Sovereign Debt Rating Changes and Stock Liquidity around the World Kuan-Hui Lee Seoul National University Business School Horacio Sapriza Federal Reserve Board at Washington DC Yangru Wu Rutgers Business School April 12, 2011 SNU Institute for Research in Finance and Economics Seoul National University Sovereign debt rating • Sovereign debt ratings are assessments of the probability of default in government bonds. – Rating agencies consider economic and political factors and make a qualitative and quantitative evaluation. • By doing so, sovereign debt rating changes deliver new information about a country, as evidenced by their significant effects on: – Stock market returns: • Kaminsky and Schmukler (2002), Brooks, Faff, Hiller, and Hiller (2004), Martell (2005) – Individual stock returns: • Martell (2005), Lee, Sapriza, and Wu (2010) – Bond yields/spreads: • Cantor and Packer (1996), Larrain, Reisen, and von Maltzan (1997), Gande and Parsley (2005) This paper is about … • However, there are no studies investigating the effect of sovereign debt rating changes on stock liquidity. Our paper fills in that gap. – We study the impact of changes in sovereign credit ratings on daily stock liquidity for 40 developed and emerging markets from January 1990 to December 2009. – Stock-level analysis: Cross-sectional variation of impact – Cross-country analysis: differences in response over countries – Regression approach rather than event-study framework (Gande and Parsley, 2005) • The importance of liquidity has been growing rapidly both for researchers and for practitioners. Why is liquidity important? “Whenever the market turns against you, you take the biggest losses in illiquid securities,” says Richard Bookstaber, former head of risk management at Salomon Bros. “Because there are so few buyers, you’re forced to sell at a discount that is both huge and highly unpredictable.” (Fortune, November 26, 2007) • Liquidity affects asset prices. • Amihud and Mendelson 1986, Brennan et al 1996, Amihud 2002, Pastor and Stambaugh 2003, Acharya and Pedersen 2005, Liu 2006, Sadka 2006, Watanabe and Watanabe 2008, Lee 2011 • Liquidity is systematic, and its commonality may lead to the fragility of financial markets. • Brunnermier and Pedersen 2009, Hameed, Kang, and Viswanathan 2010, Karolyi, Lee, and van Dijk 2010 • Liquidity affects market efficiency. • Chordia et al 2008 How rating changes could be related to liquidity? • Sovereign debt ratings impose a significant externality to the country’s private sector, which can affect investors’ incentives to hold stocks. Especially, sovereign downgrades may lead to: – Capital outflows (Reinhart and Rogoff, 2004; Kim and Wu, 2008; Gande and Parsley, 2010), which may be accompanied by fire sales of assets, thus, liquidity constraint (Pastor and Stambaugh, 2003; Acharya and Pedersen, 2005) • Low ratings are related to high adverse selection, thus to low liquidity in equity (Odders-White and Ready, 2006). – In times of market stress, bond investors chase liquidity (flight-to- liquidity), not credit quality (Beber, Brandt, and Kavajecz, 2009) – Low ratings are also related to low liquidity in bonds (Cantor and Packer, 1996) How rating changes could be related to liquidity? • Sovereign downgrades may lead to limited access to international capital markets (Dittmar and Yuan, 2008), – which, in turn, limits liquidity due to limited diffusion of information in the global capital markets (high information asymmetry). • Sovereign ceiling lite (Ferri , Lui, and Majnoni, 2001; Ferri and Liu, 2002; Kaminsky and Schmukler, 2002; Borensztein, Cowan, and Valenzuela, 2007) • More information asymmetry at the time of earnings announcement (Kim and Verrecchia, 1994) – Funding constraints of traders (e.g., hedge funds, banks, market- makers) affect, and are affected by, market liquidity: • Intermediaries make markets by absorbing liquidity shocks subject to funding constraints (via posted margins on collateral). When markets decline and if funding constraints bind, they endure loss in collateral values, margin limits are hit, thus forcing them to liquidate positions. One trader’s hitting his limit in one security leads to falling prices and greater illiquidity in other securities and makes other traders hit their respective limits; “liquidity spirals” or “liquidity black holes” arise. • Brunnermeier and Pedersen (2009), Gromb and Vayanos (2002), Kyle and Xiong (2001), Morris and Shin (2004), Hameed, Kang, and Viswanathan (2010), Karolyi, Lee, and van Dijk (2010) Outline • Data and sovereign debt ratings • Liquidity measure – Filtering seasonal effect of liquidity • Empirical results – Rating changes and their impact on liquidity – Asymmetric impact of rating changes on liquidity – Nonlinear impact of rating changes on liquidity – Cross-sectional differences of effects on stock liquidity – Cross-country differences of effects on stock liquidity • Conclusion Data • Sovereign rating data from S&P – S&P rating is more active and tends to precede other rating agencies (Brooks et al., 2004; Gande and Parsley, 2005) – Total 413 event days • Datastream/Worldscope for 1990-2009 from 40 countries (emerging and developed markets) – Including dead-stocks (free of survivorship bias) – Require: • only common shares listed in major exchanges • available total return index, trading volume, market value of equity, and B/M – Exclude: • if daily return or trading volume is at 1% extreme in a given country • non-trading days • observations for non-event days – Final Sample: 65,545 stock-event days Numeric conversion of ratings • Event=absolute value of changes in ratings • E.g. Brazil: BB+/Positive (June 12, 2007), BBB-/Stable (Apr 30, 2008) Table 1. Number of events by country Event+: upgrade Event-: downgrade Event Big+: upgrade by more than 2 notches Event Big-: downgrade by more than 2 notches Event clustering and regression framework • Many previous studies perform event-study analyses. – Cantor and Packer (1996), Brooks et al. (2004), Martell (2005) • However, clustering