investors beard excessive risks for

pursuing high returns.

7.3 Determinants of Neglected Risks Bias

In Table IX, we attempt to understand the driving factors of neglected risks bias.

Specifically, we run probit regression of investors’ backgrounds on their behavioral of

not taking risk profile assessment. We find that the exogenous measure of investors’

financial literacy–education below high school, is has significant explanatory power. In-

vestors’ preference for lottery and household indebtedness also contributes positively to

investors’ neglected risks behavior. In total, we have an explanatory power as large as

0.07 represented by pseudo R2.

8 Summary and Conclusion

Individual investors in Hong Kong, Taiwan, and Singapore bought substantial amount

of structured products which turned out to be CDOs in disguise, as revealed by the

Lehman Brothers bankruptcy in September 2008. It is difficult to justify initial invest-

ment decisions in retail structured products from standard rational theories as those

investors had little prior knowledge. We show that neglected risk has strong explana-

tory power for household asset allocation in structured products. Allocation is lower

when the investment is made more prudently with fully documented risk assessment.

Investors allocation is related to product complexity. The effect of neglected risk on

structured product investment is weaker for more financially literate investors.

This paper also demonstrates the importance of financial literacy for investment

decisions. Consistent with prior studies, our evidence suggests that improving investor

financial literacy is important for reducing neglected risks bias, and therefore, preventing

excessive demand (and issuance) for financial innovation.

Our findings have important implications for the ongoing debate on root causes of

the credit crisis in 2007-2009. If investors did not knowingly pursue investments in

structured products, the investment banks manufacturing such products are more likely

to be the culprit of the market development and the amplification of the crisis.

20

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Figure 1: Overview of structured product market from 2002 to 2009.This figure shows the gross sales of structured products to retail clients from 2002 to

2009 in Europe, Asia Pacific, North America, and Hong Kong. Data is provided by

www.structuredretailproduct.com. We only have sales data from 2006 to 2009 for North Amer-

ica due to limited access to their data base.

27

Figure 2: Structure of credit-linked notes and equity-linked notes. The first fig-

ure shows the structure of Credit-Linked Notes taking Minibond Series 35 as an example. The

7 institutions taken as reference entity of minibond series 35 reported below the figure. The

credit ratings shown next to each reference entity are those applicable to the reference obli-

gation as on 11 January 2008–shortly before the minibond is issued, as published by Moody’s

Investors Service and/or Standard & Poor’s. The second figure shows the structure of Equity-

Linked Notes (ELN) taking Pyxis ELN Series 21 as an example. The 6 HK-listed securities

are: Air China Limited, China Communications Construction Company Limited, China Mo-

bile Limited, Esprit Holdings Limited, Li & Fung Limited, and Ping An Insurance (Group)

Company of China, Ltd.

28

Figure 3: Payoff Structure of Equity-Linked Notes if No Early TerminationOccurs. This figure shows the payoff structure of Equity-Linked Notes by taking Pyxis Series

21 issued on 28 May 2007 as an example. This figure is taken directly from the prospectus

of Pyxis Series 21. The investment horizon for the note is 2.5 years. Coupon will be paid

every half a year after issuance at the observation dates. There are four auto-calls by the

issuer on each of the second to the fifth observation dates. Valuation date is equal to the

fifth observation date-about 2.5 years after issue date. When the swap between issuer and

swap counterparty is terminated prior to maturity date, the note will be redeemed at a price

based on the proceeds of selling the underlying collateral, which may be significantly below the

principal of the note. For Pyxis Series 21, the underlying collateral is European Medium-Term

Notes issued by Lehman Brothers Treasury Co. B.V.

29

Figure 4: Market Performance and Minibond Investment. This figure shows the

relation of total investment in each series of Minibond in our sample and Hang Seng Index. The

time line starts from July 2, 2003 to June 30, 2008. There are 637 observations of Minibond

investors. Those who purchased multiple series have been counted multiple times. The red

circle spots on the HSI line illustrate the date when each series of Minibond were issued.

30

Figure 5: Distribution of expectation about stock annual return. This figure

shows the distribution of investors’ expectation to Hong Kong stock market annual return.

We surveyed 783 investors who have purchased Credit-linked notes or/and Equity-linked notes

from February 2003 to May 2008 in Hong Kong, and randomly picked 353 of them to tell

their expectations about Hong Kong stock market annual return. 194 investors responded as

a percentage; the other 159 investors claimed that they cannot answer this question. One of

our key measure of financial illiteracy–“Stock Categorization = 0”, is a dummy variable if

investors’ expectation about Hong Kong stock market annual return lies out side the “Literate

Proxy 1” region: [5.1%, 50%]. We also constructed and tested an alternative measure of

financial illiteracy according to a more stringent region [7%, 17%] as shown in the figure as

“Literate Proxy 2”. The result for the alternative measure, not reported in the paper, is quite

similar to the first measure “Stock Categorization = 0”.

31

Figure 6: Financial Literacy and Portfolio Proportion. Illiterate investors are de-

fined according the the measure of “Stock Categorization = 0”, which is a dummy variable

equals to 1 if investor’s expectation about Hong Kong stock market annual return lies out side

[5.1%, 50%]. Panel A shows the average portfolio proportion of investors in 4 groups sepa-

rated by their expectation to Hong Kong stock market annual return. The four groups are: 1.

cannot answer the question; 2. expectation to stock annual return below 5.1%; 3. expectation

to stock annual return between 5.1% and 50%; 4. expectation to stock annual return above

50%. Panel B compares the composition of literate investors in four investment proportion

groups and composition of illiterate investors in four investment proportion groups. Investor

is regarded as financially ”Literate” if his/her expectation to Hong Kong stock market annual

return lies between 5.1% and 50%. The sample size of both Panel A and Panel B is 311. Panel

C categorizes literate investors and illiterate investors by their household income level, and

compares their investment proportion in structured products in each group. There are in all

312 observations in this sample. The factor of income ranges from 0 HKD to 125,000HKD.

