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

Clearing and Settlement Data as a
Tool for Strategic Planning:
New Methods and Findings from
Kenya’s CDSC
Prof. Christopher Yenkey
Presentation to the AMEDA General Membership
26 April, 2012
Research overview
Interdisciplinary 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
1,600,000
2% of all accounts are non-Kenyan
1,400,000
1,200,000
96% of all CDS accounts are
registered to domestic Kenyans
1,000,000
800,000
600,000
400,000
200,000
0
Total CDS accounts
2% are Kenyans in the diaspora
The majority of Kenyan investors are
individuals, with very few foreigners
Market Participation, by investor registration type
Total investors
1,600,000
1,400,000
E.A. Company
1,200,000
1,000,000
E.A. Individual
800,000
Foreign Company
600,000
Foreign Individual
400,000
Kenyan Company
200,000
0
Kenyan Individual
About 70% of market capitalization is
domestically owned
Market Capitalization, by investor registration type
700
Billions Ksh, nominal
600
500
E.A. Company
E.A. Individual
400
Foreign Company
300
200
Foreign Individual
100
Kenyan Company
0
Kenyan Individual
CDSC-Kenya ushers in electronic trading in
late 2004, followed by a policy shift toward
liberalization
1,600,000
1,200,000
1,000,000
Privatization Act (2005)
800,000
600,000
CDSC-Kenya launched
400,000
200,000
0
Total CDS accounts
1,400,000
50,000
1,600,000
45,000
1,400,000
40,000
35,000
1,200,000
30,000
1,000,000
25,000
800,000
20,000
600,000
15,000
10,000
5,000
0
400,000
200,000
0
Total CDS accounts
New CDS accounts, daily
98% of new investors entered the NSE
via IPO subscription
The new investing population is wide but
thin, with smaller portfolio values
Amount invested
(Kenyan Schillings, nominal)
200,000
Pre-2006
investors
(140,000)
150,000
2 weeks
wages @ 2 x
poverty level
100,000
Post-2006
new investors
(1.4 million)
50,000
0
10
20
30
40
50
60
Percentile
70
80
90
At passage of
Privatization Act
Total Investors:
140,000
Total towns: 366
150
100
50
1990
1995
2000
2005
0
After all 7 IPOs
Total Investors:
~ 1.4 mill.
(+ 900%)
Total towns: 563
(+ 54%)
150
100
50
1990
1995
2000
2005
0
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
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
District
Mombasa
Nyando
Uasin Gishu
Narok
Homa Bay
Nakuru
Kiambu
Laikipia
Kwale
Nyeri
Thika
Malindi
Kisumu
Nairobi
Migori
Embu
Kajiado
Kilifi
Tana River
Marakwet
# CDS Accounts
# High SES HH
Estimated # of CDS
accounts per High SES HH
48,840
2,471
27,957
2,446
1,642
68,169
44,913
20,548
1,619
52,466
57,873
3,311
12,908
638,532
2,467
14,728
10,747
2,339
185
455
22,865
1,164
14,378
1,282
886
38,155
25,605
12,835
1,042
35,771
39,743
2,322
9,795
497,323
3,279
23,194
18,854
4,888
405
2,519
2.14
2.12
1.94
1.91
1.85
1.79
1.75
1.60
1.55
1.47
1.46
1.43
1.32
1.28
.75
.63
.57
.48
.46
.18
Shareholding also tends to be more popular
in districts where financial literacy is lower
This data can be used to identify regions
where investor recruitment would be beneficial
CDS accounts as % of above poverty HH's
Districts with more potential investors
but lower investor participation rates
90
80
70
60
50
40
Nakuru
30
20
Kiambu
10
Mombasa
Machakos
Nandi
0
0
50,000
100,000
150,000
200,000
Potential investors: Above poverty HH’s – # existing CDS accounts
Additional investor recruitment opportunities
in districts with higher financial literacy
Districts with more potential investors and
familiarity with formal financial products
45
CDS accounts as % of other
financial product use
40
Mombasa
35
Nakuru
Kiambu
30
25
20
15
10
Machakos
5
Keiyo
0
0
50,000
100,000
150,000
200,000
Potential investors: Above poverty HH’s - # existing CDS accounts
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.
50,000
1,600,000
45,000
1,400,000
40,000
35,000
1,200,000
30,000
1,000,000
25,000
800,000
20,000
600,000
15,000
10,000
5,000
0
400,000
200,000
0
Total CDS accounts
New CDS accounts, daily
How do the experiences of existing investors in
earlier IPOs attract new investors in this IPO?
