Foreign investor behaviour and the Asian financial crisis

Journal of International Financial Markets, Institutions and Money 11 (2001) 395– 422 www.elsevier.com/locate/econbase

Foreign investor behaviour and the Asian financial crisis Michael Bowe *, Daniela Domuta Manchester School of Management, Uni6ersity of Manchester Institute of Science and Technology (UMIST), Sack6ille Street, P.O. Box 88, Manchester M60 1QD, UK Received 15 January 2000; accepted 9 October 2000

Abstract This study’s objective is to identify the relative importance of local and foreign investor expectations in explaining the short-run behaviour of equity returns in Asian markets during a period encompassing the 1997 financial crisis. The analysis utilises the insight that the pricing behaviour of closed-end country funds (CEFCs) in relation to their constituent underlying assets, can be used as a mechanism for distinguishing between the relative impact of local and foreign investor expectations. To ensure robust results, the analysis incorporates several different empirical specifications (error correction models, multivariate VAR and single equation), and uses alternative measures of underlying asset prices in the Asia markets. The results suggest that both local and foreign investor expectations are important as a channel determining the pricing behaviour of Asian assets trading in Asian and US equity markets. This finding appears independent of the degree to which a specific Asian equity market is open to foreign investment. Moreover, the measured impact of country-specific foreign investor information is enhanced during periods of financial crisis. The findings lend credibility to the view that the trading behaviour of foreign investors was significant in sustaining the dimension and duration of the Asian crisis. © 2001 Elsevier Science B.V. All rights reserved. JEL classification: F31; G14; G15 Keywords: Closed-end country funds; Asian crisis; Foreign investors

* Corresponding author. Tel.: +44-161-2003407; fax: + 44-161-2003505. E-mail address: [email protected] (M. Bowe). 1042-4431/01/$ - see front matter © 2001 Elsevier Science B.V. All rights reserved. PII: S1042-4431(01)00037-3

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1. Introduction The financial crises in Latin America, Asia and Central Europe raise a variety of questions relating to the dynamics of the information transmission process in international capital markets. A series of recent studies, comprehensively summarised in Frankel and Schmukler (1996, 1998a), argue that differing investor sentiment and/or the existence of asymmetric information in financial markets induces divergent expectations across the local and foreign investor communities, which is reflected in different trading behaviour. The impact of this investor heterogeneity may be particularly evident during times of market turbulence, such as that experienced during a financial crisis. One hypothesis, expressed at various times by the International Monetary Fund (IMF)1 is that local investors tend to be the initiators of any crisis. This possibility reflects these investors’ proximity to relevant economic and policy related information, enabling them to be the first to react to any deterioration in local economic conditions. Other commentators, including Dornbusch and Park (1995), Radelet and Sachs (1998) and Stiglitz (1998), acknowledge the destabilising potential of foreign investors.2 Indeed, the Prime Minister of Malaysia in well-documented remarks, places the responsibility for the 1997 Asian crisis firmly at the feet of the international investor community, in particular foreign speculative investors.3 This paper addresses this particular issue. Specifically, we attempt to identify the extent to which the propagation of financial crises can be attributed to local investor reactions to the prevailing economic conditions in the relevant Asian market, as opposed to the views of that market’s economic prospects held by foreign investors. As a framework for distinguishing between the influence of local and foreign investor activity in a country’s capital markets, we utilise an approach focusing upon the pricing behaviour of closed-end country funds (CECFs). CECFs are a recently innovated vehicle for global investment, the majority of funds being established during the late 1980s and early 1990s. A CECF is a publicly listed investment company which collects money from investors through an initial public offering of a fixed number of shares, and invests the proceeds in a portfolio of securities of a particular country. Shares in a CECF trade on the market on which they are listed (here the NYSE) at a price which is determined by the trading order flow in that market. The aggregate value of the assets (securities) underlying the fund is known as the fund’s Net Asset Value (NAV). These assets are individually

1 See the Annual Capital Markets report, (1995, p. 7) in relation to the 1994 Mexico Crises, and Stanley Fischer’s January 1998 address ‘The Asian Crises: A view from the IMF’, to the Bankers’ Association for Foreign Trade, in Washington, DC, expressing similar sentiments regarding Asia. 2 The alleged destabilisation occurs through differing channels. For Dornbusch and Park, (1995), foreign investment strategies induce equity price overreaction to fundamentals, Radelet and Sachs, (1998) emphasise financial panic, while Stiglitz, (1998) focuses upon volatility in international capital flows. 3 Mathahir, M., ‘Highwaymen of the Global Economy,’ Wall Street Journal, 23 September 1997.

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traded in the domestic securities market of the relevant country. As such, their value also forms a significant constituent component of the local security market index. While no definitive public information exists identifying to the nationality of CECF holders, extensive surveys of CECF managers and administrators indicate CECFs are held predominantly by resident investors in the country where the fund trades. In contrast, a fund’s NAV reflects the information and expectations of the local investors in the relevant domestic market. Choe et al. (1999) examine equity trades on the Korean Stock Exchange by three groups of investors, Korean individual investors, Korean institutional investors, and foreign investors (who must be registered with the Korean Securities Supervisory Board before they can trade on the KSE). For their sample period (11/30/97 – 12/29/98), foreign investor ownership averages less than 10%, varying from 6.47 to 4.62% of shares (on an equal-weighted basis) and averaging 9.28% on a value-weighted basis. These features of CECFs and their NAVs corroborate the contention that in relati6e terms, CEFC prices provide a better indication of the foreign investor expectations of a particular market’s prospects, while the fund’s NAV reflects the views of the local investor community.4 This paper contributes to the literature in several respects. First, we adopt an alternative approach to those studies of foreign investor trading during the Asian crisis, notably Choe et al. (1999), which examine the behaviour of foreign investors who actually trade on the domestic markets in the crisis country. Second, Frankel and Schmukler (1996) and Chandar and Patro (2000) also analyse the relation between CECF and underlying asset values during crisis periods. The former study the Mexican crisis of 1994, the latter 25 currency crises in 18 markets, including 12 emerging and six developed nations. We examine data for eight CECFs relating to seven south-east Asian countries, sampled daily over the 1993–1999 period. The economies of two of these countries, Singapore and Taiwan, were not severely affected by the 1997 financial crises. Unlike previous studies, this fact enables us to generate a control group by which we can compare the impact of investor heterogeneity across crisis and non-crisis countries in the same region, enabling us to be more confident that our results are reflecting crisis conditions. Third, Frankel and Schmukler’s (1996) interest is not only in identifying whether local or foreign investors were at the forefront of the 1994 Mexican crisis, but also in explaining the observed dynamics of the discounts5 between CECF prices and the relevant fund’s NAV. This is also the central objective of Chandar and Patro (2000). Such a focus constrains these analyses to using weekly data observations on CEFC values and NAVs.6 We conjecture that during periods of high volatility in

4

Commonly attributed to Frankel and Schmukler, (1996). The difference between the price of a CECF and its NAV forms its premium, (discount) if positive, (negative). 6 Data on the latter are only reported on a weekly basis. 5

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security prices, characteristic of crisis periods, the use of weekly data may lead to a significant loss of relevant information. Consistent with this paper’s primary objective which is the analysis of the short-run dynamic relationship between Asian CECF prices and underlying asset values in the Asian markets during the financial crisis, in the majority of this paper we employ a high frequency dataset.7 This takes the form of daily data on CECF prices, and US dollar values of both local and US stock market indices. Incorporating both the latter variables enables us to design tests which distinguish between country-specific sentiment and the global impact of undifferentiated foreign investor sentiment originating in the US market. Fourth, the econometric methodology, discussed in a later section, uses a variety of specifications, including error correction models, multivariate VAR and single equation specifications, facilitating the identification of both the overall significance and also the direction of the information transmission mechanism between markets. Not only does this ensure the results are robust to alternative specifications, it also enables us to determine if the trading behaviour of foreign investors moderated or exacerbated the 1997 financial crisis in Asia. The remainder of the paper is easily summarised. Section 2 presents a selected overview of the CECF literature, emphasising studies which attribute the pricing behaviour of CECFs in relation to their underlying asset values as reflecting differential investor sentiment or information. Section 3 discusses the data, undertakes tests for stationarity, cointegration and weak exogeneity. Section 4 presents the results of multivariate and single equation specifications analysing the nature of the short-run information transmission mechanism between local and foreign securities markets. Brief concluding remarks follow in Section 5.