32

Table ISample Summary Descriptives

This table reports the summary statistics and correlations of principal variables that are used in our analysis. Thedata were collected by questionnaire survey on Hong Kong investors who had purchased credit-linked note or equity-linked note during February 2003 and May 2008. We conducted the survey during January 15 and June 18, 2009, andobtained 783 responses. Panel A reports the summary statistics. “Can give reasonable estimation of stock return=0”equals to one if investor’s expectation of Hong Kong stock market annual return lies below 5.1% or above 51%. PanelB reports the correlations between all principal variable in our analysis. A detailed instruction about the definition ofeach variable is provided in Appendix I, and a comparison of our sample and two major survey sample was reported inAppendix II.

Panel A: Summary Statistics of Principal Variables

Variables Obs Mean Median Std

Proportion in structured products (%) 267 59.981 65.000 27.178

Neglected Risks BiasRisk profile assessed=0 267 0.536 1.000 0.500Acquainted with salesperson=0 267 0.371 0.000 0.484Engaging in neighborhoods=0 267 0.577 1.000 0.495

Sell Side: Pricing ComplexityRange of credit rating of reference entities 267 2.838 2.690 1.272Foreign currency=1 267 0.131 0.000 0.338

Buy Side: Financial LiteracyKnow interest rate compounding=0 267 0.625 1.000 0.485Can give reasonable estimation of stock return=0 267 0.655 1.000 0.476Years of education 267 10.060 12.000 3.731Above high school=1 267 0.577 1.000 0.495Above college=1 267 0.150 0.000 0.358

Household Representative BackgroundAge 267 55.472 57.500 9.500Male=1 267 0.360 0.000 0.481Married=1 267 0.899 1.000 0.302Buy lottery=1 267 0.251 0.000 0.434Household monthly income ($1,000 HKD) 247 16.499 10.100 18.622Household own house=1 245 0.824 1.000 0.381Household in debted=1 199 0.131 0.000 0.338

Household Asset AllocationProportion in saving (%) 234 28.627 18.750 26.111Proportion in bond (%) 234 5.677 0.000 11.474Proportion in equity (%) 234 7.042 0.000 13.575

33

Table

I-C

ontinued

Pan

elB

:C

orr

elati

on

Matr

ix

No.

Var

iab

les

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(14)

(1)

Ris

kp

rofi

len

otas

sess

ed1.

000

(2)

Not

acqu

aint

wit

hsa

lesp

erso

n-0

.020

1.0

00

(3)

Not

enga

gin

gin

nei

ghb

orh

ood

s0.

194

-0.0

46

1.0

00

(4)

Ran

geof

cred

itra

tin

g0.

030

0.0

38

-0.0

45

1.0

00

(5)

Kn

owin

tere

stco

mp

oun

din

g=0

0.00

0-0

.009

0.1

11

0.0

24

1.0

00

(6)

Rea

son

able

stock

retu

rnes

tim

atio

n=

00.

099

-0.0

50

0.1

10

0.1

07

0.2

68

1.0

00

(7)

Ab

ove

hig

hsc

hool

=0

0.17

4-0

.075

0.2

44

0.0

27

0.2

01

0.0

54

1.0

00

(8)

Age

0.05

0-0

.081

0.0

86

-0.0

09

-0.0

03

-0.0

08

0.2

08

1.0

00

(9)

Mal

e=1

0.00

50.0

92

0.0

21

0.0

00

-0.0

84

-0.0

80

-0.0

33

0.2

12

1.0

00

(10)

Mar

ried

=1

0.01

70.0

23

-0.0

03

-0.0

15

-0.0

31

-0.0

49

0.0

52

0.0

04

0.0

65

1.0

00

(11)

Bu

ylo

tter

y=

10.

098

0.0

67

0.0

05

-0.0

33

-0.0

68

0.0

17

-0.0

52

-0.0

18

0.1

54

-0.0

18

1.0

00

(12)

Hou

seh

old

inco

me

0.04

1-0

.063

-0.1

19

0.0

42

-0.1

70

-0.0

15

-0.2

43

-0.0

49

0.0

82

0.0

97

0.1

18

1.0

00

(13)

Hou

seh

old

ind

ebt

0.17

80.0

68

-0.0

63

-0.0

19

-0.0

21

0.0

31

-0.1

13

-0.0

94

0.0

32

0.0

04

0.0

50

0.1

80

1.0

00

(14)

Hou

seh

old

own

hou

se-0

.058

-0.1

03

-0.1

23

-0.1

45

-0.1

42

-0.0

89

-0.2

20

0.0

97

0.0

62

0.0

53

0.0

75

0.1

56

0.1

71

1.0

00

34

Table IINeglected Risks and Investments in Structured Financial Products

The sample comprises household investors who invested in structured financial products during 2003 and 2008. Thedependent variable is the proportion of wealth that the investor invested in structured products. The independent vari-ables are a measure of neglected risks, Risk Profile Not Assessed, and control variables including investors’ backgroundand market condition when investment was made. The Risk Profile Not Assessed variable equals one for investors whodid go through risk profile assessment by distributing banks before purchasing structured products. Income measuresthe household monthly income by $1,000 HKD. Hang Seng Index Return is the return of Hang Seng Index in the lastthree month before investments were made.

