Designated Market Makers for Small-Cap Stocks Is One Enough?

Transcription

1 Designated Market Makers for Small-Cap Stocks Is One Enough? Albert J. Menkveld Vrije Universiteit Amsterdam Presentation AMF-SEC Colloque Paris May

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3 Background and Motivation Firms care about liquidity. Small-cap firms in particular, due to - high bid-ask spreads e.g. Amihud and Mendelson (1986, JFE) show that bid-ask differentials across stocks could account for 50% value differentials - Acharya and Pedersen (2005, JF) find that low liquidity coincides with high (priced) liquidity risk - Pastor and Stambaugh (2003, JPE) study size directly, show that liquidity risk is highest for small-caps and find an associated additional required return of 3.7% annually 3

4 Background and Motivation Firms care about liquidity. Small-cap firms in particular, due to - high bid-ask spreads e.g. Amihud and Mendelson (1986, JFE) show that bid-ask differentials across stocks could account for 50% value differentials - Acharya and Pedersen (2005, JF) find that low liquidity coincides with high (priced) liquidity risk - Pastor and Stambaugh (2003, JPE) study size directly, show that liquidity risk is highest for small-caps and find an associated additional required return of 3.7% annually Exchanges respond by (re-)introducing designated market makers (DMMs) Contemporary studies document liquidity improvement and 5% abnormal return (see Nimalendran and Petrella (2003), Venkataraman and Waisburd (2006), Anand, Tanggaard, and Weaver (2005)) 3-a

5 Our Contribution Thus far, markets typically feature a single DMM. Microstructure theory suggests two arguments in favor of multiple DMMs - competition (see e.g. Glosten (1989), Bernhardt and Hughson (1997), Biais, Martimort, and Rochet (2000), and Biais, Glosten, and Spatt (2005) for survey second-generation microstructure models ) - classic inventory-sharing (see Stoll (1978) and Ho and Stoll (1981, 1983), etc.) 4

6 Our Contribution Thus far, markets typically feature a single DMM. Microstructure theory suggests two arguments in favor of multiple DMMs - competition (see e.g. Glosten (1989), Bernhardt and Hughson (1997), Biais, Martimort, and Rochet (2000), and Biais, Glosten, and Spatt (2005) for survey second-generation microstructure models ) - classic inventory-sharing (see Stoll (1978) and Ho and Stoll (1981, 1983), etc.) We exploit the Euronext introduction of DMMs in the Dutch market to 1. study empirically whether the #DMMs that a firm hires matters for liquidity supply 2. argue exogeneity of this #DMMs and exploit it as an instrumental variable to identify causality from liquidity supply to liquidity demand (volume) and volatility 4-a

7 Institutional Background On October 29, 2001, Euronext introduced DMMs in the Dutch equity market. A DMM - commits to maximum spread of 4% - commits to minimum depth ofe10, does not pay trading fees - potentially receives pecuniary compensation from issuer - might be indirectly compensated through additional business e.g. seasoned offerings, banking services, etc. DMMs supply liquidity in a pure limit order market. They do not enjoy ex-post price improvement privileges (as e.g. the NYSE specialist) 5

8 Simple Model A maximum spread of 4% and a minimum depth ofe10,000 seem to be non-binding, but for these small-cap stocks, they can be binding. As liquidity provider you lose money for sure when the market is very volatile. Willem Meijer, SNS Securities in Financieel Dagblad 6

9 Simple Model A maximum spread of 4% and a minimum depth ofe10,000 seem to be non-binding, but for these small-cap stocks, they can be binding. As liquidity provider you lose money for sure when the market is very volatile. Willem Meijer, SNS Securities in Financieel Dagblad We extend a standard Ho and Stoll inventory model to capture two salient features of our institutional setting 1. two volatility regimes: in the high regime liquidity constraint is binding 2. DMMs lose money on liquidity supply but enjoy a private indirect compensation 6-a

10 Simple Model Consider the market for a risky asset with end-of-period liquidation value v NID(0, σ i ). We propose a five-stage game. WLOG, we consider the sell-side of liquidity supply: 1. N potential DMMs consider entry. If they enter, they enjoy private benefit C i and commit to ask price cap A 2. the volatility regime is learned: σ h with probability p h, normal volatility otherwise 3. liquidity is supplied by those who entered 4. a liquidity trader sends market buy of (deterministic) size Q 5. payoffs are realized 7

