Emerging Capital Markets AG907 M.Sc. Investment & Finance - - PowerPoint PPT Presentation

Emerging Capital Markets AG907

M.Sc. Investment & Finance M.Sc. International Banking & Finance Lecture 3 – Efficiency & Other Financial Characteristics of Emerging Capital Markets

I g n a c i o R e q u e j o – G l a s g o w , 2 0 1 0 / 2 0 1 1

Overview of Lecture

• 1. Introduction
• 2. Market Information & Risk-Free Rate
• 3. Company-Specific Information
• 4. Evaluating Efficiency
• 5. Diversification, Return & Volatility

Empirical Evidence – Efficiency in African Stock Markets

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• 1. Introduction

Efficient markets ⇒ Security prices reflect all that is known about assets underlying those securities Belief in market efficiency is vital for interpretation of

• bserved prices

How much does the market know?

• Quality of information available
• How the market processes the information

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• 1. Introduction

Studies suggest in DMs quality of both information & processing is reasonably good Do the same conclusions hold for EMs? We need to evaluate

• Availability of market wide information
• Availability of company-specific information
• How such information is processed in EMs

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• 2. Market Information & Risk-free Rate

Difficulty in determining availability of market information & investment horizon Proxy for time span for which timely & reliable information is available ⇒ Term for which local government is able to borrow at fixed-interest rate a) Market in which government is able to borrow in the local currency at fixed rate for 20 years b) Market with only floating-rate debt

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• 2. Market Information & Risk-free Rate

Empirical evidence ⇒ Longest-maturity fixed-rate local- currency-denominated bond issued (period from January 2001 through March 2003) Of 33 EMs, only 17 had any fixed-rate offerings listed Only 14 of this subgroup had quotes for long-rated bond Does absence of fixed-rate market indicate high risk?

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• 2. Market Information & Risk-free Rate

Interpretation ⇒ Absence of borrowing at fixed rate by government that needs financing a) Yields on Colombian & Philippine fixed-rate debt are more than 15% & 13% b) Absence of tradable instruments in Argentina & Brazil Fixed-rate instruments allow investors to estimate risk-free rate of return in local currency ⇒ Baseline return for specific market fundamental to valuation process

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• 3. Company-Specific Information

Apart from general information ⇒ Availability & relevance

• f company-specific information is important (weak, semi-

strong & strong market efficiency) Variance of a firm’s stock price ⇒ Firm-specific vs. market a) If substantial company-specific information available ⇒ Larger amount of total variance due to firm factors b) If not much reliable firm-specific information ⇒ Only reliable information for market as a whole

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• 3. Company-Specific Information

Ranking of EMs according to proportion of stock variance attributable to the overall market (vs. 15% for NYSE)

• From a low of about 28% for South Africa
• To a high of 74% for Sri Lanka

Overall, less relevant firm-specific information available in EMs than DMs Surprises ⇒ Egypt & Jordan / Taiwan

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• 4. Evaluating Efficiency

Availability of market- & company-specific information is not sufficient ⇒ How information is used by the market? Whether and how information is incorporated into prices ⇒ Three levels of market efficiency

• Weak-form ⇒ past information; no information about

future prices; trends are ruled out; violation in markets with short time horizon investors

• Semi-strong-form

⇒ public information; prices incorporate new public information; abnormal returns can be obtained with private information

• Strong-form

⇒ all information (public & private); abnormal returns are not possible

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• 4. Evaluating Efficiency

Most DMs exhibit weak & semi-strong form efficiency

• Past prices do not predict future prices
• Prices adjust quickly to release of new information

Weak form market efficiency in EMs ⇒ test basic proposition that stock prices follow random walk (weak- form market efficiency in: Argentina, Brazil, Chile, China, India, Mexico, South Africa & Turkey) Another test ⇒ two-period-lagged autoregressive process to evaluate whether past returns predict current returns

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• 4. Evaluating Efficiency

Further perspective on efficiency in EMs

• Trading characteristics (‘Group 1 status’)
• Existence of fixed-rate instruments
• Relevance of firm-specific information
• Evidence on weak-form market efficiency

Highest-scoring ⇒ SK, SA, MEX, TAI, SLO, INDO & INDI Lowest-scoring ⇒ MOR, TUR, RUS & PAK

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• 4. Evaluating Efficiency

EMs do not seem to be efficient

• 1/2 long-term fixed-rate instrument
• Less company-specific information
• 1/2 weak-form market efficiency

Variation in efficiency among EMs Information on efficiency is relevant to decide valuation method

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• 5. Diversification, Return & Volatility