of events may contaminate event- windows. – E.g. Argentina: 2001/10/9 (CCC+/Neg), 2001/10/30 (CC/Neg), 2001/11/6 (SD/NM=Not meaningful) • Hence, following Kaminsky and Schmukler (2002) and Gande and Parsley (2005), we adopt regression framework: Change in liquidityi,t = ai + bi*Eventi,t + ei,t Liquidity measure and its seasonality • Illiquidity measure from Amihud (2002) – Popular price impact measure: • Hasbrouck (2005), Goyenko et al. (2009) – Can be computed at daily frequency. – By taking log, we use: • Illiquidity has day-of-the-week effect and seasonality with calendar month. – Chordia et al. (2005), Hameed et al. (2010) – Hence, we run the following filtering regression: By taking first-differences of residuals, Table 2. Filtering regression Table 3. Descriptive statistics Hypotheses A. If sovereign rating changes deliver new information to the market, investors will repackage their portfolio, hence stock liquidity will drop on event. B. The effect of sovereign rating changes will be asymmetric: downgrades will have a larger impact than upgrades. • Reluctant to downgrade, hence downgrading delivers more information -> larger effect on portfolio rebalancing needs • Downgrades limit access to international financial market, increasing information asymmetry • Downgrades may lead to higher level of funding constraints C. The effect of sovereign rating changes will be non-linear: large downgrades will have a much larger impact than small downgrades. D. The effect of sovereign rating changes will be non-linear: rating changes from investment grade to non-investable grade will have larger impact than rating changes not involving investment grade changes. Rating changes and stock liquidity (Table 4) Country dummy: Yes, Year dummy: Yes Event=absolute value of changes in ratings Asymmetric effect on stock liquidity (Table 4) • Event(+)=positive changes, zero otherwise • Event(-)=absolute value of downgrade, zero otherwise Country dummy: Yes, Year dummy: Yes Nonlinear effect on stock liquidity (Table 5) • Big+ (-) Event=absolute value of changes in ratings by more than 2 notches upward (downward), zero otherwise • Small+ (-) Event=absolute value of changes in ratings by less than 2 notches upward (downward), zero otherwise Country dummy: Yes, Year dummy: Yes Investable -> Non-investable or vice versa (Table 5) • Event(Inv->Non-inv)=1 if the change is from investable to non- investable rating, and zero otherwise • Event(Non-inv->Inv)=1 if the change is from non-investable to investable rating, and zero otherwise Country dummy: Yes, Year dummy: Yes Cross-sectional differences in response Firms with the following characteristics will have smaller impact on events: – Politically-connected • Politically-connected firms are more likely to be bailed out (Faccio, Masulis, and McConnell, 2006) – Large-cap; High transparency • Information effect by rating changes will be smaller – Low book-to-market, High profitability • Less likely to default, hence smaller incentive to rebalance – Not closely-held • More free-floating shares, hence easier to trade – Liquid • Less liquidity constraint Cross-sectional differences in response (Table 6) Controls: Global market returns, log(MV), log(B/M), Volatility, Country dummies, Year dummies Cross-country analyses Stocks from the following type of country will experience a smaller impact (ie., less commonality in liquidity) on events: • High transparency • Revision in expectations from new information delivered by rating changes will be smaller either due to smaller sensitivity of expectations to new information in transparent setting or due to smaller amount of new information delivered by events. • High investor protection • More incentive to trading based on firm-specific information (Jin and Myers, 2006) • Well-developed stock market, High GDP • Less liquidity constraint or less information asymmetry • Low/High (?) foreign institutional ownership • Low – Less tendency to herd • High – Less closely-held shares (more free floating shares) (Dahlquist, Pinkowitz, Stulz, and Williamson, 2003) Cross-country analyses: Variables • Investor protection – LAW=1 (civil law), =0 (common law) (La Porta et al. 1998) – ANTIDIRR = Higher numbers indicating stronger minority investor protection (La Porta et al. 1998) – EXPRISK: Higher values indicating smaller political risk (La Porta et al. 1998) • Financial market development – SCAP = Average of “stock market cap / GDP” for 1988-1999 (Stulz, 2005) – GDP per capita = in US dollar, as of 2003 (World development indicator) – Foreign institutional ownership (as % of market cap) • Transparency – ACCSTAND = Higher values indicating higher accounting standards (La Porta et al. 1998) – DISCL = Credibility of financial disclosure – EARNMGT = Lower values indicating lower level of earnings management (Leuz et al. 2003) – NANALYSTS = N of analysts covered for the largest 30 firms in a country Cross-country differences in response (I) Controls: Global market returns, log(MV), log(B/M), Volatility, Year dummies Cross-country differences in response (II) Controls: Global market returns, log(MV), log(B/M), Volatility, Year dummies Conclusion • We study an impact of sovereign rating changes on stock liquidity for 40 countries for 1990-2009. • Our findings show that sovereign rating changes have a significant and robust impact on stock liquidity. – The impact is nonlinear and asymmetric and varies across stocks and countries. – In a cross-section, firms with no political connection, smaller size, higher book-to-market ratio, closely-held ownership, less liquidity, less profitability, and less transparency tend to experience more negative liquidity effects from sovereign debt downgrades. – Stocks from countries with higher investor protection, well- developed stock markets, more transparency, higher foreign institutional ownership, and higher GDP tend to experience milder drops in stock liquidity – less commonality in liquidity.
Pages to are hidden for
"Liquidity Spillover"Please download to view full document