11 We find Simple Model 1. multiple Nash equilibria, unique sequential one, assume n DMMs enter 8

12 We find Simple Model 1. multiple Nash equilibria, unique sequential one, assume n DMMs enter 2. that a higher #DMMs leads to a lower expected ask: E[A ] = (1 p h ) φ n + p h A with φ Qσ 2 γ 8-a

13 We find Simple Model 1. multiple Nash equilibria, unique sequential one, assume n DMMs enter 2. that a higher #DMMs leads to a lower expected ask: E[A ] = (1 p h ) φ n + p h A with φ Qσ 2 γ 3. that a higher #DMMs reduces transitory volatility: E[A ] = E[A ] + I φ n Q 8-b

14 We find Simple Model 1. multiple Nash equilibria, unique sequential one, assume n DMMs enter 2. that a higher #DMMs leads to a lower expected ask: E[A ] = (1 p h ) φ n + p h A with φ Qσ 2 γ 3. that a higher #DMMs reduces transitory volatility: E[A ] = E[A ] + I φ n Q Relevance of the model for our setting - the competitive equilibrium is most likely implemented in limit order market (see Biais, Foucault, and Salanié (1998)) 8-c

15 We find Simple Model 1. multiple Nash equilibria, unique sequential one, assume n DMMs enter 2. that a higher #DMMs leads to a lower expected ask: E[A ] = (1 p h ) φ n + p h A with φ Qσ 2 γ 3. that a higher #DMMs reduces transitory volatility: E[A ] = E[A ] + I φ n Q Relevance of the model for our setting - the competitive equilibrium is most likely implemented in limit order market (see Biais, Foucault, and Salanié (1998)) - a private indirect compensation drives the entry as evident from (i) press coverage, (ii) interviews with DMM firms, and (iii) a Granger causality analysis using volume and volatility 8-d

16 Data and Methodology DMMs are introduced for 74 small-caps. Based on Euronext data, we define for each of the first 20 months - average #DMMs - average of time-weighted (intraday) quoted spread - volume - volatility of daily midquote returns - average market cap (fixed across time) 9

17 Data and Methodology DMMs are introduced for 74 small-caps. Based on Euronext data, we define for each of the first 20 months - average #DMMs - average of time-weighted (intraday) quoted spread - volume - volatility of daily midquote returns - average market cap (fixed across time) We use standard panel data econometrics (and robust standard errors) - simple OLS, OLS between, OLS within - use #DMMs as instrumental variable in Hausman/Taylor and Arrelano/Bond/Bover GMM approach 9-a

18 Hausman-Taylor and ABB GMM The general model takes the form: y it = x 1,itβ 1 + x 2,iβ 2 + λ t + η i + v it We follow two strategies to maximize the power of the instrumental variable. 10

19 Hausman-Taylor and ABB GMM The general model takes the form: y it = x 1,itβ 1 + x 2,iβ 2 + λ t + η i + v it We follow two strategies to maximize the power of the instrumental variable. 1. Hausman and Taylor (1981) propose strategy that allows each time-varying instrumental variable to instrument for a time-varying as well as a time-invariant endogenous variable through decomposition into two orthogonal components: z i = t=1 z i,t z i,t = z i,t z i In its most elementary form, the approach assumes (i) no time effect, (ii) v it to be i.i.d. and (iii) η i to be i.i.d. and (iv) both are mutually independent and independent of any of the explanatory variables. 10-a

20 Hausman-Taylor and ABB GMM (ctd) 2. Arellano and Bond (1991) and Arellano and Bover (1995) propose two-stage GMM that generalizes Hausman-Taylor. For group i, we define: y i = W i δ + ιη i + v i where W contains x 1,it, x 2,i, and a time-dummy, δ is the associated parameter vector, ι is a Tx1 vector of ones, and v i is the vector with error terms. Let Z i contain the instrumental variables i.e. Z i [ιz i z i ] The GMM estimator now takes the form: ( ˆδ = ( W iz i )A N ( ) 1( Z iw i ) W iz i )A N ( i i i i ( 1 ) A N = Z N ih i Z i i 11 Z iy i )