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:
# 𝑵𝒆𝒘 𝑰𝒏𝒗𝒆𝒔𝒕𝒐𝒓𝒔𝒊𝒕 =
𝜶 + ∅𝑰𝑷𝑶𝒕 + 𝜷𝟏 𝑻𝒐𝒘𝒏 𝑻𝒓𝒂𝒊𝒕𝒔𝒊𝒕 + 𝜷𝟐 𝑻𝒐𝒘𝒏 𝝅𝒊𝒕−𝟏 + 𝜷𝟑 𝑮𝒆𝒐𝒈𝒓𝒂𝒑𝒉𝒊𝒄 𝑷𝒆𝒆𝒓 𝝅𝒌𝒕−𝟏 + 𝒆𝒊
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
N = 3,372 observations: 562 towns in 6 prior IPO periods.
Profits earned in all
other towns in last
IPO: weighted by
geographic proximity
A highly detailed yet conservative model
The 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)
Profits earned in nearby towns are highly
influential in attracting new investors
Variable
SES high
SES medium
Distance to nearest major city
Use of other financial products
All towns
-12.6%
8.1%
-18.5%
15.5%
Without
Nairobi
-11.8%
7.8%
-17.1%
15.7%
Town profit in last IPO
Social network profit in last IPO
5.2%
17%
2.8%
16.6%
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).
Predicted ratio of new investors
Profits are more influential than losses in
recruiting new investors
Relative Effects of Profit and Loss
2
1.5
1
Losing IPOs
Gaining IPOs
0.5
0
-60
-40
-20
0
20
40
60
Profit earned in the network (10 Mill. Ksh; t-1)
Note: Dummy variable for gain vs. loss (t-1) interacted with both town
and peer profit measures.
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?
Predicted ratio of new investors
Advertising moderates the effects of earlier gains
and losses experienced by those around us
2.5
1M Ksh
(low advert)
2
5M Ksh
15M Ksh
1.5
30m Ksh
(high advert)
1
0.5
-30
-20
-10
0
10
20
30
40
50
60
Profit earned in the network (10 Mill. Ksh; t-1)
Note: Interaction term is significant at the .001 level; all other
variables in model set to mean values.
70
The number of existing investors moderates social
network influence
Predicted ratio of new investors
2
0 existing
investors
1,000
1.5
2,000
1
0.5
0
-80
-60
-40
-20
0
20
40
Total peer profits (10 Mill. Ksh; t-1)
60
80
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
Profits earned by tribally peers are just as
influential in attracting new investors
Variable
SES high
SES medium
Distance to nearest major city
Use of other financial products
Town profit in last IPO
Geographic peer profit (t-1)
Ethnic peer profit (t-1)
All towns
-11.2*
7.7*
-18.6***
15.6**
5.1***
15.1***
9.8**
Without
Nairobi
-13*
-1
-27***
13.2*
1.0
10.0**
12.1**
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
16%
% of observations
14%
The average
Kenyan town
has 8.7 times
more of some
particular tribe
than the national
average
12%
10%
8%
6%
4%
2%
0%
0
5
10
15
20
Tribally diverse - - - - - - - - - - - - - - - - - - - - - Tribally homogeneous
Concentration of town’s largest tribe
34
Predicted ratio of new investors
In a socially diverse community, profits in the
previous IPO recruit many new investors
2.2
2
1.8
Very low tribal
concentration
1.6
1.4
1.2
1
0.8
-20
-10
0
10
20
30
40
50
60
70
Profit earned in the network (10 Mill. Ksh; t-1)
80
Predicted ratio of new investors
But less social diversity reduces the positive
influence of profits earned by nearby investors
2.2
1 (low tribe
concentration)
2
1.8
5
1.6
1.4
10
1.2
15 (high tribe
concentration)
1
0.8
-20
-10
0
10
20
30
40
50
60
70
Profit earned in the network (10 Mill. Ksh; t-1)
80
The effect of geographic peers declines as the
number of local shareholders increases, but the
influence of tribal peers remains unchanged
Change in geographic peer influence
Change in ethnic peer influence
1.6
1.6
1 SD
1.4
2 SD
1.2
1
3 SD
1.2
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0
-40
-30
-20 -10
0
10
20
Geographic peer profits
30
40
50
3 SD
1
0.8
-50
1 SD
1.4
0
-2
-1.5
-1
-0.5
0
0.5
Ethnic peer profits
1
1.5
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.
2
Bad news also flows through the network: the
negative effect of living close to investors
affected by stockbroker scandals
Variable
SES high
SES medium
Distance to nearest major city
Use of other financial products
Town profit in last IPO
Geographic peer profit in last IPO
Town scandal exposure
Peer scandal exposure
All towns
15.2
11
-21.8
8.8
W/out Nairobi
13.3
11.2
-22.4
8.3
0
3.4
-12.9
-17.9
2.7
3.6
-3.8
-17.8
“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
Prev IPO?
yes
no
Scandal
yes
no
13.4
6.8
3.8
2.4
KNRE
Prev IPO?
SCOM
Prev IPO?
yes
no
Scandal
yes
no
58.3
53.6
37.6
34.7
yes
no
Scandal
yes
no
67.1
60.9
18.5
15.7
COOP
Prev IPO?
yes
no
Scandal
yes
no
13
10.6
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?
Early price gains: who pays and who profits?
400
Indexed Share Price
350
30 Days
300
KEGN
250
SCAN
200
EVRD
150
ACCS
KNRE
100
SCOM
50
0
0
50
100
150
200
250
Trading days after IPO launches
300
IPO trading volume is highest in early trading and
declines over time for almost all IPOs
Trading Volume as % of Total
Shares Floated
14
12
KenGen
10
ScanGroup
8
Eveready
6
AccessKenya
4
Kenya Re
2
0
1
3
5
7
9
11 13 15 17 19 21 23
Month after IPO launch
Early IPO share trading according to experience:
first time investors are the most likely to speculate
0.07
400
0.06
350
0.05
300
250
0.04
200
0.03
150
0.02
100
0.01
50
0
0
KEGN
SCAN
EVRD
ACCS
KNRE
First
Investment
Max. share price
Prob. of selling in 1st month
Predicted probability of early IPO selling,
by investor experience
Second
Investment
Third or
More
Investment
Max Price
The largest investors are by far the most likely to
speculate in IPO shares
0.4
400
0.35
350
0.3
300
0.25
250
0.2
200
0.15
150
0.1
100
0.05
50
0
0
KEGN SCAN
EVRD
ACCS KNRE
Minimum
Investors
Max. share price
Prob. of selling in 1st month
Predicted probability of early IPO selling,
by size of initial investment
Medium
Investors
Large
Investors
Institutional
Investors
Max Price
Small, inexperienced investors seem to learn to
speculate like institutional investors
0.25
Individual,
First
Investment,
Minimum
Shares
400
10.5x
4.9x
350
0.2
300
2.9x
250
0.15
200
4.3x
0.1
2.3x
150
100
0.05
50
0
0
KEGN SCAN
EVRD
ACCS
KNRE
Max share price
Prob. of selling in 1st month
Predicted probability of early IPO Selling,
across investor ideal types
Company,
Third or more
Investment,
Institutional
Shares
Max Price
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
Net Share Purchases in Early Trading: KenGen (1st IPO)
500,000
350
400,000
300
300,000
250
200
100,000
0
150
-100,000
100
-200,000
50
-300,000
0
-400,000
-500,000
-50
0
5
10
15
Trading Days
20
25
30
Indexed share price
Shares purchased
200,000
All foreign
Individuals
Companies
Price (index = 100)
Retail investors consistently underperform
institutional investors when gains are highest
Eveready
250
150
50
-50
0
5
10
15
20
25
Shares purchased
350
30
120,000
90,000
60,000
30,000
0
-30,000
-60,000
-90,000
-120,000
180
130
80
30
-20
0
Trading Days
Indexed share price
500,000
400,000
300,000
200,000
100,000
0
-100,000
-200,000
-300,000
-400,000
-500,000
Indexed share price
Shares purchased
KenGen (1st IPO)
10
20
30
Trading Days
AccessKenya
Kenya Re
150
100,000
0
100
-100,000
50
-200,000
-300,000
0
0
10
20
Trading Days
LC LI Foreign
30
1,000,000
200
500,000
150
0
100
-500,000
50
-1,000,000
0
0
10
20
Trading Days
30
Indexed share price
200,000
Shares purchased
200
Indexed share price
Shares purchased
300,000
But the most sophisticated domestic
investors are no less susceptible to biased
expectations for high status
30,000,000
160
20,000,000
140
10,000,000
120
0
100
-10,000,000
80
-20,000,000
60
-30,000,000
40
0
5
10
15
Trading Days
20
25
30
Indexed share price
Shares purchased
Net Share Purchases in Early Trading: Safaricom
All foreign
Individuals
Companies
Price (index = 100)
Part 4:
Additional research topics and
plans for expansion.
Ongoing research questions
1. 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 program
1.
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 marketsmethods 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 Yenkey
[email protected]