2. Closed-end country funds: pricing anomalies and investor sentiment If international capital markets were efficiently and perfectly integrated, then the distinction between CECF fund prices and the underlying NAVs would have little analytical significance. As CECF prices and NAVs are simply two market values of the same assets, arbitrage should ensure that they are always equal. However, arbitrage limitations have resulted in the pricing behaviour of CECFs being characterised by a series of anomalies, a set of counterintuitive features, which run counter to the propositions of market efficiency. The best documented relate to the consistent observation that funds customarily trade at a large and variable average discount to their NAV.8 This observation holds not only for emerging market 7 Bennett et al., (1998) in a paper presented at a conference on ‘Information from Financial Markets,’ at the Bank of England, 18 September 1998, adopt a similar strategy, although they employ a different empirical methodology. We check the robustness of our results using alternative datasets, including NAVs. Section 3 discusses alternative datasets and reports the summary findings of these procedures. 8 Other commonly observed puzzles relate to:, (i) the at issue trading premium of closed-end funds which later transforms itself into a discount,, (ii) the wide fluctuations of fund discounts, which appear to be mean reverting, and, (iii) the fact that the discounts decrease significantly around the fund termination date.

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country funds, but also for large, liquid CECFs comprising securities trading in liquid markets without capital flow restrictions. This imperfect arbitrage has been attributed to several phenomena. Market and institutional frictions have been identified, namely; various types of transaction costs, asset illiquidity, exchange rate risk arising from buying and selling assets denominated in different currencies, a lack of information on CECF composition, or capital gains tax liabilities and other barriers to capital mobility.9 Another explanation claims that the CECF and its underlying securities are held by different types of investor clientele. The generic hypothesis is that the underlying assets are held by rationale traders, while CEFCs are held by both rationale investors and noise traders. The trading decisions of noise traders incorporate sentiments, defined as generalised optimism or pessimism not based on economic fundamentals. The divergent expectations of rational investors and noise traders are reflected in the existence of a systematic risk factor. The latter’s expectations are assumed to contain a non-predictable (stochastic) component, deriving from the fact that sentiments cause them to randomly under- or overestimate returns on CEFC investment. As noise trader sentiments cannot be accurately predicted, it follows that investing in the CECF is somehow riskier than investing in the underlying assets. Since investors are risk averse, the average CECF price is below the funds NAV.10 A final explanation identifies asymmetric information as the source of imperfect arbitrage.11 Local investors who trade in the underlying assets are assumed to be

9 See Pontiff, (1996), which develops a general arbitrage cost approach applying to all classes of closed-end funds. Errunza et al., (1998, 2000) propose theoretical models for interpreting fund mispricing. The models are based on both the degree of accessibility of the foreign markets, (cross-border arbitrage), and the extent to which country funds are substitutes for their underlying assets. They demonstrate the potential for country funds to enhance efficiency in asset pricing across segmented markets. Bosner-Neal et al., (1990) utilise event study methodology to investigate the impact of barriers to international investment on a sample of CECF prices. They find that an announcement of a relaxation of investment restrictions is associated with a 6.8% decrease in the price-net asset value ratio across funds, during the 3 weeks surrounding the announcement date. However, these investment barriers raised by government are unable to account for the time variation in CECF discounts/premia. 10 For a recent exposition of the investor sentiment approach see De Long et al., (1991). Lee et al., (1991) apply the investor sentiment argument to an analysis of domestic closed-end funds, where the underlying fund assets are traded in the same market as the fund shares. Lee et al.’s analysis is extended to country funds by Bodurtha et al., (1995). Related analysis is presented in Hardouvelis et al., (1994). This paper provides a useful synthesis of the main empirical regularities displayed by closed-end funds, such as co-movement in the discounts across funds, stationarity of the premiums, and excess variation of country fund prices relative to net asset value returns. The authors argue in favour of a noise trader model of asset pricing. We note that a critique of the investor sentiment hypothesis has recently been articulated by Elton et al., (1998). This maintains that the sentiment, (discount) index does not enter the return generating process and, therefore, is not a systematic, priced-in-equilibrium risk factor. 11 First proposed by Frankel and Schmukler, (1996, 1998a), op. cit.

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better informed than CECF investors, typically resident overseas. This informational advantage can derive from a variety of sources, including: the ability to access relevant locally available information, lower costs of accessing such information, or the timeliness of access to relevant information. CEFC investors realising their relative informational disadvantage, expect to realise a lower return on the fund than domestic investors in the fund’s underlying assets. This implies CECF investors are unwilling to pay as much as locals for the same assets. Average positive discounts are the result. This explanation of fund discounts differs from that attributable to sentiment or noise trading, because in this asymmetric information model, foreign CEFC investors are rational individuals who generate unbiased forecasts of fund returns. It is their informational disadvantage which leads them to exhibit a higher subjective variance than the local investors in the underlying assets, explaining their perception that investment in the CECF is a riskier undertaking. The dynamics of the pricing relationship between CECFs and their NAVs establishes a convincing a priori rationale for distinguishing between local and foreign investor expectations in models linking international asset prices. In relative terms, the former are embedded in the underlying NAV/stock market indices, the latter in CECF prices. The empirical evidence we now consider is unable to distinguish between noise trading and asymmetric information as the source of investor heterogeneity, although it does provide some evidence on certain aspects of the market frictions approach.

3. Testing for exogeneity: CECF prices and underlying Asian asset values This section reports the results of short- and long-run exogeneity tests, with emphasis on the relationship between CECF prices and the locally traded underlying assets.12 One particular area of interest is whether lagged short-run changes in CECF prices (local asset values) are significant in explaining current changes in the local asset values (CECF prices), and which variable adjusts to any identified long-run relationship between the two. Ideally, the variable incorporating the most information relating to the fundamental value of the traded asset(s) will appear exogenous with respect to the other variable. If local stock market indices better reflect the changes in the underlying fundamental determinants of asset value than CECF prices, then future CECF price changes will tend to be explained by current underlying asset price changes and not the reverse. Alternatively stated, underlying asset values will appear exogenous while exogeneity of the CECF price changes will tend to be rejected. Economic reasoning suggests the existence of a long-run equilibrium (cointegrating) relationship between CECF and their underlying asset values in a frictionless trading environment, as they are ultimately two different values of the same asset.13 12

This section has benefited greatly from incorporating the suggestions of an anonymous referee. Frankel and Schmukler, (1998a) reject the hypothesis of no cointegration between CECF prices and NAVs for 29 of the 61 funds they analyse, which goes some way to confirming this intuition. 13

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To investigate whether the results are robust to different measures of local asset values and empirical specifications, we consider a number of formats for the exogeneity tests. Two are particularly noteworthy. First, although we argue previously that the use of daily data on Asian market stock indices generates significant advantages in facilitating analysis of the short-run information transmission process, we are aware that they represent imperfect proxies for the underlying assets in the CECF. Therefore, we also conduct the analysis using two alternative measures of Asian assets. Specifically, we use both weekly data on the fund NAVs, and higher frequency (daily) data from the IFC investable index for the relevant country. Given the capital controls imposed in Asia (outlined below) it may be difficult in certain cases for CECF managers to replicate the domestic Asian market index, and the Asian assets which constitute the IFC investable index can be traded by foreign resident investors. Second, all the results we report control for the potential global impact of US market sentiment. This is important given the substantial evidence that emerging market traders appear to infer from New York prices information relevant for the pricing of local equities (Engle et al., 1990; King and Wadhwani, 1990; Bodurtha et al., 1995).

3.1. Data description and Asian market foreign in6estor restrictions The CECF data consists of daily closing prices for eight south-east Asian CEFCs trading on the NYSE. The eight country funds together with their NYSE codes are: First Philippine Fund (FPH), Jakarta Fund (JKG), Korea Fund (KOR), Malaysia Fund (MAL), Roc Taiwan (ROC), Taiwan Fund (TAW), Thai Fund (THA), and Singapore Fund (SNG). Fund selection is based on our interest in analysing CECF pricing behaviour during financial crisis. In this respect, five funds are from crisis affected countries: Indonesia, Korea, Malaysia, Philippines and Thailand, while three funds, namely the Taiwan Fund, the Roc Taiwan Fund, and the Singapore Fund, can be considered to be control variables, as they invest in markets which were less affected by the Asian region’s 1997 crisis. CECF prices, in US dollars, are obtained from the International Financial Statistics database in Datastream. For the reasons outlined previously we use three proxies for the CECF underlying assets. Daily closing values of the local Asian market index and the IFC investable index are obtained from Datastream, and weekly values of the fund NAVs are provided by Weisenberger, a Thompson Financial Company. All local currency values are translated into US dollars using the spot exchange rate in effect at the time. Closing values of the S&P 500 are again obtained from Datastream.14 The data spans the trading period from 1 January 1993 to 2 June 1999 and is identical for all assets. Full definitions of all the variables and series are given in Table 7. Earlier we remark that limits to arbitrage have been attributed to the existence of market frictions, and the countries in the sample differ in the type of controls imposed on both foreign ownership and foreign exchange transactions. While 14 No adjustments are made to the returns of the S&P 500 in relation to dividends. No IFC investable index is available for Singapore.

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significant controls exist in Indonesia, Korea, the Philippines and Thailand, other countries, namely Singapore and Taiwan have insignificant restrictions. Malaysia had virtually no regulations prior to the Asian crisis, but in September 1998 imposed severe controls on short-term capital flows. Specifically, foreign residents can purchase equity in Indonesian companies only up to the amounts agreed in accordance with applicable provisions on foreign capital participation in the issuing company. The foreign ownership limit is 49%, with taxes imposed on dividends and capital gains. In Korea, foreign investors in aggregate could not own more than 20% of a firm’s shares prior to May 1997, when the limit was raised to 23%. It was further raised in November and December 1997, first to 26% and then to 50%. Thailand also imposes a general maximum 49% foreign ownership, although in some sectors, notably financial services, it is as low as 25%. Foreigners also submit trading orders for Thai company shares (Class B shares) to the Alien Board of the Stock Exchange of Thailand, while Thai citizens trade Class A shares on the Main Board. Dividend payments to foreigner investors continue to be taxed, although the tax rate has been gradually reduced during the 1990s. The Philippines has established foreign ownership ceilings in certain industries, has a share classification system similar to that operative in Thailand for key companies15, and taxes foreign investors in a discriminatory manner. We do not explicitly control for these restrictions in the following empirical specifications. However, if such market frictions are important in preventing information transmission, we expect to notice significant differences in results between open and restricted financial markets.

3.2. Stationarity tests and cointegration Certain of the test procedures undertaken require stationary variables. We conduct two standard tests to detect unit roots, augmented Dickey–Fuller (ADF) and KPSS. The null hypothesis in the former test is that the levels of the variables contain a unit root, in the latter it is that the levels of the variables are stationary. 16 Lag length is determined using the Akaike Information Criterion (AIC) and the Schwarz Bayesian Criterion (SBC). In the overwhelming majority of cases, the results17 identify a unit root in the CECF price and the various underlying asset series (in levels), indicating these series are non-stationarity. Equivalent unit root tests for the variables in first differences widely reject non-stationarity, enabling us

15

Although Philippine citizens can trade Class B shares. It is generally agreed that using tests with alternative null hypothesis regarding stationarity improves the reliability of the results, see chapter 4 in Maddala and Kim, (1998). However, one must be careful about interpretation when using the tests for confirmatory purposes. If both sets of tests fail to reject the respective nulls, or both reject the respective nulls, we do not have a confirmation. In the case of ADF, we also test for the presence of a unit root in the series in non-crisis periods. This ensures that the discovery of a unit root is not due to any structural break in the data. 17 Not reported, but available from the authors on request. 16

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to conclude that the series are well-described as integrated of order 1, or I(1) processes. This finding is unsurprising as it accords with the existing consensus in the literature. The tests for cointegration we conduct are based on the trace and eigenvalue likelihood ratio procedures developed by Johansen (1988) and Johansen and Juselius (1990). To ensure robustness, we conduct both bivariate tests between CECF prices and the selected proxies for the funds underlying assets (namely Asian market indices, NAVs, and IFC indices) and also multivariate cointegration specifications which incorporate the S&P index. Tests are conducted over the full sample period.18 As general conclusions regarding the existence of cointegration are of particular interest at this stage of our analysis, we simply summarise the findings. At the 10% significance level there is evidence of at least one cointegrating vector in at least five, and sometimes all of the eight Asian funds. At a 5% significance level, evidence for cointegration is much weaker, occurring in between one and five of the eight funds.19 In terms of testing with alternative data to proxy the funds underlying assets, the weakest evidence for cointegration (in terms of statistical significance) is found using the IFC investable index, and the strongest using the funds underlying NAVs. However, the difference in results using the local market indices rather than NAVs is marginal, suggesting that the former may be a reasonable high frequency proxy for the NAVs.

3.3. Exogeneity tests: CECF prices and underlying Asian asset prices The exogeneity test results are obtained from the error correction model (ECM) specified in Eq. (1), between CECFs prices and the Asian market assets. The specification relates the CECF and local asset values to the one-period lagged cointegrating vector, and to lagged first differences of both dependent variables. It incorporates lagged first differences of the US market index (S&P 500) as an exogenous variable. From the perspective of the economic interpretation of the results, Eq. (1) has certain advantages over other alternatives we considered.20 First, the h coefficients on the cointegrating vectors have an unambiguous meaning. A significant fitted h1 (h2) means that CECF prices (local market indices) adjust in the long-run to changes in any detected cointegration relationship. Second, inclusion of US sentiment as an exogenous variable accords with economic intuition. It is difficult to provide an a priori economic justification for why information concerning CECF prices and Asian indices should impact significantly on the level of the S&P 500. We note that the results obtained from the multivariate cointegrating

18

The tests were undertaken both with and without crisis period dummy variables. We report the latter. 19 There is slightly more evidence of cointegration using the multivariate specification. 20 In particular, a cointegrating transformation of a multivariate VAR in CECF prices, emerging market indices and the S&P 500.

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VAR specification, which does not assume exogeneity of the S&P 500 not only provide support for this latter conjecture, but strongly confirm the qualitative nature of the results obtained from estimates of Eq. (1)21 L

L

j=1

j=1

L

L

j=1

j=1

FRt =‚1 +h1(Ft − 1 −y −uLt − 1) + % i1j FRt − j + % k1j LRt − j L

+ % q1j USRt − j +u1t j=1

LRt =‚2 +h2(Ft − 1 −y −uLt − 1) + % i2j LRt − j + % k2j FRt − j L

+ % q2j USRt − j +u2t

(1)

j=1

Representative results from maximum likelihood estimation of the model given a prior finding of cointegration (at the 10% significance level) are presented in Table 1. Appropriate lag length is selected using generalizations of the Akaike Information Criterion (AIC), and the Schwarz Bayesian Criterion (SBC). We report the lag length selected by AIC. Standard adjustments are undertaken upon detection of serial correlation and/or heteroscedasticity, following procedures outlined in Newey and West (1987) and White (1980). To ensure convergence towards long-run equilibrium, the model’s structure implies that the expected sign of h1 (h2) is negative (positive). Weak exogeneity tests conducted by analysing the adjustment towards the long-run relationship as reflected in the parameters h1 and h2, suggest that the underlying Asian assets values tend to be the exogenous variable, with CECF prices adjusting to past changes in these asset values. This result corroborates the findings reported in Frankel and Schmukler (1998a), who note it is consistent with the fact that local asset values better reflect information concerning local fundamentals. It is also consistent with studies which assume that noise traders hold CECFs but not the fund’s underlying assets. If CECF investors consistently under- or overpredict fund price changes, these prices will eventually adjust to local asset values which reflect fundamentals. Moreover, we note that the speed of adjustment to long run equilibrium implied by the size of the point estimates of the h1 and h2 coefficients, exceeds that reported in the Frankel and Schmukler (1998a) study. The latter document an average (absolute) value of 0.11 and 0.075 for significant h1 and h2, respectively. One possible explanation is provided by Klibanoff et al. (1998), who document a significant increase in the sensitivity of CECF prices to contemporaneous changes in underlying asset values in the presence of dramatic news affecting the relevant country. The present sample period incorporates data from the Asian crisis period,

21 Estimates obtained from the cointegrating transformation of the trivariate VAR in CECF prices, the S&P 500, and the underlying fund NAVs, (or their proxies, namely: emerging market indices and IFC investable) support the inference of strong exogeneity of the S&P 500 for all countries. This suggests the specification in Eq. (1) is appropriate. Detailed results are available from the authors on request.