21 Summary Statistics Mean St. Dev. St. Dev. Between a St. Dev. Within b #DMM Market Capitalization (ebln) #Trades per Day Daily Volume (emln) Quoted Spread (%) Volatility Daily Midquote Returns (σ) Autocorrelation Daily Midquote Returns Price (e) #Observations (N*T) 74*20 a : Based on the time means i.e. x i = 1 T T t=1 x i,t. b : Based on the deviations from time means i.e. x i,t = x i,t x i. 12

22 Does #DMMs Impact Bid-Ask Spread? Without Instrumental Variables (A) OLS (B) OLS between (C) OLS within (D) HT/ GLS With IVs (E) ABB/ GMM #DMM -0.65** -0.70** -0.51* -0.50** -0.56** (-3.41) (-3.57) (-1.64) (-2.08) (-2.37) (#DMM) ** 0.05** 0.06* 0.06* 0.05** (2.46) (2.54) (1.73) (1.64) (2.19) Market Cap -0.94** -0.92** * (-3.64) (-3.69) (-0.52) (-1.88) N*T 74*20 74*1 74*20 74*20 74*20 R a : Based on the time means i.e. x i = 1 T T t=1 x i,t. */**: Significant at a 90/95% level. 13

23 Does #DMMs Impact Bid-Ask Spread? 2.00 OLS ABB GMM

24 Does the Bid-Ask Spread impact Volume? Without Instrumental Variables (A) OLS (B) OLS between (C) OLS within (D) HT/ GLS With IVs (E) ABB/ GMM Quoted Spread -0.13** -0.16** -0.08* (-2.46) (-2.40) (-1.66) (-0.90) (-0.55) Market Cap 1.43** 1.39** (7.19) (7.14) (0.86) (0.60) Intercept 0.21** 0.27** (2.04) (2.14) (0.06) (0.10) N*T 74*20 74*1 74*20 74*20 74*20 R a : Based on the time means i.e. x i = 1 T T t=1 x i,t. */**: Significant at a 90/95% level. 15

25 Does the Bid-Ask Spread impact Volatility? Without Instrumental Variables (A) OLS (B) OLS between (C) OLS within (D) HT/ GLS With IVs (E) ABB/ GMM Quoted Spread 0.72** 0.68** 0.78** 0.89* 0.73** (12.65) (6.27) (10.54) (1.63) (2.83) Market Cap (0.65) (0.45) (0.59) (1.40) Intercept 1.28** 1.35** (8.20) (5.94) (-0.19) (-0.05) N*T 74*20 74*1 74*20 74*20 74*20 R a : Based on the time means i.e. x i = 1 T T t=1 x i,t. */**: Significant at a 90/95% level. 16

26 Does the B-A Spread impact Autocorrelation? We regress first-order autocorrelation in midquote returns on the bid-ask spread to identify whether low spreads reduce transitory volatility: Without Instrumental Variables (A) OLS (B) OLS between (C) OLS within With IVs (E) ABB/ GMM Quoted Spread -0.02** -0.04** ** (-3.37) (-3.07) (0.66) (-3.31) Intercept (-0.65) (0.62) (0.77) N*T 74*20 74*1 74*20 74*20 a : Based on the time means i.e. x i = 1 T T t=1 x i,t. */**: Significant at a 90/95% level. 17

27 Conclusions As of 10/29/01, Euronext allows Dutch small-caps to hire DMMs to guarantee a minimum liquidity supply. Interestingly, the #DMMs that a firm hires seems to be dominated by factors other than trading conditions, e.g. seasoned equity offerings, banking services, etc. We study a panel of 74 firms that hire up to 8 DMMs in the first 20 months after the introduction. We find: 18

28 Conclusions As of 10/29/01, Euronext allows Dutch small-caps to hire DMMs to guarantee a minimum liquidity supply. Interestingly, the #DMMs that a firm hires seems to be dominated by factors other than trading conditions, e.g. seasoned equity offerings, banking services, etc. We study a panel of 74 firms that hire up to 8 DMMs in the first 20 months after the introduction. We find: 1. The quoted spread decreases in the #DMMs with diminishing marginal effect (2% for 1 DMM to 1% for 8 DMMs). This is consistent with (i) competition among DMMs and (ii) risk-sharing. 2. We use the exogenous #DMMs as IV and find that a lower spread does not create additional volume, but does reduce (transitory) volatility. 18-a

29 Designated Market Makers for Small-Cap Stocks Is One Enough? Albert J. Menkveld Vrije Universiteit Amsterdam 19

30 References Acharya, V.V., and L.H. