Prof. Christopher Yenkey Presentation to the AMEDA General Membership 26 April, 2012

Clearing and Settlement Data as a Tool for Strategic Planning: New Methods and Findings from Kenya’s CDSC

Prof. Christopher YenkeyPresentation to the AMEDA General Membership26 April, 2012

Research overviewInterdisciplinary approach:

Combining Sociology with Economics to deepen our understanding of how markets develop

Current research I’ll discuss today:

The power of CDS data for modeling market development

What attracts new investors to the market?

How do they learn to trade their shares over time? How is market performance affected by increased experience of the investing population?

Time permitting, I’ll discuss other emerging market research I’m involved with

Investor-level data taken from CDS records:

Timing of market entry (date of first share ownership) Trades (buys and sells in the secondary market) Broker/intermediary Location (Town of residence)

Merge with GIS databases to map each investor Name and mailing address removed to protect

confidentiality; account numbers can be altered to insure anonymity but allow tracking of individuals

Survey data provides context for the communities where investors live:

Town-level attributes are estimated from 3 recent high quality national surveys:

Local wealth: % of town that is high, medium, low wealth At-risk population (town population – poverty residents -

existing investors) Use of other financial products:

Bank accounts, credit cards, insurance, etc.

Exposure to IPO advertising campaigns: Partnered with market research firm (Synovate) to quantify

IPO advertising expenditures in each media outlet Gives a district-level measure of IPO advertising exposure

Part 1:

Who are your investors? What parts of your society have been mobilized into shareholding?

Growing investor Participation on the NSE: 93% of all Kenyan investors are new since 2006

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2% of all accounts are non-Kenyan

2% are Kenyans in the diaspora

96% of all CDS accounts are regis-tered to domestic Kenyans

The majority of Kenyan investors are individuals, with very few foreigners

Nov-04

Apr-05

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-07

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-08

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1,600,000Market Participation, by investor registration type

E.A. Company

E.A. Individual

Foreign Company

Foreign Individual

Kenyan Company

Kenyan Individual

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tal

inve

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rs

About 70% of market capitalization is domestically owned

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Market Capitalization, by investor registration type

E.A. Company

E.A. Individual

Foreign Company

Foreign Individual

Kenyan Company

Kenyan Individual

Bil

lio

ns

Ksh

, n

om

inal

CDSC-Kenya ushers in electronic trading in late 2004, followed by a policy shift toward liberalization

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CDSC-Kenya launched

Privatization Act (2005)

98% of new investors entered the NSEvia IPO subscription

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New

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ts,

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tal CD

S acco

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10 20 30 40 50 60 70 80 900

50,000

100,000

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200,000

Percentile

Am

ou

nt

inve

sted

(K

enya

n S

chil

lin

gs,

no

min

al) Pre-2006

investors(140,000)

Post-2006 new investors(1.4 million)

2 weeks wages @ 2 x poverty level

The new investing population is wide but thin, with smaller portfolio values

At passage of Privatization Act

Total Investors: 140,000

Total towns: 366

1990 1995 2000 20050200,000400,000600,000800,0001,000,0001,200,0001,400,0001,600,000

After all 7 IPOs

Total Investors: ~ 1.4 mill.(+ 900%)

Total towns: 563(+ 54%)

1990 1995 2000 20050200,000400,000600,000800,0001,000,0001,200,0001,400,0001,600,000

Investors are distributed similarly to the general population

Shareholding seems to be relatively more common in lower income areas

Measured as a portion of wealthy households, shareholding is less popular in the most wealthy districts

Of Kenya’s 68 districts, the most wealthy have some of the fewest investors per high income household

Rank District # CDS Accounts # High SES HHEstimated # of CDS

accounts per High SES HH

49 Mombasa 48,840 22,865 2.1450 Nyando 2,471 1,164 2.1251 Uasin Gishu 27,957 14,378 1.9452 Narok 2,446 1,282 1.9153 Homa Bay 1,642 886 1.8554 Nakuru 68,169 38,155 1.7955 Kiambu 44,913 25,605 1.7556 Laikipia 20,548 12,835 1.6057 Kwale 1,619 1,042 1.5558 Nyeri 52,466 35,771 1.4759 Thika 57,873 39,743 1.4660 Malindi 3,311 2,322 1.4361 Kisumu 12,908 9,795 1.3262 Nairobi 638,532 497,323 1.2863 Migori 2,467 3,279 .7564 Embu 14,728 23,194 .6365 Kajiado 10,747 18,854 .5766 Kilifi 2,339 4,888 .4867 Tana River 185 405 .4668 Marakwet 455 2,519 .18

Shareholding also tends to be more popular in districts where financial literacy is lower

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Districts with more potential investors but lower investor participation rates

Potential investors: Above poverty HH’s – # existing CDS accounts

CD

S a

cc

ou

nts

as

% o

f a

bo

ve

po

ve

rty

HH

's

Mombasa

MachakosNandi

Kiambu

Nakuru

This data can be used to identify regions where investor recruitment would be beneficial

0

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Districts with more potential investors and familiarity with formal financial products

Potential investors: Above poverty HH’s - # existing CDS accounts

CD

S a

cc

ou

nts

as

% o

f o

the

r fi

na

nc

ial p

rod

uc

t u

se

Machakos

Keiyo

MombasaNakuru

Kiambu

Additional investor recruitment opportunities in districts with higher financial literacy

Part 2:

How are investors recruited?

Using social networks to convey the benefits of share ownership to a larger portion of the society.

What draws investors into the market?

We already know that attributes of individuals and listing firms are highly influential:

Individuals: income, financial literacy, etc.

Firms: size, state-ownership, industry ( esp. telecom), advertising campaigns, etc.

What do we know about how existing investors recruit new investors? How do the experiences of existing investors influence the recruitment of new investors?

Experience tells us that positive performance attracts increased attention. But studying the role of social networks in conveying the benefits of share ownership uncovers a new source of legitimation:

How material information moves through the informal channels of a society influences investor recruitment and therefore market development.

How do the experiences of existing investors in earlier IPOs attract new investors in this IPO?

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Think of each town in Kenya as a point/station in the network; each of the stations can broadcast and receive “signal”.

Here, I model size of profits earned on earlier investments as the signal that each station in the network can send and receive.

Do we think that influence is a local phenomena (only the experiences of other town residents matters), or does information about prior experience in the stock market (gains and losses) travel from town to town through the network?

Estimating how profits earned by earlier investors influences new investor recruitment via informal social networks

For each town (i) in each IPO (t), number of new investors should be:

Firm-level fixed effects: captures size, industry, SOE vs. private, etc.

Town-level attributes: at risk population, wealth, use of other financial products, geographic remoteness, # of existing investors

Profit earned by town’s investors in last IPO: paper profits, total across all town investors

Profits earned in all other towns in last IPO: weighted by geographic proximity

N = 3,372 observations: 562 towns in 6 prior IPO periods.

A highly detailed yet conservative modelThe model predicts the number of new investors that enter the market in this town in this IPO as a function of:

“Control” variables: geographic remoteness (how far from the nearest major city), town residents’ wealth, experience with other financial products, ethnic composition, conditions in the country at the time (inflation, GDP change, etc.) and the characteristics of the IPO firm (size, state vs. private ownership, etc.), and offer terms of the IPO (share price, minimum buy-in, advertising)

“Explanatory” variables: profits earned in the town in the previous IPO, profits earned by investors in other nearby towns (if existing investors don’t talk to potential investors in other towns, there should be no effect)

Variable All towns Without Nairobi

SES high -12.6% -11.8%SES medium 8.1% 7.8%Distance to nearest major city -18.5% -17.1%Use of other financial products 15.5% 15.7%

Town profit in last IPO 5.2% 2.8%Social network profit in last IPO 17% 16.6%

Profits earned in nearby towns are highly influential in attracting new investors

Note: % increase in town’s new investors given a one standard deviation increase in the explanatory variable. All models are estimated with town-level control variables not shown here (town population, tribal populations, IPO advertising exposure, number of existing investors).

Profits are more influential than losses in recruiting new investors

Note: Dummy variable for gain vs. loss (t-1) interacted with both town and peer profit measures.

-60 -40 -20 0 20 40 600

0.5

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Relative Effects of Profit and Loss

Losing IPOs

Gaining IPOs

Profit earned in the network (10 Mill. Ksh; t-1)

Pre

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Remember the network metaphor: each town is a point in the network, surrounded by signals of profit

The network effect requires two complimentary stimuli: A signal to be broadcast, and a receptor that’s sensitive enough to receive that signal

The signal is the amount of profit earned in the last IPO (lots of profit = strong signal), but what local conditions might make the town more/less receptive to this signal?

Advertising moderates the effects of earlier gains and losses experienced by those around us

-30 -20 -10 0 10 20 30 40 50 60 700.5

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1M Ksh (low advert)

5M Ksh

15M Ksh

30m Ksh (high advert)

Note: Interaction term is significant at the .001 level; all other variables in model set to mean values.

The number of existing investors moderates social network influence

-80 -60 -40 -20 0 20 40 60 800

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Total peer profits (10 Mill. Ksh; t-1)

Pre

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tio

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w i

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es

tors

0 existing investors

2,000

1,000

Other community attributes that might moderate the recruitment of new investors

No. of existing investors in the town has a statistically significant but low magnitude moderating effect on profits of geographic peers.

Cell phone use strongly moderates the network effect: communities with higher rates of phone use are less influenced by their immediate neighbors (likely drawing information from longer distances)

Local wealth has no effect: communities across the SES spectrum are similarly affected

Use of other formal financial products has a small moderating effect, but falls just short of statistical significance (might be some reason to think that more financially literate areas are less reliant on/influenced by experiences of their neighbors, but the evidence falls short)

Kenyan society is characterized by a high degree of tribal diversity, with tribal groups clustered into localities

Source: Ethno-linguistic map of Kenya, courtesy of Kenyan mission to the United Nations

Variable All townsWithout Nairobi

SES high -11.2* -13*SES medium 7.7* -1Distance to nearest major city -18.6*** -27***Use of other financial products 15.6** 13.2*Town profit in last IPO 5.1*** 1.0Geographic peer profit (t-1) 15.1*** 10.0**Ethnic peer profit (t-1) 9.8** 12.1**

Profits earned by tribally peers are just as influential in attracting new investors

Note: % increase in town’s new investors given a one standard deviation increase in the explanatory variable. All models are estimated with town-level control variables not shown here (town population, tribal populations, IPO advertising exposure, number of existing investors).

Most Kenyan towns have a high concentration of a single, particular tribe

0%

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Concentration of town’s largest tribe

% o

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Tribally diverse - - - - - - - - - - - - - - - - - - - - - Tribally homogeneous

The average Kenyan town has 8.7 times more of some particular tribe than the national average

In a socially diverse community, profits in the previous IPO recruit many new investors

-20 -10 0 10 20 30 40 50 60 70 800.8

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Very low tribal con-centration

But less social diversity reduces the positive influence of profits earned by nearby investors

-20 -10 0 10 20 30 40 50 60 70 800.8

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1 (low tribe con-centration)

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The effect of geographic peers declines as the number of local shareholders increases, but the influence of tribal peers remains unchanged

Note: Estimates for subsample of all towns < 1,000 population- similar estimates result for towns < 10,000 population. Interaction term of no. investors and geo peer profit is significant at the .001 level; interaction with ethnic peer profit is not significant; all other variables in model set to mean values.

-50 -40 -30 -20 -10 0 10 20 30 40 500

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Geographic peer profits

1 SD

2 SD

3 SD

-2 -1.5 -1 -0.5 0 0.5 1 1.5 20

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Ethnic peer profits

1 SD

3 SD

Change in geographic peer influence Change in ethnic peer influence

Variable All towns W/out NairobiSES high 15.2 13.3SES medium 11 11.2Distance to nearest major city -21.8 -22.4Use of other financial products 8.8 8.3

Town profit in last IPO 0 2.7Geographic peer profit in last IPO 3.4 3.6Town scandal exposure -12.9 -3.8Peer scandal exposure -17.9 -17.8

Bad news also flows through the network: the negative effect of living close to investors affected by stockbroker scandals

“Peer scandal” is measured as the number of geographically proximate investors involved in one of two recent stockbroker scandals affecting approximately 135,000 investors: Francis Thuo (2005) and Nyaga (2008).

Consistent with the earlier network effects, all districts are affected by scandal, so bad news is often broadcast into the network

However, investors that are already in the market seem undeterred

ACCS KNREScandal Scandal

yes no yes no

Prev IPO?yes 13.4 6.8


no 3.8 2.4 no 18.5 15.7

SCOM COOPScandal Scandal

yes no yes no


Prev IPO?yes 13 10.6

no 37.6 34.7 no 2.4 1.9

Note: 2 x 2 tables showing the percentages of investors subscribing for each of four IPOs according to involvement in a stockbroker scandal and participation in the previous IPO

Summary of findings: the role of social networks in recruiting new investors

Net of characteristics of listing firms and individual’s ability to pay for shares:

1. The experiences of nearby investors in the previous IPO is more influential than wealth, financial literacy, or geographic location of the communities in which investors reside.

2. Positive experiences are more beneficial than negative experiences are detrimental.

3. Peers’ experiences become less influential in places with higher exposure to IPO adverting campaigns, higher cell phone use, and more existing investors.

4. The social network also transmits bad news: existing investors tell potential investors about scandals and poor performance.

Part 3:

How do new vs. experienced investors trade their shares?

How might a more experienced investing population affect future market performance?

Trading behaviors of different types of investors

Much research on investor trading behaviors according to “sophistication””

1. New vs. experienced

2. Low vs. high portfolio value

3. Retail vs. institutional

4. Rural vs. urban

The basic idea is that “unsophisticated” investors will under-recognize opportunities, but does this hold when we account for learning through experience?