Long-run adjustments (weak-exogeneity)

Null hypothesis

h1

h2

Short-run adjustment; chi-squared

Granger-non-causality (strong-exogeneity); chi-squared

Ho: Short-run impact of US Market =O; chi-squared

Ho: k1i =0

Ho: k2i =0

Ho: h1 =0 and k1i =0

Ho: h2 = 0 and k2i = 0

Ho: q1i = 0

Ho: q2i = 0

(Ho: h1 =0)

(Ho: h2 =0)

Country fund Indonesia

−0.766 (−3. 493e)

0.723 (1.109)

23.076c

60.446e

41.308e

60.448e

32.704e

39.205e

Korea

−0.993 (−2.836e)

1.011 (1.314)

8.442

20.816e

18.408e

23.885e

5.195

157.78e

ROC Taiwan

−0.938 (−3.443e)

1.1470 (0.357)

13.528

21.940c

22.553c

23.928d

22.049c

40.760e

Summary fundings (local index)b Philippines

6/7 86%

1/7 14%

4/7 56%

−0.845 (−2.889e)

0.740 (1.168)

2.840c

3.862d

13.253e

9.986e

1.517

4.935d

Malaysia

−1.064 (−1.472)

1.098 (1.889c)

10.353e

0.724

13.465e

5.256c

7.326d

10.639e

Singapore

−0.766 (−7.607e)

0.554 (0.992)

6.421d

6.128d

17.830e

13.332e

9.492e

11.527e

5/7 72%

0/7 0%

3/7 42%

7/7 100%

6/7 86%

6/7 86%

5/7 72%

7/7 100%

6/7 86%

4/7 56%

3/7 42%

7/7 100%

7/7 100%

Dataset (frequency: c 0BS)

LAG length (AIC)

Local Index (daily: 1268) Local Index (daily:1268) Local Index (daily: 1268) Local Index

16

NAV (weekly 252) NAV (weekly 252) NAV (weekly 252)

2

7 14

2 2

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Table 1 Representative and summary maximum likelihood results: exogeneity tests (Wald statistics)a

405

406

Long-run adjustments (weak-exogeneity)

Summary fundings (NAVs)b Thailand Malaysia

6/8 75%

4/8 50%

2/8 25%

−1.099 (−2.489d) e

−0.814 (−3.285 ) e

0/8 0%

1.277 (2.02d) e

0.508 (2.68 )

Short-run adjustment; chi-squared

Granger-non-causality (strong-exogeneity); chi-squared

Ho: Short-run impact of US Market =O; chi-squared

6/8 75%

6/8 75%

5/8 62%

6.968e

0.006 (0.002)

2.013

Summary fundings (IFC)b

5/5 100%

2/5 40%

3/5 60%

2/5 40%

5/8 62%

2.779

13.615 3/5 60%

5/5 100%

6/8 75%

17.503e

c

28.491

−0.933 (−3.975 )

5/8 62% 14.314e

e

Taiwan

5/5 100%

4/8 50%

39.486 e

e

2.779

c

23.184

21.445 5/5 100%

5/5 100%

4/8 50%

d

14.268 4/5 80%

2/5 40%

8/8 100%

7/8 88%

25.615d

13.814

e

9.185

5/5 100%

7/8 85%

18.189e

d

4/5 80%

7/8 85%

Dataset (frequency: c 0BS)

2/5 40%

38.371

e

39.985

e

5/5 100%

4/5 80%

LAG length (AIC)

NAV

IFC (daily: 1268) IFC (daily: 1268) IFC (daily: 1268) IFC

2 2 3

a Full sample period: 2/1/93–2/6/99. Tests using alternative data for the underlying Asian asset prices. For an explanation of the restrictions see Eq. (1) in the text. Figures in the table denote values of the  2-test statistic. Heteroscedasticity and/or serial correlation corrections undertaken with White (1980) or Newey and West (1987) adjustments, as appropriate. b Reported as number of funds in which null hypothesis is rejected/total number of funds in which null of no cointegration is rejected (at 10% level). The percentage of funds is given below. The left (right) hand figure relates to rejection of the null hypothesis in the numerator at a level of significance of 10% (5%). c Significant at the 10% level. d Significant at the 5% level. e Significant at the 1% level.

M. Bowe, D. Domuta / Int. Fin. Markets, Inst. and Money 11 (2001) 395–422

Table 1 (Continued)

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407

and it is this impact that may be being captured in the data.22 Representative and summary test results from the ECM capturing the short-run adjustment process are also displayed in Table 1. A vector k1j (k2j ) different from zero implies that CECF prices (NAVs) adjust to past changes in NAVs (CECF prices). Finally, we report evidence from Granger-noncausality or strong exogeneity tests, which examine whether h1 and k1 or (h2 and k2) are jointly zero. Accepting the null hypothesis that h1 and k1 (h2 and k2) are jointly zero, would suggest that CECF prices (NAVs) are not explained by either the long-run equilibrium or by recent changes in the other variable. Analysis of the short-run adjustment process provides evidence of significant mutual interaction between the local asset market and CECF return series. One notable result is that CECF returns, reflecting foreign investor information, consistently influence short-run asset returns in the local market. This result is particularly discernible when the two higher frequency datasets are used. Asian asset returns also impact significantly on CECF returns, with the exception of Taiwan and Korea. Hardouvelis et al. (1994) also note that in some instances CECF prices appear sticky with respect to movements in the local country’s stock market. They advocate noise trader misperceptions as one possible rationale, maintaining their evidence is consistent with CECF investors under-reacting to innovations in the local stock market, and over-reacting to positive developments in world markets.23 Results of the Granger-noncausality tests consistently reject strong exogeneity of both CECF prices and the underlying asset values across all the different data proxies for a fund’s underlying assets. This contrasts with previous studies using only NAVs, which found they tend to be the exogenous variable, although this evidence is somewhat weaker for the Asian region. The results may reflect the fact that our sample period incorporates the Asian crisis. The enhanced local asset price volatility relative to CECF price volatility evident during crisis periods (Chandar and Patro, 2000), suggests that extracting accurate signals on the value of fundamentals from local asset prices becomes more difficult during crises. This may lead investors to place more reliance upon external information sources, such as CECF prices. The estimates of Eq. (1) indicate that the expectations of foreign investors, as incorporated in CECF returns, appear to have a pervasive impact upon local asset returns. To control for the possibility that the influence of CECF returns on the relevant stock market returns simply reflects the global impact of US market sentiment, we incorporate US market (S&P 500) returns as an exogenous variable in Eq. (1) as suggested by Hardouvelis et al. (1994) and Bodurtha et al. (1995), 22 The Frankel and Schmukler, (1998a) data period ends in December 1996. As the main focus of this paper is the short-run information transmission process, we do not explore this issue further, (but see Frankel and Schmukler, 1998a for further insights). We note that the size of the hs obtained using NAVs, while qualitatively similar, are smaller in absolute value than those obtained using local indices or IFC data. 23 A second rationale highlights the difficulties, (or costs) for US investors to acquire information on funds relating to emerging equity markets, which may be perceived as having insufficient depth and liquidity to develop accurate asset-pricing signals. As a result, CECF prices may be ‘sticky’ to market developments which reflect fundamentals, leading investors to place undue reliance on information relating to US market developments as a substitute for such information.

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among others. The clear rejection of the hypothesis that q2i = 0 for all countries, establishes a clear and significant independent role for US market sentiment in influencing local Asian market asset returns, thereby justifying its inclusion in the model. This impact is distinct from that of country-specific foreign investor expectations already incorporated in the CECF returns. This result is invariant to the use of alternative measures of local asset values, and also holds in the multivariate VAR specification.24 There is mixed evidence in support of the hypothesis that CECF returns are determined independently of US market sentiment factors. This hypothesis, that q1i = 0 cannot be rejected for Taiwan, Korea and Thailand, but US sentiment appears to be important in determining returns for the remaining country funds. We further investigate these preliminary findings on the heterogeneity of foreign investor expectations in the next section.

4. Short-run information transmission and foreign investor trading behaviour

4.1. Foreign in6estor sentiment: undifferentiated or country-specific This section’s immediate objective is to more fully establish the nature of the foreign investor sentiment factors. The analysis proceeds by estimating the trivariate VAR in Eq. (2) in Asian stock market returns, CEFC returns, and US market (S&P 500) returns, over both the full sample and also the crisis/non-crisis period sub-samples25: FRt =‚10 +A11(L)FRt − 1 +A12(L)LRt − 1 + A13(L)USRt − 1 + m1t LRt =‚20 +A21(L)FRt − 1 +A22(L)LRt − 1 + A23(L)USRt − 1 + m2t USt =‚30 +A31(L)FRt − 1 +A32(L)LRt − 1 + A33(L)USRt − 1 + m3t

(2)

where the ‚i0 terms are parameters representing the intercept term, and Aij s are the polynomials in the lag operator L (which is defined as L jZt − 1 = Zt − 1 − j for Z= FR, LR, USR).

24 This finding is of potential importance in another context. Recent studies demonstrate that the sensitivity of any inference of causality in a VAR can be affected by failing to consider the impact of another important ‘causing’ variable, (Carporale and Pittis, 1997; Hassapis et al., 1999). It is apparent that incorporating returns on the S&P 500 improves the VAR specification, thereby increasing the confidence associated with any attributed causal interaction between CECF and stock index returns. 25 As further corroboration of the results from Eq. (1), we conduct block exogeneity tests, on the VAR system in Eq. (2) for the full data sample, together with the crisis/non-crisis sub-samples. The tests possess the advantage that they allow one variable to affect another through the remaining equations in the system of equations. Likelihood ratio tests are conducted on the appropriate cross-equation restrictions, that A13(L) and A23(L) are jointly equal to zero. Overall, except for Taiwan and Korea in the non-crisis period, the results clearly reject the null hypothesis that the lagged values of the S&P 500 return series do not enter the equations.

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The major advantage of the trivariate VAR formulation, is that it facilitates consideration of several hypothesis never previously addressed in the literature.26 We formulate these hypotheses in what we term a strong and weak form, defined in relation to the direct and indirect causal impact of both CECF and US market returns on local market returns. They are then translated into a set of corresponding restrictions on the significance of the lagged Aij polynomial coefficients from the trivariate VAR in Eq. (2). The hypotheses of immediate interest in relation to foreign investor expectations are: “ strong composite foreign sentiment (direct causality from both CECF and US returns): A21(L) "0 and A23(L) "0; “ strong country-specific foreign sentiment (direct causality from CECF; no direct or indirect causality from US returns): A21(L)" 0, A23(L)= 0, and A13(L)= 0; “ weak country-specific foreign sentiment (direct causality from CECF; indirect causality from US returns): A21(L) "0, A23(L)= 0, and A13(L)" 0; “ strong undifferentiated foreign investor sentiment (direct causality from US returns; no direct or indirect causality from CECF returns): A21(L)= 0, A23(L) "0, and A31(L) =0; “ weak undifferentiated foreign investor sentiment (direct causality from US returns; indirect causality from CECF returns): A21(L)= 0, A23(L)" 0, and A31(L) "0. Table 2 presents the results which indicate that countries (and the associated funds) can analytically be separated into distinct groups. Three countries, Indonesia, Thailand and Singapore present a consistent picture. Strong composite foreign sentiment27 effects emerge across all three samples. Both country-specific and undifferentiated US market sentiment factors appear to be important in predicting local stock index returns for these countries. Strong composite foreign sentiment also characterises a second group of countries, Malaysia and the Philippines, but only during non-crisis periods. During crisis periods the impact of sentiment differs. In the case of Malaysia, foreign investor sentiment appears undifferentiated, with any discernible impact completely transmitted through the S&P 500 index. For the Philippines, during the crisis undifferentiated sentiment has a marginal impact at best, with foreign investor sentiment appearing to be strongly country-specific.

26 A second advantage is that the formulation can be used to corroborate the results of both the estimates of Eq. (1) and the cointegrating transformation of the multivariate VAR, whose short-run dynamics only have a meaningful interpretation in the presence of cointegration between the variables. As evidence of cointegration noted earlier, (at the 5% level at least) is weak, the specification adopted in Eq. (1) may be subject to pre-test specification biases. We note that the qualitative nature of the following results are confirmed using the robust methodology suggested by Dolardo and Lutkepohl, (1996). 27 In the discussion of this section, particularly in formulating the empirical hypothesis, we do not imply anything about the rationality or otherwise of investor ‘sentiment’. In particular it should not be identified solely with noise trading, but construed in a more general sense akin to expectations, independently of the process of their formation.

Length of Non-crisis VAR (AIC); non-crisis/crisis CECF returns; A12(L) =0

Philippines

3/1

Indonesia

10/6

Malaysia

2/3

Taiwan

3/2

ROC Taiwan Singapore

5/2

Thailand — July Thailand — March Korea — July Korea — Oct

1/1 6/3 4/3

4/5 4/5

Crisis CECF returns; A13(L) =0

Index returns; A21(L) =0

Index returns; A23(L) =0

CECF returns; A12(L) = 0

CECF returns; A13(L) = 0

Index returns; A21(L) = 0

Index returns; A23(L) = 0

19.306 (0.000)d 28.242 (0.002)d 3.371 (0.185) 3.007 (0.371) 5.293 (0.381) 3.963 (0.047)c 35.588 (0.000)d 25.274 (0.000)d

14.263 (0.000)d 29.713 (0.001)d 12.070 (0.002)d 8.337 (0.040)d 8.689 (0.122) 9.407 (0.002)d 15.588 (0.016)c 14.760 (0.005)d

18.456 (0.000)d 47.501 (0.000)d 7.951 (0.019)c 17.126 (0.001)d 10.702 (0.048)c 4.578 (0.032)c 22.652 (0.001)d 19.113 (0.001)d

17.183 (0.001)d 27.536 (0.001)d 25.233 (0.000)d 3.630 (0.304) 5.649 (0.342) 39.725 (0.000)d 12.943 (0.044)c 15.614 (0.004)d

6.703 (0.010)d 18.328 (0.005)d 11.713 (0.008)d 5.983 (0.050)c 3.212 (0.201) 1.527 (0.217) 20.492 (0.000)d 20.606 (0.000)d

1.474 (0.225) 14.344 (0.026)c 6.156 (0.104) 3.566 (0.168) 0.691 (0.708) 0.742 (0.389) 0.191 (0.979) 0.276 (0.789)

7.504 (0.006)d 12.941 (0.044)c 4.907 (0.179) 3.916 (0.141) 2.424 (0.298) 4.408 (0.036)c 13.370 (0.004)d 12.266 (0.007)d

3.0186 (0.082)b 15.145 (0.019)c 10.754 (0.013)c 19.585 (0.000)d 24.051 (0.000)d 6.363 (0.012)c 10.461 (0.015)c 8.375 (0.039)c

10.209 (0.037)d 10.507 (0.039)d

11.815 (0.019)c 10.356 (0.024)c

13.336 (0.010)d 15.712 (0.003)d

2.769 (0.597) 2.425 (0.658)

13.692 (0.018)c 12.679 (0.024)c

5.591 (0.348) 4.697 (0.304)

20.266 (0.001)d 19.438 (0.002)d

10.055 (0.074)b 9.593 (0.088)b

For an explanation of the restrictions see Eq. (2) in the text. Figures in parentheses are P-values. Figures in the table denote values of the  2-test statistic from the Wald test, with serial correlation and/or heteroscedasticity corrections undertaken with White or Newey–West methods as appropriate. Optimal VAR length obtained from Akaike’s Information Criteria (AIC). b Significant at the 10% level. c Significant at the 5% level. d Significant at the 1% level. a

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Country fund

410

Table 2 Foreign investor behaviour and Asian market indices: Non-crisis/crisis sub-samplesa

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411

The final group consists of the second control country, Taiwan, and Korea. The latter presents the clearest example of foreign investor sentiment being entirely transmitted through country-specific channels. Korean stock returns appear to be generally immune to the impact of undifferentiated foreign market sentiment factors, apart from a weak effect, observable only during non-crisis periods. The results for the Taiwanese funds differ markedly between non-crisis and crisis periods. In non-crisis periods, investor sentiment appears country-specific, weakly so for the Taiwan fund, and strongly so for Roc Taiwan. During the crisis, sentiment becomes strongly undifferentiated, with country specific factors playing no apparent role, and returns linked closely to those in the US market. The fact that Taiwan emerges relatively intact from the effects of the Asian crisis suggests that the Taiwanese market’s links to developments in the rest of the world, as characterised by the behaviour of the US market, were strengthened at this time. In conclusion, we note that the findings provide no evidence that capital market restrictions are an important determinant of the links between Asian and US equity markets. None of the results is directly attributable to a given market’s openness to foreign investment, thereby corroborating the substantial body of evidence (Phylaktis, 1997, 1999) that the nature of capital market integration between Asian and US markets is invariant to the existence of foreign ownership limits and exchange controls.

4.2. Information flow dynamics and foreign in6estor sentiment: moderating or exacerbating The preceding analysis identifies measurable differences in the nature of information flows between Asian equity markets and the relevant CECF prices in New York during crisis and non-crisis periods. This section models the short-run information dynamics in more detail, focusing on two questions. First, is there any evidence to suggest that any detected impact of foreign investor sentiment on local asset returns is either moderating or exacerbating during a financial crisis? Second, to what extent do local fundamentals in the relevant Asian markets influence the prices of the corresponding CECF in subsequent trading on the NYSE; moreover, does the nature of this influence change over the Asian crisis period.

4.3. Responsi6eness of local asset returns to foreign trading To address the first question, and clearly identify the direction in which any detectable foreign investor sentiment originating in New York exerts its impact, we regress close-to-close market returns in Asia on the preceding close-to-close CECF returns in New York encompassed by the Asian market returns. The regression again includes close-to-close returns on the S&P 500 to control for the global effects of undifferentiated US market sentiment, and interaction terms with the crisis dummy variable to see whether the onset of the crisis changes the measured relationships. The estimated specification has the general form:

412

Country fund

a0

a1

Philippines Indonesia Malaysia Taiwan ROC Taiwan Singapore Thailand — July Thailand — Mar Korea — July Korea — Oct

0.001 (2.855)c 0.001 (2.284)b 0 (1.363) 0.001 (2.286)b 0.001 (2.266)b 0 (1.719) 0 (0.935) 0 (0.508) 0 (1.429) 0 (1.164)

−0.004 −0.007 −0.004 −0.003 −0.003 −0.002 −0.003 −0.003 −0.003 −0.001

a

A2(L) (−2.367)b (−2.011)b (−1.670) (−2.286)b (−2.373)b (−1.710) (−1.158) (−1.176) (−1.138) (−0.947)

0.129 0.125 0.079 0.103 0.057 0.054 0.147 0.148 0.128 0.135

A3(L) (5.267)b (3.789)b (2.877)b (4.079)b (2.344)b (2.906)c (4.478)c (4.612)c (4.672)c (5.082)c

0.093 0.100 0.149 0.032 0.013 0.118 0.103 0.104 0.255 0.264

A4(L) (1.977)b (1.973)b (1.519) (0.503) (0.237) (2.097)b (1.460) (1.428) (2.865)c (2.697)c

For an explanation of the coefficients see Eq. (3) in the text. Figures in parentheses are t-values. Significant at the 5% level. c Significant at the 1% level. b

0.278 0.338 0.262 0.100 0.119 0.293 0.337 0.287 0.213 0.202

R2

A5(L) (4.847)c (2.838)c (3.924)c (1.662) (1.932) (5.966)c (3.144)c (2.834)c (2.839)c (2.913)c

−0.023 0.367 0.371 0.321 0.337 0.035 −0.074 0.036 −0.229 −0.273

(−0.142) (1.402) (1.878) (2.810)c (2.850)c (0.234) (−0.336) (0.147) (−0.988) (−0.981)c

0.11 0.07 0.08 0.06 0.04 0.09 0.07 0.07 0.08 0.06

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Table 3 Foreign investor behaviour: moderating or exacerbatinga

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413

LRt =a0 +a1D +A2(L)FRt − 1 +A3(L)D.FRt − 1 + A4(L)USRt − 1 +A5(L)D.USRt − 1 +mt

(3)

where mt is an error term orthogonal to the other right-hand side variables. The specification is corrected for conditional heteroscedasticity and results accommodate serial correlation, following the adjustments suggested in White (1980) and Newey and West (1987), respectively. Two sets of estimates are obtained. Initially, we restrict j= 0 in the lag operator. The advantage of this restriction is that it clearly identifies the direction of influence of any foreign investor sentiment.28 It also allows a direct comparison of our results with Bennett et al. (1998), the only other study familiar to us which analyses CEFC price behaviour during the Asian crisis in relation to daily asset pricing behaviour in the underlying local market. Coefficient estimates are reported in Table 3. However, the evidence of significant short-run stickiness in the relationship between CECF and the relevant local market returns (Hardouvelis et al., 1994), suggests the one-period dependence assumption of the Markovian framework may be unduly restrictive in attempting to fully capture the information flow dynamics.29 To more Table 4 The significance of Asian equity market price information on CECF price formationa Country fund

Optimal lag length on (L)

A2(L)

A3(L)

A4(L)

A5(L)

Philippines Indonesia Malaysia Taiwan ROC Taiwan Singapore Thailand — July Thailand — March Korea — July Korea — Oct

3 6 3 3 5 1 4

32.373 (0.000)d 25.107 (0.000)d 6.941 (0.074) 18.529 (0.000)d 10.076 (0.073) See Table 5 29.307 (0.000)d

18.931 (0.000)d 9.164 (0.065)b 4.200 (0.241) 1.934 (0.586) 9.639 (0.084) See Table 5 5.876 (0.209)

24.014 (0.000)d 20.253 (0.002)d 24.108 (0.000)d 2.990 (0.393) 5.960 (0.310) See Table 5 7.632 (0.106)

11.470 (0.009)d 6.752 (0.344) 4.725 (0.193) 14.615 (0.002)d 15.199 (0.010)d See Table 5 11.498 (0.022)c

4

29.971 (0.000)d 4.329 (0.363)

11.634 (0.020)c

7.479 (0.113)

5 5

23.884 (0.000)d 18.466 (0.002)d 8.438 (0.134) 27.580 (0.000)d 19.099 (0.002)d 8.010 (0.156)

10.510 (0.062)b 9.358 (0.096)b

a For an explanation of the restrictions see Eq. (4) in the text.Figures in parentheses are P-values. Figures in the table denote values of the  2-test statistic from the Wald test, with serial correlation and/or heteroscedasticity corrections undertaken with White or Newey–West methods as appropriate. Optimal VAR length obtained from Akaike’s Information Criteria (AIC). b Significant at the 10% level. c Significant at the 5% level. d Significant at the 1% level.

28 In the discussion of this section, particularly in formulating the empirical hypothesis, we do not imply anything about the rationality or otherwise of investor ‘sentiment’. In particular it should not be identified solely with noise trading, but construed in a more general sense akin to expectations, independently of the process of their formation. 29 This sets j = 0 in the lag operator. The Markov framework that results in first-order models is extensively employed in this empirical literature as it greatly facilitates analysis of the links between unit roots, causality and co-integration.

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accurately measure the strength and overall significance of the adjustments, we conduct a specification search to determine the appropriate lag length in Eq. (3), selecting that which minimises the Akaike Information Criteria (AIC). Wald tests then examine the hypothesis that the set of the coefficients in each of the fitted lag polynomials, Ai (L) are insignificantly different from zero, with results reported in Table 4. The results in Tables 3 and 4 are consistent with those derived from the VAR specification. During both crisis and non-crisis periods, country-specific foreign investor sentiment incorporated in the CECF returns, has a significant impact on local Asian asset returns in all countries. These results contrast sharply with those of Bennett et al. (1998), who find evidence of significant country-specific sentiment only in the case of Thailand. Moreover, the positive coefficient on CECF returns during the Asian crisis period indicates that the onset of the crisis strengthens the role of country-specific foreign investor sentiment, both in those countries affected by the crisis, and in the control group. These effects are particularly significant not only for Korea and Indonesia, as noted by Bennett et al. (1998), but also for the Philippines, Singapore and to a lesser extent, Thailand. A possible explanation for these findings is as follows. Chandar and Patro (2000), note that local asset price volatility increases both absolutely, and relative to CECF volatility, during crisis periods. The increased crisis period impact of CECF returns on local asset returns may reflect an enhanced difficulty in extracting accurate information on the value of fundamentals from local price movements. During the crisis, evidence suggests that local prices effectively become noisier signals, leading investors to place more reliance upon relevant external sources of information, such as CECF prices. However, while the coefficients in Table 3 are all positive, they are far lower than unity, again indicating a sluggish adjustment of the local index to foreign price changes. Local markets incorporate foreign information into asset prices, but do so with a degree of caution. We contend that these results lend some, albeit qualified, support for assigning some role in propagating the Asian crisis to the trading behaviour of foreign investors. With the exception of Taiwan (and Korea in the unrestricted lag specification), Tables 3 and 4 indicate undifferentiated US market sentiment exerts a significant positive influence on local market returns during non-crisis periods. Again this contrasts with Bennett et al.’s (1998) finding of significant undifferentiated sentiment only in the case of Indonesia, Malaysia and the Philippines. There is no evident change in the impact of undifferentiated sentiment during the crisis period for the Asian countries as a whole. Indeed, its cumulative impact is significantly weaker for Korea, the Philippines, and Thailand. Considered in relation to the detected increase in country-specific foreign sentiment in the former two countries, this suggests that the channel through which foreign investor expectations affect crisis economies becomes increasingly country-specific during periods of market turmoil. Undifferentiated sentiment appears stronger during a crisis in Indonesia, Malaysia, Singapore and Taiwan, but only in the latter is there a significant difference from its impact in the non-crisis period. The fact that Taiwan escaped the

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415

major adverse effects of the Asian crisis seems to have consolidated its links to the US market.30

4.4. Responsi6eness of CECF prices to local market fundamentals We now turn to address the relation between CECF prices and local market fundamentals. The specification design is careful to allow for non-overlapping trading hours. On any given calendar weekday, the NYSE opens for trading following market closure in Asia, and closes about 5 h before the Asian exchanges re-open for the following day’s trading. Clearly, information arising from events in an Asian market on a given calendar day can influence the subsequent trading during the same calendar day in New York, but not the reverse. Our test specification accounts for this fact31, taking the general form: FRt =b0 +b1D +B2(L)LRt +B3(L)D.LRt + B4(L)USRt − 1 +B5(L)D.USRt − 1 +B6(L)FRt − 1 + B7(L)D.FRt − 1 + mt

(4)

where mt is an error term orthogonal to other right-hand side variables. The dependent variable is the one-period CECF return. The right-hand side of the regression includes lagged values of close-to-close CECF returns to control for the observed mean-reversion in CECF discounts, and for the fact that the close-to-close Asian market returns could reflect previous trading in New York. Close-to-close returns on the S&P 500 are included to control for US market sentiment. The interaction of the dummy variable, D, with the other independent variables enables us to determine whether the onset of the crisis changes the measured relationships. Again, White (1980) and Newey and West (1987) corrections are undertaken where appropriate. For the reasons elaborated in relation to Eq. (3), we undertake the analysis with both a restricted ( j= 0) and unrestricted lag operator. The results are presented in Tables 5 and 6 for each of the eight CECFs. In the non-crisis period, daily movements in the local stock market indexes exert strong positive effects on CECF prices. Moreover, the estimated coefficients for B2 in Table 5, while highly significant are less than one, reflecting a degree of sluggishness in the adjustment of CECF prices to local asset price changes. This is consistent with the results obtained in previous studies using weekly NAV data (Hardouvelis et al., 1994; Bodurtha et al., 1995; Frankel and Schmukler, 1998a; Chandar and Patro, 2000). It is also consistent with the hypothesis that foreign investors underreact to news that affects local market fundamentals (Pontiff, 1997). 30 These results are broadly in line with Bennett et al., although they also note significant increases for Malaysia and Indonesia. The other countries exhibited no significant change. One rationale for the difference in results, in addition to the use of different sample periods, is the fact that Bennett et al. utilise open-to-close return, as opposed to close-to-close returns, for both the CECF and S&P 500 series. Thus, they are measuring the local index return series over a 24-h period and the foreign series over one trading period. This may be significant given the concensus the return series exhibit mean-reversion. 31 The earlier VAR formulation is unable to incorporate this feature, as the right-hand side variables must be identical in each equation. It is also ignored by Chandar and Patro, (2000) in their analysis of CECF responsiveness to the underlying NAVs, which may lead to bias in their results.

416

Country fund

b1

B2(L)

B3(L)

B4(L)

B5(L)

B6(L)

B7(L)

R2

Philippines

−0.002 (−1.104) −0.005 (−1.905) −0.004 (−1.696) 0 (−0.685) 0 (−0.280) −0.002 (−1.450) −0.003 (−1.319) −0.002 (−1.226) −0.001 (−0.746) 0 (−0.450)

0.647 (13.315)d 0.340 (3.274)d 0.399 (3.401)d 0.538 (13.974)d 0.595 (15.688)d 0.796 (11.695)d 0.498 (9.656)d 0.528 (9.514)d 0.544 (16.100)d 0.556 (16.746)d

−0.133 (−1.861)b −0.802 (−0.271) −0.148 (−0.981) 0.021 (0.308) 0.198 (2.107)c −0.310 (−3.415)d −0.105 (−1.349) −0.149 (−1.935)b −0.047 (−0.797) −0.065 (−1.099)

0.101 (1.313) 0.183 (1.572) 0.229 (2.520)d 0.136 (1.853)b 0.109 (1.435) 0.071 (0.997) −0.070 (−0.663) −0.114 (−0.970) 0.026 (0.344)

−0.522 (−1.900)b 0.104 (0.372) −0.118 (−0.640) −0.231 (−1.622) −0.426 (−2.274)c −0.144 (−1.060) −0.166 (−0.589) −0.050 (−0.197) −0.552 (−2.126)c −0.625 (−2.072)c

−0.060 (−1.895)b −0.127 (−3.321)d 0.022 (0.458) −0.038 (−1.118) −0.086 (−2.535)d −0.112 (−2.859)d 0.068 (1.842)b 0.070 (1.807)b −0.071 (−2.042)c −0.674 (−1.992)c

−0.070 (−0.825) −0.132 (−1.467) −0.549 (−0.559) 0.020 (0.354) −0.029 (0.450) 0.057 (0.903) −0.097 (−1.676) −0.097 (−1.678) −0.068 (−0.960) −0.065 (−0.845)

0.19

Indonesia Malaysia Taiwan ROC Taiwan Singapore Thailand — July Thailand — March Korea — July Korea — Oct

a

0.016 (0.255)

For an explanation of the coefficients see Eq. (4) in the text. Figures in parentheses are t-values. Significant at the 10% level. c Significant at the 5% level. d Significant at the 1% level. b

0.12 0.11 0.18 0.18 0.26 0.26 0.17 0.20 0.17

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Table 5 Information transmission from Asian market indices to CECF pricesa

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Table 6 Foreign investor behaviour: impact significancea Country fund

Optimal lag length on (L)

B2(L)

B3(L)

B4(L)

B5(L)

Philippines

3

Indonesia

6

Malaysia

3

Taiwan

3

ROC Taiwan

5

Singapore Thailand — July Thailand — March Korea — July

1 4

Korea — Oct

5

218.285 (0.000)d 17.696 (0.013)c 13.079 (0.011)c 245.971 (0.000)d 270.045 (0.000)d See Table 5 146.882 (0.000)d 149.338 (0.000)d 276.984 (0.000)d 296.873 (0.000)d

8.695 (0.049)c 6.838 (0.446) 15.442 (0.028)c 4.306 (0.366) 8.963 (0.176) See Table 5 21.074 (0.001)d 23.167 (0.000)d 6.257 (0.395) 6.390 (0.381)

4.956 (0.175) 10.991 (0.089)b 9.202 (0.027)c 12.437 (0.006)d 11.512 (0.042)c See Table 5 4.644 (0.326) 5.671 (0.225) 3.976 (0.553) 4.185 (0.523)

13.148 (0.041)c 4.065 (0.255) 1.553 (0.670) 5.774 (0.123) 6.202 (0.287) See Table 5 0.708 (0.950) 0.587 (0.965) 5.395 (0.370) 5.989 (0.307)

4 5

a For an explanation of the restrictions see Eq. (4) in the text. Figures in parentheses are P-values. Figures in the table denote values of the chi-squared test statistic from the Wald test, with serial correlation and/or heteroscedasticity corrections undertaken with White or Newey–West methods as appropriate. Optimal VAR length obtained from Akaike’s Information Criteria (AIC). b Significant at the 10% level. c Significant at the 5% level. d Significant at the 1% level.

The onset of the crisis appears to weaken the effect of local fundamentals on CECF prices. The coefficient on B3 is negative for the five crisis countries and Singapore, and positive only in the case of Taiwan, although the estimates are not statistically significant for Indonesia or South Korea. Moreover, they are weakly significant for Thailand only if the onset of the crisis is dated from March 1997, and for Malaysia only in the specification with the unrestricted lag operator. Bennett et al. (1998) argue that this weaker effect explains the pricing relationships exhibited by Asian funds during the crisis. They maintain that CECF prices simply lagged behind movements in their NAVs as local stock prices plummeted, thereby accounting for the fact that for the crisis countries, the trading discounts usually observed on such country funds turned into premia. However, while a weakening of the impact of local fundamentals is consistent with this reversal, the fact that Singapore, a non-crisis country exhibits an analogous weakening without undergoing a discount reversal, suggests this explanation is only partial. The results also indicate that with the exception of Malaysia in the non-crisis period, undifferentiated US market sentiment has little significant impact on CECF returns, and whatever effect it exhibits weakens during the crisis, significantly so for the Philippines, Taiwan and Korea, although for the latter country only in the restricted lag specification.