Dependent Variable: Investment Proportion in Structured Products

(1) (2) (3)

Risk Profile Not Assessed 9.07∗∗∗ 10.08∗∗∗

(2.75) (3.04)

Age −0.08 −0.14(−0.47) (−0.82)

Male −2.66 −2.60(−0.74) (−0.74)

Married −1.49 −1.14(−0.27) (−0.21)

Buy Lottery 3.26 2.12(0.83) (0.55)

Income −0.35∗∗∗ −0.36∗∗∗

(−3.73) (−3.90)

Own House −9.35∗∗ −8.23∗

(−2.04) (−1.82)

Indebted 4.12 0.75(0.72) (0.13)

Hang Seng Index Return 0.02 0.05(0.16) (0.38)

Interest Rate (HIBOR) −2.50∗ −2.81∗∗

(−1.86) (−2.12)

Constant 55.12∗∗∗ 86.06∗∗∗ 84.56∗∗∗

(22.86) (7.36) (7.34)

Observations 267 267 267Adjusted R2 0.02 0.05 0.08

35

Table IIIAlternative Measures of Neglected Risks

The table reports the effect of two alternative measures of neglected risks bias on investments in structured financialproducts. The dependent variable is the proportion of wealth the investor invested in structured financial products.The independent variables are two alternative measures of neglected risks bias (either Not Engaging in Neighborhoods,or Not Acquainted with Salesperson), and control variables including investors’ background and market condition wheninvestment was made.Not Engaging in Neighborhoods equals one for investors who do not engage in any activity inneighborhood community. Not Acquainted with Salesperson equals one for investors who are not acquainted with thesalesperson who introduced and facilitated the purchase.

Dependent Variable: Investment Proportion in Structured Products

Bias Measure: Bias Measure:Not Acquainted with Salesperson Not Engaging in Neighborhoods

(1) (2) (3) (4) (5) (6)

Neglected Risks 6.13∗ 6.39∗ 4.76 9.63∗∗∗ 9.86∗∗∗ 7.60∗∗

Bias Measure (1.79) (1.82) (1.38) (2.90) (2.94) (2.27)

Age −0.07 −0.05 −0.10 −0.07(−0.40) (−0.30) (−0.58) (−0.42)

Male −3.11 −3.05 −3.67 −3.45(−0.84) (−0.85) (−1.01) (−0.97)

Married −4.23 −1.93 −2.96 −0.99(−0.75) (−0.35) (−0.53) (−0.18)

Buy Lottery −0.21 2.62 0.71 3.17(−0.05) (0.67) (0.18) (0.82)

Income −0.34∗∗∗ −0.33∗∗∗

(−3.58) (−3.50)

Indebted 3.79 4.80(0.66) (0.84)

Own House −8.84∗ −8.49∗

(−1.93) (−1.86)

Hang Seng Index 0.00 0.05Return (0.03) (0.36)

Interest Rate −2.50∗ −2.20(HIBOR) (−1.86) (−1.64)

Constant 57.71∗∗∗ 66.61∗∗∗ 82.74∗∗∗ 54.42∗∗∗ 63.79∗∗∗ 79.00∗∗∗

(27.64) (6.00) (6.94) (21.58) (5.79) (6.58)

Observations 267 267 267 267 267 267Adjusted R2 0.01 0.00 0.06 0.03 0.02 0.07

36

Table IVPricing Complexity and Neglected Risks

This table reports the interaction of neglected risks bias and pricing complexity. The dependent variable is proportionof wealth invested in structured financial products. The independent variables are Risk Profile Not Assessed, whichis a measure of neglected risks bias, two pricing complexity measures (other Credit Rating, or Foreign Currency), andcontrol variables.Credit Rating equals one if the range of credit rating among reference entities are larger than fourrating levels (eg. Minibond Series 12 has six reference entities, among which the highest rating is A+ and lowest isBBB). See Appendix I for detailed definition. Foreign Currency equals one if investor purchased to USD or AUDtranche, instead of HKD tranche, of the structured products.

Dependent Variable: Investment Proportion in Structured Products

Complexity: Credit Rating Complexity: Foreign Currency

(1) (2) (3) (4) (5) (6)

Risk Profile Not Assessed 9.74∗∗∗ 9.36∗∗∗ 10.31∗∗∗ 10.26∗∗∗ 9.85∗∗∗ 12.06∗∗∗

[NoAssess] (2.98) (2.80) (3.10) (3.11) (2.86) (3.37)

Pricing complexity=1 24.68∗∗∗ 33.82∗∗ 8.12∗ 14.21∗∗

[Complexity] (2.79) (2.57) (1.69) (2.12)

NoAssess × Complexity 17.29 −16.75 1.77 −12.44(1.42) (−0.94) (0.26) (−1.30)

Age −0.12 −0.15 −0.11 −0.17 −0.15 −0.17(−0.71) (−0.88) (−0.60) (−0.97) (−0.83) (−1.00)

Male −3.38 −3.23 −3.06 −2.29 −2.49 −2.79(−0.97) (−0.91) (−0.87) (−0.65) (−0.70) (−0.79)

Married −0.76 −0.52 −1.22 −1.80 −1.23 −1.65(−0.14) (−0.10) (−0.23) (−0.33) (−0.23) (−0.31)

Buy Lottery 2.45 2.42 2.28 2.20 2.16 2.02(0.64) (0.63) (0.60) (0.57) (0.56) (0.52)

Income −0.36∗∗∗ −0.36∗∗∗ −0.36∗∗∗ −0.34∗∗∗ −0.36∗∗∗ −0.34∗∗∗

(−3.99) (−3.93) (−3.98) (−3.66) (−3.85) (−3.66)

Own House −8.65∗ −8.63∗ −8.43∗ −8.47∗ −8.31∗ −8.09∗

(−1.94) (−1.91) (−1.88) (−1.88) (−1.83) (−1.79)