Two characteristics of EMs attractive for investors

• Low correlation with world equity markets
• Potential future growth in market cap

Low correlation ⇒ reduction

• f

6% points in total portfolio’s volatility adding EMs securities (Harvey, 1995) Potential growth in market cap of EMs

• In 1992 = 9% of world market cap & 19% of world GDP
• In 2002 = 10.5% of world market cap & 20% of world

GDP

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• 5. Diversification, Return & Volatility

In addition to low correlation EMs / DMs ⇒ Higher returns & volatility in EMs than DMs (Harvey, 1995) Changes in performance characteristics ⇒ Crises in EMs & increased economic & financial integration Four main factors contribute to higher volatility

• Asset

concentration ⇒ Diversification/concentration intrinsic in indices (vs. country’s industry mix)

• Stock market development & economic integration
• Market microstructure ⇒ Market liquidity & information

asymmetries

• Macroeconomic influences & political risk

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• 5. Diversification, Return & Volatility

After Asian crisis (1997) & Russian default (1998) ⇒ Correlation increased between S&P/IFCI Composite Index & S&P 500 & MSCI World indices Contagion ⇒ Spread of financial market turmoil from one country to the next, causing financial markets to move downward in synchronized fashion Financial contagion ⇒ Increases volatility in EMs & correlation with DMs

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• 5. Diversification, Return & Volatility

Factors that can augment the risk of contagion

• Weak economic fundamentals
• Macroeconomic similarities with other crisis countries
• Heavy exposure to certain financial agents
• Overall state of international financial markets

Technical definitions of contagion

• Shock

in

• ne

country transmitted to another (independent of previous correlation)

• How the shock is propagated (through trade, through

investor behaviour) ⇒ Only shock above & beyond ‘standard channels’ is contagion

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• 5. Diversification, Return & Volatility

Different types of contagion

• ‘Pure contagion’ ⇒ idea that financial shocks are

transmitted across markets & countries

• ‘Shift contagion’ ⇒ if correlation between the two

markets increases during crisis period Benefit

• f

international diversification diminishes if correlation between markets increases significantly during crises

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‘African stock markets: Multiple variance ratio tests of random walks’

Smith, G., Jefferis, K. and Ryoo, H.J. Applied Financial Economics 12 (2002) 475–484

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Scenario

• Increasing prominence of stock markets in EMs one

striking feature of international financial development

• Most important EMs in Latin America & Asia but

recently new stock markets in Africa

• Benefits of establishment of stock markets
• Attract portfolio investment
• Boost domestic savings
• Improve pricing & availability of capital for domestic

investment

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Scenario

• Role of stock markets in allocation / pricing of capital &

pricing of risk depends on efficiency level

• Common test for market efficiency ⇒ Whether price

follows a random walk

• Random

walk ⇒ Investors will be unable to consistently earn abnormal returns

• No random walk ⇒ Distortions in capital & risk pricing

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Main Objectives

• Provide overview of EMs in Africa
• Test random walk hypothesis in eight of largest African

stock exchanges

• Suggest factors that determine whether stock markets

follow random walk

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African Stock Markets

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African Stock Markets

• 15 formal stock markets in Africa at the end of 1997
• Four stock market categories
• Large size & sophistication

South Africa

• Medium-sized markets with long history

E.g., Egypt, Nigeria, Zimbabwe

• Small new markets with rapid growth

E.g., Botswana, Mauritius, Ghana

• Small new markets yet to take off

E.g., Swaziland, Zambia, Malawi

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African Stock Markets

• South Africa accounts for 82% of African stock market

capitalization ⇒ Large by world standards (market capitalization = US$232 billion at end of 1997)

• South Africa ⇒ Third largest EM & 16th largest equity

market in the world

• Remaining markets accounted for only 0.2% of world

stock market cap & 2.2% of EM cap

• All African markets lack liquidity

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Markets Covered in the Study

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Markets Covered in the Study

• Eight of nine largest stock markets in Africa (by

turnover)

• Five of them included in IFCG Index (end of 1997) but

very small weights in index (except South Africa)

• Three stock market groups represented
• South Africa
• Five

medium-sized markets: Egypt, Nigeria, Morocco, Zimbabwe & Kenya

• Two small new markets: Mauritius & Botswana

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Markets Covered in the Study

• Most African stock markets are relatively small
• But rapid increases in market cap & turnover ⇒

Turnover average annual growth rate = 46% (between 1988 & 1997) vs. 15% for all EMs

• Good performance in terms of returns but high volatility

levels

• Low correlations with other markets internationally

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Factors that Contributed to Growth

• Economic reform programmes
• Role of state in economy
• Role of private sector
• Greater role of market forces
• Price determination
• Allocation of both real & financial resources
• Financial sector reforms
• Establishment of new stock markets
• Improvements in environment
• Privatization programmes ⇒ New listings of shares

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Factors that Contributed to Growth

• Increased attention from international investors
• Growing size of African markets
• Potential for high returns
• Low correlations with other markets
• Barriers to entry progressively eased
• Liberalization of exchange controls
• Restrictions on foreign ownership relaxed
• Lack of liquidity remains a serious problem ⇒ Supply &

demand sides

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Market Specificities

South Africa

• Johannesburg Stock Exchange (JSE) established in

19th century to raise finance for gold mining ventures

• Large but illiquid ⇒ Large conglomerates linked to

mining companies or financial holdings

• Substantial inflows of foreign portfolio investment since

ending of apartheid

• No restrictions on ownership of shares by foreigners
• Restrictions to export capital for domestic investors
• Screen based electronic trading system since 1996
• Wide

range

• f

financial institutions, markets & information flows

• Concerns about insider trading

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Market Specificities

Egypt

• Egyptian Stock Exchange ⇒ Oldest in Africa & second

largest of study (market cap & turnover)

• Excessive bureaucracy & regulation ⇒ Illiquidity
• Recent moves towards deregulation & privatization
• Efficiency

constrained by daily 5% stock price fluctuation limit

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Market Specificities

Morocco

• Casablanca Stock Exchange relatively old
• Recent moves towards deregulation & privatization
• Low foreign participation
• Included in IFCI in February 1997

Zimbabwe

• Long-established market
• Opening to foreign investors & relaxation of exchange

controls

• But restrictions on foreign ownership ⇒ 10% & 40% of

company shares (individual- and collectively)

• High volatility ⇒ +143% (93), +66% (96), -53% (97)

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Market Specificities

Nigeria

• Nigerian Stock Exchange ⇒ Government bonds
• Restrictions on foreign ownership, political & economic

problems, low liquidity

• Privatization has helped stock market

Kenya

• Kenyan market ⇒ History similar to Zimbabwe
• Inconsistent reforms & political problems ⇒ Volatility

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Market Specificities

Mauritius

• Stock Exchange of Mauritius (SEM) ⇒ From 5 (1989)

to 40 (1997) listed companies

• Wide range of economic activities ⇒ Manufacturing,

tourism, sugar & finance

• Foreign investors play active role

Botswana

• Botswana Stock exchange ⇒ Smallest market of study
• From 5 (1989) to 12 (1997) listed companies
• Concentration in finance & services
• Restrictions on foreign ownership ⇒ 10% & 55% of

company stocks (individual- and collectively)

• More liquid than other larger African markets

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Methodology

• Multiple variance ratio tests
• Null hypothesis = Stock prices follow random walk
• VR (q) = 1 for all q = observation interval
• Lo and MacKinlay (1988) test statistics: Z (q) & Z* (q)
• Rejection of null hypothesis ⇒ No efficiency

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Results

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Results

• With present sample, 0.05 critical value is 2.49
• Example: Botswana
• Z (16) = 20.87 > 2.49 ⇒ Rejection of hypothesis
• Z* (2) = 23.73 > 2.49 ⇒ Rejection of hypothesis
• Random walk hypothesis rejected because of auto-

correlation of weekly increments in stock market index

• Rejection of random walk hypothesis for all markets,

except South Africa

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What Explains Difference of JSE

• Size ⇒ Large size of South African Stock Exchange
• Other stock exchanges around the world
• Small markets follow random walk ⇒ Argentina,

Indonesia, Turkey

• Large markets do not follow random walk ⇒ Hong

Kong, Korea, Mexico

• Liquidity ⇒ Cross-listing of South African corporations

(28% of JSE All-share capitalization)

• ‘Institutional maturity’ ⇒ sophistication, good quality

research, higher information flows, future contracts on indices, screen-based trading system, JSE market governance

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Conclusions

• Use of multiple variance ratio tests to examine random

walk hypothesis in 8 African stock markets

• In 7 (Botswana, Egypt, Kenya, Mauritius, Morocco,

Nigeria & Zimbabwe) the hypothesis is rejected ⇒ Returns are auto-correlated

• JSE All-share index follows a random walk
• Do markets approach a random walk as they become

more liquid & more institutionally mature?

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Recommended Readings

• The Economist (2009): ‘Efficiency and beyond’.

‘The efficient-markets hypothesis has underpinned many of the financial industry’s models for years. After the crash, what remains of it?’

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