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The weaker effect of local market returns on CECF prices noted during the crisis period, is not inconsistent with studies of the October 1987 crash which argue that volatility tends to strengthen spillover effects among equity markets.32 Rather, in conjunction with the earlier results, those in Tables 5 and 6 suggest that the transmission mechanism giving rise to spillovers may be unidirectional. Augmented Table 7 Definition of variables used in regressions Notation

Definition of variable

Variable calculation

Lt

Asian market underlying asset price levela at close of trading period t CECF price level at close of trading period t Local Asian asset market dollar return from close of trading period t−1 to t Local Asian asset market dollar return from close of trading period t−2 to t−1 CECF return in New York from close of trading period t−1 to close of period t CEFC return in New York from close of trading period t−2 to close of period t−1 S&P 500 return in New York from close of trading period t−1 to t S&P 500 return in New York from close of trading period t−2 to t−1 Dummy variable equal to 1 during the crisisb and equal to 0 otherwise Local stock market dollar return in relevant market during crisis; 0 otherwise CECF return in New York during crisis; 0 otherwise S&P 500 return in New York during crisis; 0 otherwise

Local currency converted to US dollars at spot ex. rate Measured in US dollars

Ft LRt LRt−l FRt FRt−l USRt USRt−1 D DLRt DFRt DUSRt

ln Lt−ln Lt−l ln Lt−1−ln Lt−2 ln Ft−ln Ft−1 ln Ft−1−ln Ft−2 ln SPt−ln SPt−1 ln SPt−1−ln SPt−2 See below D×LRt D×FRt D×USRt

a The local Asian market price level refers to the US value of either the local equity market index, the underlying NAVs of the CEF, or the IFC investables index, expressed in logarithms. NAVs trading periods are weekly. b Certain tests differentiate between crisis and non-crisis periods. For the Philippines, Indonesia, Malaysia, Taiwan and Singapore the crisis period is defined as beginning on the first trading day of July 1997. In the case of Thailand and South Korea, the tests are also performed with a second alternative crisis period. For Thailand, this date is March 1997, when the financial problems of several Thai finance companies become public information. Subsequently, the Thai stock market fell 25% over the next 90 days. For South Korea, the alternative date is October 1997 when several of the countries large banks were downgraded by credit agencies and the stock market embarked upon a rapid decline. In all cases, the results we report use the end of September 1998 as the end date of the crisis period. We experimented with alternative end dates over the period September 1998 to January 1999. Tests could not identify clear structural breaks in the data. The qualitative results are not sensitive to the choice of end date.

32 See for example, Dwyer and Hafer, (1988), King and Wadhwani, (1990), and articles in the summer edition of the Federal Reserve Bank of New York Quarterly Review, 1, 1988.

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spillover effects are here attributable to the increased impact on local markets of information flows originating with foreign investors.33 5. Summary and conclusion This study analyses the dynamic relationship between CECF prices and the relevant underlying asset market values for a group of seven Asian countries for a period incorporating the 1997 Asian financial crisis. The central objective is to ascertain the importance for the behaviour of equity returns in Asian markets, of both country-specific and undifferentiated foreign investor sentiment (originating in the US market), relative to the expectations of local investors. Robust results are ensured through utilisation of a variety of estimation methods and three different data proxies for the value of Asian market assets, namely the local Asian equity market index, the relevant NAVs underlying the CECF, and the IFC investable index. The following general conclusions emerge. There is strong evidence to suggest that both local and foreign investor expectations are jointly important as a channel linking the price behaviour of Asian assets trading in both Asian and US equity markets. The results also corroborate those of Hardouvelis et al. (1994) and Klibanoff et al. (1998) that CECF prices in New York underreact to information on local fundamentals as conveyed by the underlying value of Asian assets. There is evidence, albeit not conclusive, to suggest that the onset of the crisis weakened the impact of Asian market fundamentals on CECF prices in New York. 33 We note that to analyse the transmission dynamics in more detail we also undertook impulse response, (IR) analysis. We select generalised IR, (Koop et al., 1996) in preference to orthogonalised IR, as the formers results are insensitive to the ordering of the variables in the VAR. We measure the response of one variable, X to an autonomous, one S.D., positive shock in another variable, Y, over a cumulative timeframe. The analysis is conducted both on the three variable VAR in Eq. (2), (with stationary return series), and a cointegrated bivariate VAR between CECF prices and local Asian market indices, specified in levels of the, (non-stationary) variables. Detailed results are available from the authors: we now briefly summarise. All responses are positive. In Eq. (2), non-crisis periods impact responses across all equations are low in magnitude and comparable for shocks to all three return series. Convergence to equilibrium is rapid, (2 –4 days). During the crisis, measured responses are somewhat higher, (except for shocks to US returns) although still low in absolute value; convergence times also increase to 3 –6 days, except for Indonesia and Korea where it averages 10 – 12 days. Results from the cointegrated bivariate VAR system should be interpreted with caution, given the well-documented problems encountered by IR analysis within cointegrated systems, (Lutkepohl and Reimers, 1992; Naka and Tufte, 1997). The most important finding is the fact that in general, for all periods, the reaction of local Asian indexes to a unit shock in CECF prices is significantly larger and more persistent than the reverse reaction of CECF prices to a unit shock in the equation for the local indices. When local indexes are shocked, the resulting CECF reaction is very small and stable. When CECF prices are shocked, the magnitude of the response in local indices is positive and with a magnitude between 0.4, (Korea) and 2.8, (Malaysia). There is however, evidence of both permanent shifts in the system’s equilibrium level, (Lutkepohl and Reimers, 1992), and of explosive behaviour in the IR function for some countries, namely Korea, Taiwan and to a lesser extent, Malaysia, (for a potential explanation, see Naka and Tufte, 1997). While these IR results complement those in the text, we recommend that they be interpreted with caution.

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This nature of the systematic links between the Asian and US equity markets are found to be independent of the extent to which the Asian equity markets are open to foreign investment, supporting the results of Phylaktis (1997); Phylaktis (1999). There is also no strong evidence that the information transmission mechanism varies systematically between the five crisis countries and the non-crisis control group of countries, Taiwan and Singapore. The central contribution of this analysis is the discovery that the impact of country-specific foreign investor information is not only significant for the Asian stock markets in non-crisis periods, but that its effect is enhanced during periods of financial crisis. We conjecture this may reflect the fact that the local Asian asset prices appear to become noisier signals of fundamental value at such times, leading investors to place more reliance upon relevant alternatives. These results contrast with those obtained by Bennett et al. (1998) for Asia, and by Frankel and Schmukler (1996) using NAVs for the 1994 Mexico crisis, the latter concluding that ‘local investors were more alert to potential warning signals’, and were at the forefront of the crisis. However, in a later study (Frankel and Schmukler, 1998a, p.22), they maintain that foreign investors may have treated the Pacific Rim and Mexico differently. Our evidence supports this conjecture. It also complements the findings of Bennett et al. (1998), that pessimism reflected in US equity markets contributed to the depreciation of Asian currencies during the crisis period. Overall, we contend that these results lend some credibility to the view that the trading behavior of foreign investors played a measurable role in sustaining the dimension and duration of the financial crisis in Asia. Certain issues remain unexplored by this analysis offering potential for further research. In particular, the study could be extended to include alternative measures of foreign investor sentiment towards emerging markets, for example by incorporating an emerging market index series, or emerging market yield spreads.34 Frankel and Schmukler (1998b), in one of the few studies to analyse the relationship between contagion and financial crisis, follow this procedure in testing for contagion from Mexico to Asian and Latin American countries. This issue, together with a detailed analysis of the nature of regional spillovers between crisis-affected economies still remains relatively unexplored.

Acknowledgements This research was undertaken with support from the European Union’s Phare ACE Programme. The content of the publication is the sole responsibility of the authors and in no way represents the views of the Commission or its services. The authors would also like to acknowledge the support of the Chartered Institute of Bankers. This paper has benefited from the insights of Ian Garrett, Brahim Saadouni and helpful discussions at the University of Manchester. Maria Ketch-

34

A referee of this paper made suggestions along similar lines.

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ledge of Wiesenberger® kindly provided some of the data used in this study. The authors wish to acknowledge the constructive comments of an anonymous referee and the editor, Rich Lyons. The usual disclaimer applies.

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