Indebted 1.91 1.37 1.74 1.09 0.90 0.30(0.33) (0.24) (0.30) (0.19) (0.16) (0.05)

Hang Seng Index 0.02 0.04 0.03 0.06 0.05 0.08Return (0.17) (0.28) (0.20) (0.49) (0.38) (0.59)

Interest Rate −2.37∗ −2.56∗ −2.45∗ −2.82∗∗ −2.81∗∗ −2.86∗∗

(HIBOR) (−1.80) (−1.92) (−1.85) (−2.14) (−2.12) (−2.17)

Constant 81.61∗∗∗ 84.37∗∗∗ 80.71∗∗∗ 85.14∗∗∗ 84.73∗∗∗ 84.43∗∗∗

(7.14) (7.34) (7.04) (7.41) (7.33) (7.35)

Observations 267 267 267 267 267 267Adjusted R2 0.11 0.09 0.11 0.09 0.08 0.09

37

Table VFinancial Literacy and Neglected Risks

This table reports the joint effect of neglected risk bias and financial literacy (illiteracy). The dependent variableis the proportion of wealth the investor invested in structured financial products. The independent variable is RiskProfile Not Assessed, which is a measure of investors’ neglected risks bias, a financial illiteracy measure (either InterestCompounding=0, Stock Categorization=0, or Above High School=0), an interacting term of neglected risks bias andfinancial illiteracy, and background and market control variables. The Risk Profile Not Assessed variable equals onefor investors who did go through risk profile assessment by distributing banks before purchasing structured products.Interest Compounding=0 equals one if the investor does not know compound interest rate is higher than simple interestrate. Stock Categorization=0 equals one if the investor’s estimation of Hong Kong stock market return lies below 5%or above 50%. Above High School=0 equals one if the investor did not enroll in high school education.

Dependent Variable: Investment Proportion in Structured Products

Illiterate: Illiterate: Illiterate:Interest Compounding=0 Stock Categorization=0 Above High School=0

(1) (2) (3) (4) (5) (6)

Risk Profile Not Assessed 9.95∗∗∗ 4.17 9.72∗∗∗ 4.48 8.61∗∗∗ 4.19[NoAssess] (3.02) (0.94) (2.95) (0.97) (2.61) (1.05)

Financially Literate=0 7.21∗∗ 7.58∗∗ 10.27∗∗∗

[Illiterate] (2.10) (2.18) (2.96)

NoAssess × Illiteracy 9.18∗∗ 8.28∗ 12.02∗∗∗

(1.98) (1.72) (2.61)

Age −0.16 −0.14 −0.16 −0.15 −0.24 −0.23(−0.90) (−0.83) (−0.93) (−0.87) (−1.38) (−1.31)

Male −1.93 −2.67 −1.97 −2.41 −2.00 −2.05(−0.55) (−0.76) (−0.56) (−0.69) (−0.57) (−0.59)

Married −1.28 −1.21 −0.28 −0.67 −0.90 −0.69(−0.24) (−0.22) (−0.05) (−0.12) (−0.17) (−0.13)

Buy Lottery 2.09 2.64 1.78 2.07 1.50 1.76(0.54) (0.69) (0.46) (0.54) (0.39) (0.46)

Income −0.32∗∗∗ −0.33∗∗∗ −0.36∗∗∗ −0.36∗∗∗ −0.29∗∗∗ −0.31∗∗∗

(−3.45) (−3.58) (−3.92) (−3.88) (−3.08) (−3.27)

Own House −6.99 −7.77∗ −7.19 −8.24∗ −6.16 −6.17(−1.54) (−1.73) (−1.59) (−1.83) (−1.37) (−1.36)

Indebted 0.58 1.19 0.15 0.68 1.43 1.34(0.10) (0.21) (0.03) (0.12) (0.25) (0.23)

Hang Seng Index 0.09 0.08 0.11 0.09 0.07 0.05Return (0.69) (0.65) (0.80) (0.70) (0.54) (0.37)

Interest Rate −2.67∗∗ −2.84∗∗ −2.73∗∗ −2.98∗∗ −2.33∗ −2.58∗

(HIBOR) (−2.03) (−2.16) (−2.08) (−2.25) (−1.77) (−1.97)

Constant 78.74∗∗∗ 83.77∗∗∗ 78.85∗∗∗ 84.90∗∗∗ 81.93∗∗∗ 85.52∗∗∗

(6.68) (7.31) (6.72) (7.39) (7.19) (7.50)

Observations 267 267 267 267 267 267Adjusted R2 0.09 0.09 0.10 0.09 0.11 0.10

38

Table VINeglected Risks, Pricing Complexity, and Financial Literacy

This table reports the joint effect neglected risks, pricing complexity, and financial literacy (illiteracy) on investmentsin structured financial products. The dependent variable is the proportion of wealth the investor invested in structuredfinancial products. The Risk Profile Not Assessed variable equals one for investors who did go through risk profileassessment by distributing banks before purchasing structured products. Credit Rating equals one if the range of creditrating among reference entities are larger than four rating levels (eg. Minibond Series 12 has six reference entities,among which the highest rating is A+ and lowest is BBB). See Appendix I for detailed definition. Foreign Currencyequals one if investor purchased to USD or AUD tranche, instead of HKD tranche, of the structured products. InterestCompounding=0 equals one if the investor does not know compound interest rate is higher than simple interest rate.Stock Categorization=0 equals one if the investor’s estimation of Hong Kong stock market return lies below 5% or above50%. Above High School=0 equals one if the investor did not enroll in high school education

Dependent Variable: Investment Proportion in Structured Products

(1) (2) (3) (4) (5) (6)

Neglected Risks BiasRisk Profile Not Assessed 10.08∗∗∗ 9.91∗∗∗ 8.30∗∗ 8.21∗∗

(3.04) (3.04) (2.53) (2.54)

