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Chapter 3. Are There Crouching Tigers and Hidden Dragons in Asia’s Stock Markets?

Author(s):
Ratna Sahay, Cheng Lim, Chikahisa Sumi, James Walsh, and Jerald Schiff
Published Date:
August 2015
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Author(s)
Fabian Lipinsky and Li Lian Ong 

Main Points of this Chapter

  • Asia’s stock markets tend to be more influenced by idiosyncratic factors than do the stock markets of the Group of Seven (G7)1 countries, beyond the systematic factors and local fundamentals that are identified in the analysis.

  • There is a significant relationship between the strength of implementation of securities regulations and the “noise” in stock pricing.

  • Improvements in the regulation of securities markets in Asia could enhance the role of stock markets as stable and reliable sources of financing into the future.

Introduction

The Chinese proverb “crouching tiger hidden dragon” is an apt description of Asia’s stock markets today. It refers to the mysteries or undiscovered potential that lie beneath the surface, appropriately capturing the stage of development of the region’s stock markets. These stock markets continue to be a key source of financing for local firms, but their potential is yet to be fully realized. They are an important destination for foreign investment, yet some are seen as somewhat opaque and idiosyncratic.

The role of Asia’s stock markets as important drivers of growth in the region is underappreciated. Though not widely known, the share of stock market capitalization as a percentage of GDP in most Asian countries is comparable to that of their total banking sector assets, with debt securities markets coming in a distant third (Figure 3.1). This contrasts with developments in many advanced economies, where banking sectors continue to dominate financial intermediation (see Chapter 1). Statistics published by the World Federation of Exchanges illustrate the breadth and depth of Asia’s stock markets. For example, they show the following:

Figure 3.1Asia and Pacific (Excluding Japan), the Group of Seven, and the Rest of the World: Structure of the Financial Sector at End-2012

(Percent of GDP)

Sources: Bank for International Settlements; Bloomberg, L.P.; Haver Analytics; and IMF staff calculations.

1 The Hong Kong SAR column is truncated for presentation purposes, given the relatively large size of its financial system, with stock market capitalization amounting to almost 13 times GDP and outstanding debt securities issued domestically amounting to 54 percent of GDP at end-2012.

  • Equity issuances have been an important source of financing in many Asian countries. New capital raised by stock issuance in Asia in 2012 amounted to $198 billion, compared with $234 billion in the Americas and $102 billion in Europe, the Middle East, and Africa (EMEA) combined.

  • With a capitalization of almost $15 trillion, Asia’s stock market capitalization is about equivalent in value to that of EMEA (Table 3.1).

  • Almost 20,000 companies were listed on Asia’s stock markets at end-2012, slightly less than the rest of the world combined. More than 10,000 were listed in the Americas, and 13,300 were listed in EMEA.

  • Market liquidity in 2012, measured as the ratio of total turnover to average capitalization, was 0.9 for Asia, compared with 1.2 for the Americas and 0.65 for EMEA.

Table 3.1Stock Markets around the World: Capitalization at End-2012
RegionCountryExchangeAmount

(Millions of

U.S. dollars)
Share of

Region

(Percent)
Share of

World

(Percent)
Americas23,193,460100.042.5
BrazilBM & FBOVESPA1,227,4475.32.2
CanadaTMX Group2,058,8398.93.8
ChileSantiago SE313,3251.40.6
ColombiaColombia SE262,1011.10.5
MexicoMexican Exchange525,0572.31.0
PeruLima SE102,6170.40.2
United StatesNASDAQ OMX14,582,38919.88.4
NYSE Euronext (US)14,085,94460.725.8
Others35,7420.20.1
Asia and Pacific216,928,860100.031.0
AustraliaAustralian SE1,386,8748.22.5
ChinaShanghai SE2,547,20415.04.7
Shenzhen SE1,150,1726.82.1
Hong KongHong Kong Exchanges2,831,94616.75.2
IndiaBSE India1,263,3357.52.3
National Stock Exchange India1,234,4927.32.3
IndonesiaIndonesia SE428,2232.50.8
JapanOsaka SE202,1511.20.4
Tokyo SE Group3,478,83220.56.4
KoreaKorea Exchange31,179,4197.02.2
MalaysiaBursa Malaysia466,5882.80.9
PhilippinesPhilippine SE229,3171.40.4
SingaporeSingapore Exchange4765,0784.51.4
ThailandThe Stock Exchange of Thailand389,7562.30.7
Others812,1164.81.5
EMEA14,447,481100.026.5
GermanyDeutsche Börse1,486,31510.32.7
United Kingdom and ItalyLondon SE Group3,396,50523.56.2
Includes FranceNYSE Euronext (Europe)2,832,18919.65.2
Others6,732,47346.612.3
World54,569,801
Source: World Federation of Exchanges.Note: EMEA = Europe, the Middle East, and Africa; NYSE = New York Stock Exchange; SE = stock exchange

NASDAQ OMX Nordic Exchange: OMX includes Copenhagen, Helsinki, Iceland, Stockholm, Tallinn, Riga and Vilnius Stock Exchanges.

Total for Asia and Pacific excludes Osaka and National Stock Exchange of India to avoid double counting with Tokyo and Bombay SE, respectively.

Korea Exchange: includes Kosdaq market data.

Singapore Exchange: market capitalization includes domestic listings and a substantial number of foreign listings, defined as companies whose principal place of business is outside of Singapore. Inactive secondary foreign listings are excluded.

Asia’s stock markets have also become more integrated with the international financial system. Foreign investment in many of the region’s stock markets has grown since the Asian financial crisis. Investment has grown exponentially in some of the larger markets, which resumed their expansions following the sharp retrenchment during the global financial crisis (Figure 3.2). Meanwhile, cross-listings, including depository receipts from within the region and elsewhere, also expanded as companies sought to tap the region’s liquidity. The number of foreign listings in Asia’s stock markets tripled between 2002 and 2012, though they are still low, at about 2 percent of the total. This amount compares with 10 percent each in the Americas and EMEA. In turn, emerging market firms, including those from Asia, have sought to access more developed capital markets to benefit from the lower cost of capital, higher valuations, enhanced investor recognition, and better corporate governance, among other reasons.

Figure 3.2Asia and Pacific (Excluding Japan): Outstanding Foreign Investment in Equity Securities, 1997–2012

(Millions of U.S. dollars)

Source: IMF staff calculations.

Note: Broken lines denote interpolation of data.

Although Asia’s stock markets have been an important source of funding for the region, their full potential remains to be exploited. One possible reason is the perception that pricing of Asian stocks is more idiosyncratic in nature:

  • Speculative activity rather than economic and corporate fundamentals are seen to be driving prices in some of these stock markets. Researchers and the financial press often ascribe sharp drops in Asia’s stock markets to the bursting of speculative bubbles (Samuelson 1994; Nam, Park, and Kim 1999), with some of the more recent literature on the topic providing support for this view (Hanim Mokhtar, Nassir, and Hassan 2006; Mei, Scheinkman, and Xiong 2009; Homm and Breitung 2012). Anecdotal evidence suggests that such perceptions of the region’s stock markets have prevailed despite analyses showing that the findings are not exclusive to Asia. Evidence of speculative bubbles has also been found in stock prices of advanced economies (West 1987; Homm and Breitung 2012).

  • Other related research suggests that the variation in stock returns is larger in emerging markets, appears unrelated to comovement of fundamentals, and is therefore consistent with “noise trading” (Kim and Shamsuddin 2008).2 In addition to macroeconomic conditions, the literature also contemplates the importance of institutional quality—such as political, legal, regulatory, and governance considerations—for the development of Asia’s capital markets (for example, Yartey 2010; Law and Habibullah 2009; Cherif and Gazdar 2010). The evidence also suggests that pricing efficiency in Asian stock markets depends on the level of development as well as on the regulatory framework for transparent corporate governance (Kim and Shamsuddin 2008).

This chapter analyzes the pricing of Asia’s stock markets to determine the validity of some of these long-held views. The analysis draws on asset pricing and economic theory, as well as on empirical evidence from the accounting literature. It examines the extent to which well-established systematic international factors and domestic fundamentals influence Asian stock markets as opposed to “idiosyncratic” factors. Specifically, the model does the following:

  • It incorporates (1) international factors common to the universe of assets across national boundaries, such as global and regional risks; and (2) domestic economic and financial fundamentals, such as the local business cycle and the financial performance of the corporate sector, to extract the idiosyncratic component in stock returns.

  • It subsequently tests for the relationship between this idiosyncratic component and the strength of implementation of securities regulations to represent the role of institutional quality in the pricing of stocks.

The findings corroborate the existing literature on stock market pricing. We find evidence of greater idiosyncratic influences in Asia-Pacific stock markets than in their G7 counterparts, beyond the systematic international factors and local economic and financial fundamentals, which are identified in the analysis. The influence of these international and local factors appears to vary with time, and was most significant during the global financial crisis, when regional developments became the most important factors. Among local factors, forecast earnings appeared to carry the most information for stock pricing, and markedly so during the global financial crisis. These results suggest that investors may have been seeking more guidance from fundamentals in their pricing decisions during the crisis, in both emerging and advanced economy markets. Separately, asset allocation decisions by foreign investors also appear to have affected stock market volatility and returns in both groups of countries.

We also find a direct and significant connection between market regulation and the importance of idiosyncratic factors. Countries that are better at implementing internationally accepted principles of securities regulation tend to be less subject to idiosyncratic influences in the pricing of their stocks. Thus, improvements in the regulation of securities markets in Asia would likely strengthen investor confidence by ensuring that these markets are operated efficiently and fairly. In turn, this would enhance the role of local stock markets as attractive investment destinations and, thus, as reliable sources of financing. That said, we acknowledge that some of the noise in stock returns may be attributable to other country-specific factors that are not captured in the model.

Data and Stylized Facts

The countries in the sample comprise the main emerging market and advanced economies in the Asia-Pacific region (excluding Japan), benchmarked against the G7 countries. The sample countries comprise China, Hong Kong SAR, and Korea (in Northeast Asia); and Indonesia, Malaysia, the Philippines, Singapore, and Thailand (in Southeast Asia); plus Australia and India. The weekly market and earnings data are obtained from Bloomberg and Thomson Reuters I/B/E/S on Datastream. Market capitalization statistics are from Bloomberg and the World Federation of Exchanges (WFE), while annual data on foreign investment and periodic (confidential) information on regulatory implementation are sourced from the IMF.

Broadly speaking, diversified world and regional portfolios have not been the most optimal investments, ex post. In other words, they have not been meanvariance efficient at the asset-allocation-efficient frontier, relative to some individual stock markets (Figure 3.3). For example,

Figure 3.3Stock Markets around the World: Mean-Variance Analysis of Country, Regional, and World Returns, March 1998-November 2012

(Weekly U.S. dollar returns, percent)

Source: IMF staff calculations.

  • Asia’s stock markets have generally yielded better returns over time relative to their G7 counterparts, but have tended to be more volatile.

  • Asia’s markets underperformed the G7 during the Asian financial crisis of the late 1990s based on the average risk-return trade-off. Excluding the global financial crisis, they recorded their highest volatility and lowest returns during this period.

  • The “peacetime” period immediately before the global financial crisis was the most rewarding for investors. Markets posted their highest returns and were among the least volatile.

  • All regions recorded their worst performance during the global financial crisis. Most stock markets posted negative returns (except for India, Malaysia, the Philippines, and Thailand), and many experienced their greatest volatility.

The empirical evidence indicates the following:

  • Stock markets have become more integrated. As a simple proxy, return correlations between individual and regional stock markets and between individual and world stock markets trended upward during the 15 years to 2012. These correlations tend to be lower for the emerging Asia-Pacific markets compared with the advanced economies in the region and the G7 countries.

  • Foreign investors play a role in influencing stock market volatility and returns. While there is little relationship between the share of foreign holdings in a country’s stock markets and the volatility of returns, asset allocation decisions matter. The analysis finds no relationship between foreign investment in equities as a proportion of average stock market capitalization and the volatility of weekly stock market returns for either the G7 or the Asia-Pacific countries during 2001–12. However, pullout by foreign investors, especially during the global financial crisis period, tended to exacerbate market volatility.

  • Asset allocation decisions by foreign investors have a clear impact on stock market returns. On average, stock market returns tend to be higher in markets with a higher share of foreign holdings, albeit less obviously so among the G7 markets. For both groups of countries, stock market returns exhibit a strong positive relationship with net foreign portfolio flows.

Method

The existing literature suggests that the stock market returns of a country are dependent on local and international factors. We applied Ross’s (1976) arbitrage pricing theory in our modeling of individual stock market returns as a factor model in the following generalized form:3

in which

Rc,t represents the stock market return for country c at time t.

BCc,t represents the business cycle for country c at time t.

EPSc,tf is the one-year-ahead forecast corporate sector earnings per share (EPS) for country c at time t.

EPSc,ta is the actual (realized) corporate sector EPS for country c at time t.

Rw,t is the world stock index return at time t.

Rr,t is the regional stock index return at time t.

ec is the idiosyncratic error term, and all the variables are expressed in local currency terms.

In an efficient market, stock prices respond very quickly to incorporate all relevant publicly available information (Fama 1970). Thus, Rc should reflect the information contained in the right-hand-side variables, with ec representing relevant pricing information that is not captured in the model. Unfortunately, we are not able to include other emerging market regions for comparison purposes because requisite market data are not available.

Analysis

The Pricing of Stocks

We run preliminary regressions to determine the most parsimonious form for the relationship between stock market returns and the explanatory variables. First, we apply equation (3.1) to Dataset 1, which comprises all independent variables for all countries for July 2005–November 2012. The results show that the systemic regional factor is the most important explanatory variable. Forecast EPS was significant for more countries during the global financial crisis. However, two of the local factors—business cycle and realized EPS—are largely insignificant in explaining stock market returns. The general lack of explanatory power of these variables could mean that much of the related information content may already have been captured by the forecast EPS variable. We also confirm that the individual stock market returns are generally not autocorrelated and are stationary, according to our respective Durbin-Watson and unit root tests. The regression residuals are homoscedastic, according to the White’s test results.

Next, we apply equation (3.1) to Dataset 2, which comprises all independent variables for a subset of countries for March 1998–November 2012. The results show that the systemic regional factor remained the most important factor through the extended period. As it was for the global financial crisis period, forecast EPS was significant for more countries during the Asian financial crisis period, suggesting that investors may look for more market guidance during periods of stress.

Based on these findings, we reduce the form of equation (3.1) by omitting two of the largely insignificant local factors. In doing so, we are able to include all countries for the full March 1998–November 2012 period. In this version of the model, the stock market return of a country, c, is generated by a factor model comprising one local factor (forecast EPS) and the two international (world and regional) factors. This relationship is represented as follows:

in which

EPSc,tf is the one-year-ahead forecast corporate sector EPS for country c at time t.

Rw,t and Rr,t are the world and regional stock index returns, respectively, at time t.

ec is the idiosyncratic error term, and all the variables are expressed in local currency terms.

The application of Equation 3.2 allows us to use Dataset 3, which covers the full March 1998–November 2012 period. The results suggest that although the pricing of Asian stock markets may be more idiosyncratic, the general trends over time are similar to those seen in Asia’s G7 counterparts (Annex Table 3.1.1). For example,

  • The findings corroborate the existing literature, which shows that stock market returns in emerging markets are less rehted to fundamentals and more influenced by idiosyncratic factors. On average, the adjusted R2 for Asia’s stock markets is much lower than it is for the G7 countries, while the average standard error for Asia is larger than that of the G7 countries by several multiples. Correspondingly, the more developed stock markets in the region (Australia, Hong Kong SAR, Korea, and Singapore) typically show higher adjusted R2 than that of their regional peers. It is more in line with that of the G7 markets.

  • In general, the influence of international factors on Asia’s stock markets has become more significant over time, underscoring the increasing integration across borders. The systematic regional factors have been relatively more dominant than the world factor at any point in time, supporting the empirical evidence of greater intraregional activity; the importance of regional factors has also increased over time. This outcome is consistent with what has been seen in the G7 markets for some time, where regional factors have consistently been the main pricing influence for stock markets. Within Asia, China’s stock markets’ growing openness to international influences stands out; all four markets were largely unaffected by world events until the global financial crisis, but have become significantly affected by global developments since its onset.

  • Local developments have been relatively less important in the pricing of Asia’s stock markets than world and regional influences. Any information conveyed by changes in anticipated corporate earnings appears to have had little influence on stock prices in general, both during the Asian financial crisis and “peacetime” periods. This is consistent with the empirical literature, which shows evidence of greater pricing inefficiency during the Asian financial crisis owing to the chaotic financial environment (Lim, Brooks, and Kim 2008). However, this trend changed during the global financial crisis, with information imparted by forecast earnings becoming significant for many more markets. The implication is that investors may be relying more on expert forecasts for guidance during volatile times. The trend is similar for the G7 stock markets.

  • There are few similarities in the pricing of stock markets between the Asian financial crisis period and the global financial crisis period, except for the common lack of “pricing errors” (possible abnormal returns). Pricing errors reflect, in part, returns that are not accounted for by systematic factors, fundamentals, or idiosyncratic influences. They are captured in the intercept term in equation (3.2). Pricing errors have been significantly different from zero for several Asian markets during “peacetime,” most notably for China and India, suggesting that abnormal returns may not have been arbitraged away in the relatively more insulated markets.

The Role of Regulation

The existing empirical evidence points to the importance of institutional factors in the pricing of stocks. In this context, Hsieh and Nieh (2010) argue that certain improvements are needed in Asian countries before greater international financial integration is possible, integration that would lead to the realization of potential benefits of scale, capacity, and liquidity. According to Hsieh and Nieh, areas in need of improvement include regulation, corporate governance, products, and market infrastructure.

We first test for the relationship between the overall strength of regulation and the extent of idiosyncratic influences on stock pricing. We use the International Organization of Securities Commissions’ Objectives and Principles of Securities Regulation assessments (IOSCO 2003, 2011), conducted during the IMF’s Financial Sector Assessment Program (FSAP) missions to the countries in the sample, as a proxy for the strength of securities regulation. Given the infrequency of IOSCO assessments across countries, we regress the standard error of regression from the results of equation (3.2) on individual countries’ average IOSCO ratings (see Box 3.1) from the corresponding period, between 2000 and 2012:

in which Se,c,t is the standard error of regression for country c at time t; and IOSCOc,t is the average IOSCO rating for country c at time t.

Box 3.1.Deriving a Measure of Effective Securities Regulation

The International Organization of Securities Commissions (IOSCO) is the leading international group of securities market regulators. Its membership comprises regulatory bodies from more than 100 jurisdictions. Each body has day-to-day responsibility for securities regulation and administration of securities laws. The IOSCO Objectives and Principles of Securities Regulation (“Principles”) sets out a broad general framework for the regulation of securities. Their core aims are to protect investors; ensure that markets are fair, efficient, and transparent; and reduce systemic risk. The scope of the principles includes the regulation of

  • Securities markets

  • The intermediaries that operate in those markets

  • The issuers of securities

  • The entities offering investors analytical or evaluative services such as credit rating agencies

  • The sale of interests in, and the management and operation of, collective investment schemes.

The methodology for assessing implementation of the IOSCO Principles is designed to provide IOSCO’s interpretation of the level of implementation of the principles, and to give guidance on the conduct of a self- or third-party assessment. Two methodologies have been used to date—the first was introduced in 2003 and subsequently replaced by a new one in 2011. As part of the IMF’s Financial Sector Assessment Programs (FSAPs), detailed assessments of the implementation of individual IOSCO principles are conducted. Independent experts assign ratings to principles, which are adjudged to be either “implemented,” “broadly implemented,” “partially implemented,” or “not implemented.” For this particular analysis, we assign a number to each rating, as follows:

Implemented = 1Broadly Implemented = 2
Partially Implemented = 3Not Implemented = 4

For each country, the corresponding numerical rating for each IOSCO principle in a particular assessment is aggregated and then averaged to arrive at the rating that is used in the third regression (equation 3.3).

Sources: IOSCO (2003, 2011).

The regression results point to a significant relationship between idiosyncratic influences in stock pricing and the implementation of securities regulations in individual countries (Figure 3.4). The coefficient for the IOSCO explanatory variable is significantly different from zero at the 1 percent level. The findings imply that countries with better implementation of securities regulations are associated with stock markets that are less subject to idiosyncratic influences. This suggests that some of the noise associated with the regression results for the emerging Asian markets may be attributable to institutional factors, consistent with previous evidence.

Figure 3.4Regression Results: Idiosyncratic Influences on Stock Markets and the Effectiveness of Securities Regulation, March 1998–November 2012

Sources: Bloomberg; L.P.; Thomson Reuters I/B/E/S on Datastream; and IMF staff calculations.

Note: IOSCO = International Organization of Securities Commissions.

The empirical evidence also shows that the quality of regulation affects risk perceptions, and consequently, the cost of financing over the longer term. We group the stock market performance of the Asia-Pacific countries, excluding Japan, and that of the G7 countries in the sample that have undergone FSAPS into four groups roughly equal in size. These range from Group 1, which comprises the countries with the strongest records of implementation of securities regulations (that is, those with the highest average IOSCO ratings), to Group 4, which consists of those with the weakest practices in regulating securities markets. Not surprisingly, the findings confirm that weak regulation tends to be associated with more volatile markets and higher required equity cost of capital, as represented by the actual return (Figure 3.5). As a group, the Asia-Pacific stock markets (excluding Japan) tend to have higher IOSCO ratings (that is, weaker implementation of regulations) relative to the ratings of their G7 counterparts.

Figure 3.5Asia-Pacific (Excluding Japan) and the G7 Countries: Securities Regulation and the Risk-Return Trade-Off, March 1998—November 2012

Sources: Bloomberg L.P.; Thomson Reuters I/B/E/S on Datastream; and IMF staff calculations.

Note: IOSCO = International Organization of Securities Commissions.

*Annualized.

A closer examination of the nine IOSCO assessments for the Asia-Pacific countries (excluding Japan) over the 2001–11 period shows that securities regulations and their implementation need to be strengthened. The IOSCO principles under the 2003 methodology are grouped into eight categories, specifically, principles relating to the regulator, principles for self-regulation, principles for the enforcement of securities regulation, principles for cooperation in regulation, principles for issuers, principles for collective investment schemes, principles for market intervention, and principles for the secondary market. While good practice securities regulations, as defined under the 2003 IOSCO methodology, had been implemented or broadly implemented in many countries, a wide range of such regulations had also been assessed as being either partially implemented or not implemented depending on the country (Figure 3.6). The assessments reveal that most countries typically require improvements in a few areas, with the biggest weaknesses evident in the areas of operational independence and account-ability (Principle 2) and the effective and credible use of powers and implementation of an effective compliance program (Principle 10).

Figure 3.6Asia-Pacific: Distribution of IOSCO Ratings, 2001–11

Source: IMF staff calculations.

Note: Excludes Japan. Nine assessments applying the 2003 IOSCO methodology were undertaken during this period. IOSCO = International Organization of Securities Commissions.

Conclusion

In Asia, local stock markets play a key role in financing corporate, and thus, economic, activity. Unfortunately, some of Asia’s stock markets have the reputation of being speculative, rather than trading on fundamentals. Going forward, investors must be able to credibly price their investments in the region’s stock markets if their asset allocation to the region is to remain stable or to continue to grow.

This chapter assesses the extent to which Asia’s stock pricing is based on idiosyncratic factors rather than on systematic risk factors or economic and corporate fundamentals. We design a model that uses international asset pricing and economic theory and that incorporates evidence from the accounting literature. International factors common to the universe of assets, such as global and regional market risks, the local business cycle, and the financial performance of the corporate sector, are applied to extract the “noise” component from stock prices. The G7 countries are used as benchmarks.

Overall, the findings are consistent with the existing literature on the pricing of Asian stock markets. In general, the region’s stock returns have tended to be higher than those of the G7 countries against which they are benchmarked, but have also been more volatile. International systemic risk factors and local fundamentals, such as expected corporate earnings, have substantially less explanatory power when it comes to Asian stock prices compared with the power that they have in the G7 markets. The results point to the existence of greater idiosyncratic influences in Asia.

In other aspects, the analysis finds greater commonalities between the emerging market and advanced economies. Regional factors are consistently the most influential pricing variable, which corroborates the research on international market integration. Local developments, such as forecast earnings, had been less useful as an explanatory variable in the past, but became more important for both Asian and G7 markets during the global financial crisis. One possible explanation is that investors may have sought more expert guidance during volatile periods. Foreign investor allocation decisions also significantly influenced the volatility and returns in both the Asia-Pacific and G7 markets.

This analysis demonstrates the role that policy could play in ensuring that noise is reduced in stock pricing. Although we acknowledge that the apparent importance of idiosyncratic influences on Asian markets could also be attributable to specific local fundamental factors that the model may not have adequately captured, the empirical analysis suggests the existence of a significant relationship between the strength of regulation of securities markets and the extent of noise trading in stock markets. This suggests that improvements in local institutions, such as the regulation of securities markets, could enhance the role of Asian stock markets as an attractive investment destination and, thus, as a reliable source of funding for corporate and economic activity in the region.

Annex 3.1
Annex Table 3.1.1Regression Results: Stock Market Returns, Systematic Factors and Corporate Sector Performance, Part 1: March 1998–June 2005
Adjusted

R2
Standard

Error of

Regression
Sum of

Squared

Residuals
ConstantReturn on World Portfolio (rW/C,t)Return on Regional Portfolio (rW/C,t)Change in Forecast Earnings per Share eC,tf
CoefficientProbabilityCoefficientProbabilityCoefficientProbabilityCoefficientProbability
Mar 1998–Dec 2000
G7
Canada0.5510.0210.064−0.0010.5390.0030.9880.8420.0000.3820.055
France0.8200.0130.0250.0010.6790.0870.3350.9840.0000.3310.337
Germany0.7850.0160.0380.0000.842−0.3000.0081.4540.000−0.1080.524
Italy0.6000.0210.0610.0020.417−0.1690.2391.1160.000−0.3950.401
Japan0.7890.0140.029−0.0010.454−0.2680.0001.2180.0000.0850.592
United Kingdom0.7210.0130.024−0.0010.4390.0940.3050.7110.0000.4500.140
United States0.9940.0020.0010.0000.397−0.0390.0591.0210.0000.0040.902
Mean0.7510.0140.034
Asia and Pacific excluding Japan
Australia0.2320.0160.0370.0010.4570.2510.0000.1170.027−0.6080.055
China Shanghai A−0.0180.0330.1550.0040.190−0.0550.6980.0330.747−0.0370.544
China Shenzhen A−0.0160.0320.1420.0040.180−0.0570.6740.0230.811−0.0460.431
China Shanghai β0.0450.0580.4780.0010.8160.2000.4190.3370.059−0.1550.154
China Shenzhen β0.0190.0640.5730.0020.7080.0180.9460.3220.099−0.1700.152
Hong Kong SAR0.3930.0340.1670.0010.8350.7070.0000.5090.000−0.1790.587
India0.0430.0420.2520.0000.9930.3980.028−0.0760.562−0.7560.031
Indonesia0.0260.0520.379−0.0010.769−0.3720.0210.4070.0110.0100.598
Korea0.1570.0560.4370.0000.940−0.0120.9550.8830.0000.0680.771
Malaysia0.0900.0440.276−0.0010.822−0.1080.5100.5530.000−0.3290.217
Philippines0.1440.0390.219−0.0090.0110.2080.1960.2440.056−1.8380.000
Singapore0.2700.0340.1670.0000.8660.3110.0200.6150.0000.0680.767
Thailand0.1020.0500.353−0.0050.215−0.0130.9470.6220.000−0.0180.218
Mean0.1140.0430.280
Jan 2001–Jun 2005
G7
Canada0.6100.0120.0330.0010.2660.1980.0130.4720.000−0.0460.551
France0.9060.0090.0200.0000.614−0.0450.4731.1250.0000.0350.762
Germany0.8470.0140.0470.0000.9990.1530.1141.1490.0000.0040.977
Italy0.8020.0140.0440.0000.753−0.0320.7291.0840.0000.1220.425
Japan0.8580.0110.026−0.0010.075−0.2930.0001.3540.0000.0410.637
United Kingdom0.8960.0070.0120.0000.8310.0270.5840.8050.000−0.0110.739
United States0.9970.0010.0000.0000.177−0.0700.0001.0740.0000.0290.053
Mean0.8370.0120.119
Asia and Pacific excluding Japan
Australia0.2400.0130.0380.0010.1630.2130.0000.1580.0010.1320.673
China Shanghai A0.0130.0270.169−0.0030.0990.0000.9990.1720.0670.1020.627
China Shenzhen A0.0150.0290.188−0.0040.0260.0080.9450.1750.0770.1760.428
China Shanghai β−0.0010.0440.437−0.0010.6660.2060.2300.0140.9270.0150.965
China Shenzhen β0.0260.0500.5720.0010.7060.1040.5960.3300.0560.1740.652
Hong Kong SAR0.4720.0210.0970.0000.7130.5360.0000.4090.0000.2390.289
India0.1620.0280.1820.0020.2310.3280.0030.3040.0020.1040.762
Indonesia0.0210.0300.2100.0040.041−0.2720.0130.2860.007−0.0050.975
Korea0.2870.0330.2500.0030.1180.0940.4500.9180.0000.0090.967
Malaysia0.1110.0200.0880.0010.5640.0770.3170.2350.0010.2160.386
Philippines0.0310.0290.1980.0000.8780.0260.8170.2070.0430.2920.269
Singapore0.4240.0200.0900.0010.6470.3660.0000.4720.000−0.0430.760
Thailand0.1170.0300.2010.0030.107−0.0290.7970.5020.0000.1180.411
Mean0.3730.0280.221
Jul 2005-Dec 2007
G7
Canada0.5860.0110.0160.0010.3450.0380.6870.7770.000−0.0520.470
France0.8840.0070.0060.0000.942−0.0590.5271.0900.000−0.0150.873
Germany0.8380.0090.0090.0020.008−0.1880.1081.2120.000−0.0990.432
Italy0.7980.0080.0080.0000.5600.2120.0490.6770.0000.0700.662
Japan0.8520.0100.011−0.0010.239−0.1970.0101.0120.0000.0640.710
United Kingdom0.8930.0060.004−0.0010.136−0.0160.8310.9180.0000.0040.966
United States0.9900.0020.0000.0000.416−0.1980.0001.1490.000−0.0290.014
Mean0.8340.0070.008
Asia and Pacific excluding Japan
Australia0.4900.0120.0190.0020.161−0.1600.1110.7500.000−0.0680.717
China Shanghai A0.0890.0340.1430.0140.000−0.2130.4800.5800.011−0.6760.084
China Shenzhen A0.0550.0380.1860.0160.000−0.5010.1450.6290.016−0.9100.042
China Shanghai β0.0190.0550.3890.0140.013−0.2100.6710.5580.135−0.6990.277
China Shenzhen β0.1080.0410.2150.0090.030−0.4520.2220.9270.001−0.4000.403
Hong Kong SAR0.5620.0170.0360.0020.1590.2730.0720.6660.000−0.1050.126
India0.2450.0260.0890.0050.0620.6150.0120.3160.0760.2530.279
Indonesia0.1980.0290.1060.0040.184−0.4030.1360.8890.0000.3670.341
Korea0.5970.0180.0420.0020.2750.1000.5251.0570.000−0.0590.784
Malaysia0.3030.0160.0320.0020.1340.2370.0990.3620.0010.0610.752
Philippines0.3510.0240.0720.0050.0350.0430.8440.7940.000−0.3240.197
Singapore0.5960.0140.0240.0010.6440.4220.0010.5410.0000.2470.248
Thailand0.2170.0230.0670.0010.695−0.3150.0920.7230.000−0.4610.136
Mean0.2950.0270.109
Jan 2008-Nov 2012
G7
Canada0.7720.0150.0590.0000.9200.2720.0010.6400.000−0.1020.015
France0.9160.0110.033−0.0010.480−0.1970.0041.2050.000−0.0460.494
Germany0.8760.0140.0490.0010.3770.0060.9431.0220.000−0.1220.006
Italy0.8000.0200.101−0.0020.110−0.4910.0001.4910.000−0.0700.479
Japan0.8800.0130.0400.0000.882−0.1170.0171.0070.000−0.0650.051
United Kingdom0.8930.0110.0290.0000.6400.0630.3270.8420.0000.0430.443
United States0.9930.0030.0020.0000.564−0.2270.0001.1830.0000.0160.039
Mean0.8760.0120.045
Asia and Pacific excluding Japan
Australia0.4990.0210.1090.0000.8530.8130.0000.3450.000−0.2210.027
China Shanghai A0.1880.0350.304−0.0030.134−0.5810.0001.0080.0000.2190.285
China Shenzhen A0.1310.0430.461−0.0020.506−0.7020.0001.0850.0000.1130.654
China Shanghai β0.1560.0430.459−0.0020.444−0.6990.0001.1510.0000.1540.540
China Shenzhen β0.2580.0350.3030.0000.963−0.6100.0001.1620.0000.2660.193
Hong Kong SAR0.7950.0170.0750.0000.9830.0250.7081.0570.0000.2010.015
India0.3800.0300.226−0.0010.5320.2470.0360.6220.0000.3550.029
Indonesia0.2610.0310.2480.0020.2450.0750.5420.6200.0000.0810.566
Korea0.2890.0300.2240.0000.9810.1660.1540.7450.0000.2000.147
Malaysia0.3870.0150.0590.0010.430−0.0960.1090.5140.0000.3510.003
Philippines0.3790.0260.1650.0020.1790.0460.6450.6870.0000.2170.279
Singapore0.6850.0190.0880.0010.5000.1960.0080.8410.0000.2340.073
Thailand0.4420.0250.1570.0020.2220.0770.4260.6670.0000.4080.001
Mean0.3730.0280.221
Sources: Bloomberg, L.P.; Thomson Reuters I/B/E/S on Datastream; and IMF staff calculations.
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This chapter is based on Lipinsky and Ong (2014); see this paper for more references to the related literature.

The G7 comprise Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States.

Morck, Yeung, and Yu (2000) find that stock prices tend to move together more in emerging market economies than in advanced economies, and while factors such as market and country size and economic and firm-level fundamentals matter for stock returns, a large residual effect remains and is correlated with measures of institutional development, such as property rights protection. Separately, De Long and others (1989, 1990) find that a reduction in informed trading can increase market-wide noise trader risk.

See Lipinsky and Ong (2014) for a detailed exposition of the model design.

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