0 50 100 150 200 250 3000

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30 Days

KEGN

SCAN

EVRD

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KNRE

SCOM

Trading days after IPO launches

Ind

exed

Sh

are

Pri

ceEarly price gains: who pays and who profits?

IPO trading volume is highest in early trading and declines over time for almost all IPOs

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ScanGroup

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AccessKenya

Kenya Re

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Early IPO share trading according to experience: first time investors are the most likely to speculate

KEGN SCAN EVRD ACCS KNRE0

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Predicted probability of early IPO selling, by investor experience

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Max Price

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The largest investors are by far the most likely to speculate in IPO shares


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Predicted probability of early IPO selling, by size of initial investment

Minimum Investors

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Small, inexperienced investors seem to learn to speculate like institutional investors


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Predicted probability of early IPO Selling,across investor ideal types

Individual, First In-vestment, Minimum Shares

Company, Third or more In-vestment, Institutio-nal Shares

Max Price

Pro

b.

of

sell

ing

in

1st

mo

nth

Ma

x s

ha

re p

rice

10.5x

2.9x

4.3x

4.9x

2.3x

Implications of learning processes on future market performance

IF high gains in IPO share trading drive market legitimacy, and…

IF these gains at least partly result from inexperienced investors, then…

WHAT happens to future market legitimacy when a larger portion of the investing population is more experienced?

Should we expect smaller peaks in share prices in early trading in future IPOs?

Can a 50% increase be as positive/desirable as 300%? Is a 30% gain enough to attract future investors?

Even the most sophisticated domestic investors seem to be vulnerable to the influence of high status shares when formulating trade strategies

More sophisticated investors take advantage of opportunities in the market

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Net Share Purchases in Early Trading: KenGen (1st IPO)

All foreign

Individuals

Companies

Price (index = 100)

Trading Days

Shar

es p

urch

ased

Indexed share price

Retail investors consistently underperform institutional investors when gains are highest

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-90,000-60,000-30,000

030,00060,00090,000

120,000

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30

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180

Eveready

Trading Days

Shar

es p

urch

ased

Indexed share price

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-200,000

-100,000

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100,000

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020406080100120140160180200

AccessKenya

Trading Days

Shar

es p

urch

ased

Indexed share price

0 5 10 15 20 25 30-1,000,000

-500,000

0

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1,000,000

0

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150

200Kenya Re

Trading Days

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es p

urch

ased

Indexed share price

LC LI Foreign

0 5 10 15 20 25 30-500,000

-300,000

-100,000

100,000

300,000

500,000

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50

150

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350

KenGen (1st IPO)

Trading Days

Shar

es p

urch

ased

Indexed share price

But the most sophisticated domestic investors are no less susceptible to biased expectations for high status

0 5 10 15 20 25 30-30,000,000

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Net Share Purchases in Early Trading: Safaricom

All foreignIndividualsCompaniesPrice (index = 100)

Trading Days

Shar

es p

urch

ased

Indexed share price

Part 4:

Additional research topics and plans for expansion.

Ongoing research questions1. Foreign investor participation as a stabilizing or destabilizing force

How do foreign vs. domestic investors react to domestic shocks (e.g. civil, political, macroeconomic instability)?

Are some foreign investors more tolerant of these shocks? Who recognizes the discounts available during shocks and who sells at the first sign of trouble?

Currently collecting data on home country of foreign investors- is there a difference in risk tolerance of foreign investors according

to other ties (economic, political, cultural) between the countries?

2. What diaspora investors contribute to the market

3. The role of trust in facilitating market participation: comparing the effects of scandals with price volatility on investors’ continued participation in the market.

Ideas for expanding the research program1. Are the lessons learned here (investor recruiting, market evolution, effects of

foreign vs. domestic participants, effects of scandals, etc.) only relevant in Kenya? Only in other African emerging markets? In all emerging markets?

2. The unique methodology developed here can be used to study other markets- methods that took years to develop in Kenya could be adapted relatively quickly to study other markets.

3. Expanding the research to include other AMEDA member markets can provide many benefits:

- Each market would receive analysis similar to what has been done in Kenya;

- It becomes possible to pool data across AMEDA markets to study trends in the region;

- A market development research group could be formed, where data analysis is performed at the Univ. of Chicago and results are shared at regular intervals (annually at AMEDA meetings, at workshops in Chicago, etc.)

- Expand the AMEDA learning platform and facilitate communication about best practices between members

Questions and comments are invited

Christopher [email protected]

Prof. Christopher Yenkey Presentation to the AMEDA General Membership 26 April, 2012

Documents

Transcript of Prof. Christopher Yenkey Presentation to the AMEDA General Membership 26 April, 2012