Pricing ComplexityCredit Rating 25.73∗∗∗ 25.24∗∗∗ 24.97∗∗∗

(2.88) (2.88) (2.89)Foreign Currency 7.69 8.47∗ 5.52

(1.60) (1.80) (1.17)

Financial IlliteracyInterest Compounding=0 4.65 4.73 5.03

(1.32) (1.35) (1.45)Stock Categorization=0 6.86∗ 6.41∗ 6.12∗

(1.94) (1.83) (1.76)Above High School=0 11.02∗∗∗ 9.70∗∗∗ 8.73∗∗

(3.19) (2.80) (2.54)

Investor Background Control Yes Yes Yes Yes Yes Yes

Market Condition Control Yes Yes Yes Yes Yes Yes

Constant 84.56∗∗∗ 83.49∗∗∗ 74.01∗∗∗ 82.18∗∗∗ 73.43∗∗∗ 71.08∗∗∗

(7.34) (7.22) (6.23) (7.22) (6.24) (6.08)

Observations 267 267 267 267 267 267Adjusted R2 0.08 0.08 0.11 0.11 0.13 0.15

39

Table VIINeglected Risks, Financial Literacy by Investor Age

This table reports the effects of neglected risks and financial literacy on investments in structured products insubgroups of investors specified by their age. The median of investors’ age is 57. Mode 1 to 3 report the ef-fect on those who are not older than 57, and mode 4 to 6 report the effect on those who are older than 57.

Investor Age Above or Equal Median Investor Age Below Median

(1) (2) (3) (4) (5) (6)

Risk Profile Not Assessed 7.14∗ 4.79 17.78∗∗∗ 17.40∗∗∗

(1.72) (1.15) (3.17) (3.11)

Interest Compounding=0 5.55 5.64 2.28 1.11(1.28) (1.31) (0.32) (0.17)

Stock Categorization=0 7.77∗ 7.23 6.30 8.20(1.76) (1.64) (0.96) (1.32)

Above High School=0 9.22∗∗ 8.34∗ 12.58∗∗ 10.99∗

(2.11) (1.88) (2.00) (1.84)

Male -0.48 0.63 0.68 -6.37 -4.34 -5.32(-0.11) (0.15) (0.16) (-1.03) (-0.67) (-0.86)

Married 0.42 3.36 3.36 -4.56 -7.56 -7.25(0.06) (0.47) (0.47) (-0.56) (-0.87) (-0.89)

Buy Lottery 2.59 2.21 1.72 0.53 1.90 0.77(0.52) (0.45) (0.35) (0.08) (0.29) (0.12)

Income -0.35∗∗∗ -0.27∗∗ -0.27∗∗ -0.55∗∗ -0.32 -0.40(-3.33) (-2.51) (-2.58) (-2.35) (-1.15) (-1.53)

Own House -11.69∗ -7.59 -7.53 -2.65 -3.36 -0.75(-1.97) (-1.27) (-1.26) (-0.38) (-0.45) (-0.10)

Indebted 5.27 6.75 5.41 -5.27 1.00 -6.40(0.65) (0.86) (0.68) (-0.62) (0.11) (-0.74)

Hang Seng Index 0.19 0.27 0.29∗ -0.22 -0.11 -0.07Return (1.15) (1.60) (1.68) (-1.04) (-0.46) (-0.32)

Interest Rate -3.58∗∗ -2.65 -2.87∗ -0.93 -0.57 -0.98(HIBOR) (-2.18) (-1.62) (-1.74) (-0.38) (-0.23) (-0.41)

Constant 79.46∗∗∗ 60.15∗∗∗ 59.06∗∗∗ 73.37∗∗∗ 68.44∗∗∗ 62.59∗∗∗

(7.98) (5.17) (5.06) (6.28) (4.75) (4.55)

Observations 180 180 180 87 87 87Adjusted R2 0.07 0.11 0.11 0.10 0.02 0.12

40

Table VIIINeglected Risks, Financial Literacy by Household Income

This table reports the effects of neglected risks and financial literacy on investments in structured prod-ucts in subgroups of investors specified by their household monthly income. The median of household in-come is 10,100$ HKD. Mode 1 to 3 report the effect on the households that have income below 10,100$ HKD, and mode 4 to 6 report the effect on the households that have income above 10,100 $ HKD.

Income Below or Equal to Median Income Above Median

(1) (2) (3) (4) (5) (6)

Risk Profile Not Assessed 9.79∗∗ 7.92∗ 9.31 9.46∗

(2.32) (1.86) (1.55) (1.79)

Interest Compounding=0 5.34 5.45 5.65 5.38(1.20) (1.23) (0.93) (0.90)

Stock Categorization=0 6.93 6.42 4.48 4.93(1.56) (1.46) (0.70) (0.79)

Above High School=0 8.36∗∗ 6.71 26.01∗∗∗ 26.07∗∗∗

(2.01) (1.59) (4.05) (4.12)

Age -0.23 -0.25 -0.30 -0.01 -0.25 -0.24(-1.02) (-1.10) (-1.30) (-0.04) (-0.87) (-0.88)

Male -2.54 -1.57 -1.39 -3.90 -0.36 -0.72(-0.56) (-0.35) (-0.31) (-0.61) (-0.06) (-0.13)

Married 1.20 1.98 2.23 -19.63 -13.17 -15.32(0.19) (0.32) (0.36) (-1.26) (-0.92) (-1.09)

Buy Lottery -4.41 -4.88 -5.15 10.36 12.93∗∗ 10.62∗

(-0.86) (-0.95) (-1.01) (1.61) (2.25) (1.83)

Own House -8.86∗ -5.80 -5.46 -9.47 -7.10 -6.02(-1.68) (-1.08) (-1.02) (-0.92) (-0.76) (-0.65)

Indebted -1.70 1.10 -2.73 -0.77 5.45 4.28(-0.21) (0.14) (-0.33) (-0.09) (0.71) (0.56)

Hang Seng Index 0.08 0.12 0.15 -0.09 0.16 0.16Return (0.44) (0.69) (0.87) (-0.39) (0.78) (0.80)

Interest Rate -2.00 -1.36 -1.70 -2.62 -0.49 -0.36(HIBOR) (-1.19) (-0.82) (-1.02) (-1.06) (-0.21) (-0.16)

Constant 83.61∗∗∗ 72.61∗∗∗ 72.90∗∗∗ 82.55∗∗∗ 69.58∗∗∗ 65.93∗∗∗

(6.07) (5.02) (5.07) (3.18) (2.98) (2.85)

Observations 185 185 185 82 82 82Adjusted R2 0.02 0.03 0.04 0.03 0.23 0.25

41

Table IXDeterminants of Neglected Risks Bias

This table reports the possible determinants of investors’ neglected risks bias. We run probit regression on investors’behavior of not taking risk profile assessment, which is a measure of neglected risks. The dependent variable “RiskProfile Not Assessed (Dummy)” equals to 1 if investor did not go through risk profile assessment before investing instructured products. Z statistics are in parentheses, *, ** and *** represent that p<0.1, p<0.05 and p<0.01, respectively.

Dependent Variable: Risk Profile Not Assessed (Dummy)(1) (2) (3) (4)

Above High School=0 0.37∗∗ 0.38∗∗

(2.35) (2.24)

Age 0.02∗ 0.01(1.88) (1.39)

Male -0.02 0.02(-0.12) (0.13)

Married -0.09 -0.05(-0.35) (-0.18)

Buy Lottery 0.32∗ 0.29(1.72) (1.54)

Own House -0.30 -0.20(-1.38) (-0.88)

Indebted 1.02∗∗∗ 1.04∗∗∗

(3.28) (3.29)

Hang Seng Index -0.01 -0.01Return (-1.57) (-1.03)

Interest Rate 0.08 0.10(HIBOR) (1.32) (1.50)

Constant -0.07 -0.67 -0.15 -1.03∗

(-0.64) (-1.27) (-0.78) (-1.79)

Observations 267 267 267 267Pseudo R2 0.02 0.05 0.01 0.07

42

Appendix IDefinition of Principal Variables

This table reports the definition the principal variables we use in the analysis. The data comes directly from the surveywe conducted from January 15 to June 18, 2009.

Variable Name Unit Definition

Asset Allocation CharacteristicsProportion in StructFin 0-100 Proportion of the investor’s asset invested in the structured product.Proportion in Saving 0-100 Proportion of the investor’s asset invested in the saving.Proportion in Bond 0-100 Proportion of the investor’s asset invested in the bond.Proportion in Equity 0-100 Proportion of the investor’s asset invested in the equity.

Neglected Risks Bias

Risk Profile Assessed=0 Dummy =1 if investor did not go through risk profile assessment before purchasing.Acquainted with Salesperson=0 Dummy =1 if the investor is not acquainted with the salesperson of the structured product.Engaging in Neighborhoods=0 Dummy =1 if the investor is not engaging in neighborhood activities.

Sell Side: Pricing Complexity

Cradit Rating Number The difference of the maximum and minimum of the reference obligation. Weconvert the ratings to numerical letters by AAA=9, AA+=8; AA=7, AA-=6, A+=5,A=4, A-=3, BBB+=2, BBB=1.

Foreign Currency Dummy =1 if the product is USD tranche or AUD tranche, instead of HKD tranche.Buy Side: Financial Literacy

Interest compounding Dummy =1 if the investor can do simple interest compounding rate calculation.Reasonable Stock Return Estimation Dummy =1 if the investor’s expectation to stock market return lies between 5.1% and 50%.Years of education Years =6, 12, or 16 if the investor has finished all or have some primary school education,

analogous for high school and college.Above High School Dummy =1 if the investor finished or finished some high school education.

Household CharacteristicsAge Years Age of the investor.Male Dummy =1 if the investor is male.Married Dummy =1 if the investor is married.Buy Lottery Dummy =1 if investor claims buying lottery more often than once half a year.Income HK$10,000 The investor family’s current monthly income.

Household CharacteristicsOwn House Dummy =1 if the investor owns house.HIBOR Number Hong Kong Inter-Bank Offer Rate at the issue date of the product.HSI Quarterly Return Number Hang Seng Index quarterly return at the issue date of the product.

43

Appendix IISample Comparison

The table compares demographic and financial background of investors in our survey sample and those in two majorsurveys in Hong Kong. The data for our sample were collected by questionnaire survey. We focus on Hong Kong investorswho had purchased credit-linked note or equity-linked note during February 2003 and May 2008. We conducted thesurvey from January 15 to June 18, 2009, and obtained 783 responses. One of the two compared surveys – “2006Population By-census” was conducted by Hong Kong Census and Statistics Department from July to August 2006,The second compared survey – “Retail Investor Survey 2009”, was conducted by Hong Kong Exchange and CleaningLimited from November to December 2009.

Panel A. Sample Characteristics

Variables Sample HK By-census 2006 HKEx 2009Demographics

Age (median) 58 45 45Male 0.37 0.47 0.46

Married 0.83 0.62 -Years of Education 10.14 10.06 -Above High School 0.59 0.30 0.66Above College 0.15 0.24 0.36

Financial RelatedMonthly Income(median, HK$10,000) 1.77 1.73 1.63Own House 0.82 0.53 -Buy Stock 0.40 - 0.36

Number of Observations 783 5,102,513 2,303

44

Appendix IIIDetailed Information of Credit-Linked Notes

This table shows the detailed information of two main credit-linked notes in our sample: Minibond and Constellation.“Hang Seng Index” is reported as of the issue date. “Fixed Deposit Rate” and “Current Deposit Rate” are reported asof the month before the issue date. In panel A, The second period interest rate for Minibond Series 11A is 8% minussix month LIBOR (LB), and 7.6% minus six month HIBOR (HB) for Minibond Series 11B. During the time we conductthe survey from January 2009 to June 2009, there are 28 series of Minibond and 40 series of Constellation outstandingin the market. In our sample, there are 464 Minibond investors and 80 Constellation investors.

Panel A: Minibond

SeriesNo.

IssueDate

#Investor(sample)

InterestRatePeriod1

InterestRatePeriod2

CurrencyMaturityDate

#Ref.Entity

MaxRating

MinRating

CouponFreq.

5 2003/7/2 3 3.8 - USD 2005/07/02 1 A- A- Semi-Ann6 2003/9/24 2 5 8 USD 2005/09/25 150 AA- A- Annually

7A 2003/12/3 3 4.2 - USD 2008/12/03 6 AA- BBB Semi-Ann7B 2003/12/3 10 4.2 - HKD 2008/12/03 6 AA- BBB Semi-Ann8 2004/3/3 0 7 - HKD 2009/03/03 5 A- BBB Semi-Ann

9A 2004/3/25 2 3.7 4.3 USD 2009/09/25 6 A+ A- Semi-Ann9B 2004/3/25 20 3.5 4.1 HKD 2009/09/25 6 A+ A- Semi-Ann10A 2004/5/28 4 4.25 4.75 USD 2009/11/28 7 A+ A- Semi-Ann10B 2004/5/28 17 4 4.5 HKD 2009/11/28 7 A+ A- Semi-Ann11A 2004/7/6 5 8 8 - LB USD 2010/01/06 1 A- A- Semi-Ann11B 2004/7/6 15 7.6 7.6 -HB HKD 2010/01/06 1 A- A- Semi-Ann12A 2004/9/8 6 4.65 5.4 USD 2010/03/08 6 A+ BBB Semi-Ann12B 2004/9/8 23 4.1 5.1 HKD 2010/03/08 6 A+ BBB Semi-Ann15A 2004/12/28 7 4.3 5 USD 2010/06/28 6 A+ BBB+ Semi-Ann15B 2004/12/28 8 3.3 4 HKD 2010/06/28 6 A+ BBB+ Semi-Ann16A 2005/2/7 10 4.2 4.75 USD 2010/08/07 6 A+ A- Semi-Ann16B 2005/2/7 10 3.2 3.75 HKD 2010/08/07 6 A+ A- Semi-Ann17A 2005/3/9 9 4.35 5 USD 2010/09/09 7 A+ A- Semi-Ann17B 2005/3/9 10 3.6 4.2 HKD 2010/09/09 7 A+ A- Semi-Ann18A 2005/4/6 6 4.5 5.5 USD 2010/10/06 7 AAA A- Semi-Ann18B 2005/4/6 9 3.7 4.7 HKD 2010/10/06 7 AAA A- Semi-Ann19A 2005/5/26 18 4.75 4.15 USD 2010/11/26 7 AA- A- Semi-Ann19B 2005/5/26 0 5.75 5.15 HKD 2010/11/26 7 AA- A- Semi-Ann20A 2005/7/20 3 4.8 6 USD 2011/01/20 7 A+ A- Quarterly20B 2005/7/20 3 4.2 5.4 HKD 2011/01/20 7 A+ A- Quarterly21A 2005/9/15 3 5.2 6.1 USD 2011/03/15 7 A+ A- Quarterly21B 2005/9/15 15 4.8 5.6 HKD 2011/03/15 7 A+ A- Quarterly22A 2005/11/25 1 4.65 5.65 USD 2011/05/25 7 AA- A- Quarterly22B 2005/11/25 2 4.4 5.4 HKD 2011/05/25 7 AA- A- Quarterly23A 2006/2/3 2 5.35 6 USD 2011/08/03 7 A+ A- Quarterly23B 2006/2/3 18 5.1 5.75 HKD 2011/08/03 7 A+ A- Quarterly25A 2006/4/26 1 5.5 6.5 USD 2011/10/26 7 AA- A- Quarterly25B 2006/4/26 11 5.3 6 HKD 2011/10/26 7 AA- A- Quarterly26A 2006/6/30 0 5.5 6.5 USD 2011/12/30 8 AA- A- Quarterly26B 2006/6/30 2 5.3 6 HKD 2011/12/30 8 AA- A- Quarterly27A 2006/9/15 10 7 8.3 USD 2009/09/15 7 A+ A+ Quarterly27B 2006/9/15 30 6.3 7.5 HKD 2009/09/15 7 A+ A+ Quarterly

(To be continued)

45

Appendix III-Continue

Panel A: Minibond

SeriesNo.

IssueDate

#Investor(sample)

InterestRatePeriod1

InterestRatePeriod2

CurrencyMaturityDate

#Ref.Entity

MaxRating

MinRating

CouponFreq.

28A 2006/10/27 9 6.5 8 USD 2009/10/27 7 A+ A Quarterly28B 2006/10/27 11 5.5 7 HKD 2009/10/27 7 A+ A Quarterly29A 2006/12/21 9 6 7.5 USD 2009/12/21 7 A+ A Quarterly29B 2006/12/21 10 5 6.5 HKD 2009/12/21 7 A+ A Quarterly30A 2007/01/31 2 6 7.5 USD 2010/02/01 7 AA- A Quarterly30B 2007/01/31 7 5 6.5 HKD 2010/02/01 7 AA- A Quarterly31A 2007/04/19 3 6 7.6 USD 2010/04/19 8 AA- A Quarterly31B 2007/04/19 8 5.5 7.1 HKD 2010/04/19 8 AA- A Quarterly32A 2007/07/16 1 6.1 7.8 USD 2010/07/16 8 AA- A Quarterly32B 2007/07/16 1 5.5 7.1 HKD 2010/07/16 8 AA- A Quarterly33A 2007/08/31 2 7 9.1 USD 2010/08/31 8 AA- A Quarterly33B 2007/08/31 12 6.3 8.1 HKD 2010/08/31 8 AA- A Quarterly34A 2008/01/07 16 6 - USD 2011/01/07 7 AA- BBB+ Quarterly34B 2008/01/07 50 5.6 - HKD 2011/01/07 7 AA- BBB+ Quarterly35A 2008/02/22 19 6 - USD 2011/02/22 7 AA A- Quarterly35B 2008/02/22 116 5.6 - HKD 2011/02/22 7 AA A- Quarterly36A 2008/05/15 14 5.5 - USD 2011/05/15 7 AA A- Quarterly36B 2008/05/15 49 5 - HKD 2011/05/15 7 AA A- Quarterly

(To be continued)

46

Appendix III-Continue

Panel B: Constellation

SeriesNo.

IssueDate

#Investor(sample)

InterestRatePeriod1

InterestRatePeriod2

CurrencyMaturityDate

#Ref.Entity

MaxRating

MinRating

CouponFreq.

34 2006/03/28 2 6 6.2 USD 2009/03/28 8 A+ BBB Quarterly35 2006/03/28 5 5.5 6 HKD 2009/03/28 8 A+ BBB Quarterly36 2006/03/28 0 5 5.2 USD 2008/03/28 8 A+ BBB Quarterly37 2006/03/28 9 4.5 5 HKD 2008/03/28 8 A+ BBB Quarterly39 2006/05/26 3 5.75 7 USD 2010/05/26 8 AA- BBB+ Quarterly40 2006/05/26 2 5.35 6.5 HKD 2010/05/26 8 AA- BBB+ Quarterly41 2006/05/26 0 4.5 5.5 USD 2008/05/26 8 AA- BBB+ Quarterly42 2006/05/26 1 4.1 5.1 HKD 2008/05/26 8 AA- BBB+ Quarterly43 2006/07/28 9 6.8 8 USD 2010/07/28 8 A+ BBB Quarterly44 2006/07/28 13 6.3 7.6 HKD 2010/07/28 8 A+ BBB Quarterly45 2006/07/28 3 5.5 6 USD 2009/10/28 8 A+ BBB Quarterly46 2006/07/28 2 5 5.5 HKD 2009/10/28 8 A+ BBB Quarterly47 2006/09/28 0 6.3 8 USD 2010/09/28 8 AA- BBB Quarterly48 2006/09/28 0 6 7 HKD 2010/09/28 8 AA- BBB Quarterly49 2006/09/28 0 5 6 USD 2009/03/28 8 AA- BBB Quarterly50 2006/09/28 1 4.75 5 HKD 2009/03/28 8 AA- BBB Quarterly55 2006/11/22 7 6.6 8 USD 2011/11/22 8 A A- Quarterly56 2006/11/22 6 6 6.3 HKD 2011/11/22 8 A A- Quarterly57 2006/11/22 13 6 7 USD 2010/05/22 8 A A- Quarterly58 2006/11/22 12 5.2 6 HKD 2010/05/22 8 A A- Quarterly59 2007/01/10 4 5.75 6.75 USD 2012/01/10 8 A+ BBB+ Quarterly60 2007/01/10 5 5 6 HKD 2012/01/10 8 A+ BBB+ Quarterly61 2007/01/10 1 5.1 6.1 USD 2010/07/10 8 A+ BBB+ Quarterly62 2007/01/10 0 4.5 5.25 HKD 2010/07/10 8 A+ BBB+ Quarterly63 2007/02/08 5 6.2 8 USD 2013/02/08 8 A+ BBB+ Monthly64 2007/02/08 2 5.2 6.8 HKD 2013/02/08 8 A+ BBB+ Monthly65 2007/02/08 2 5 5.5 USD 2010/02/08 8 A+ BBB+ Monthly66 2007/02/08 3 4 5 HKD 2010/02/08 8 A+ BBB+ Monthly67 2007/03/22 1 6.3 8.3 USD 2013/03/22 8 A+ A- Quarterly68 2007/03/22 0 5.6 7 HKD 2013/03/22 8 A+ A- Quarterly69 2007/03/22 0 5.6 6.6 USD 2011/03/22 8 A+ A- Quarterly70 2007/03/22 2 5 5.6 HKD 2011/03/22 8 A+ A- Quarterly71 2007/05/23 1 6.6 8.8 USD 2013/05/23 8 AA- A- Quarterly72 2007/05/23 2 6 8 HKD 2013/05/23 8 AA- A- Quarterly73 2007/05/23 0 5.6 6.8 USD 2011/05/23 8 AA- A- Quarterly74 2007/05/23 0 5.2 6 HKD 2011/05/23 8 AA- A- Quarterly78 2007/07/23 2 7 9 USD 2013/07/23 8 AA- A- Quarterly79 2007/07/23 0 6.5 8.5 HKD 2013/07/23 8 AA- A- Quarterly80 2007/07/23 0 6.2 7.3 USD 2011/07/23 8 AA- A- Quarterly81 2007/07/23 4 5.7 7.2 HKD 2011/07/23 8 AA- A- Quarterly

47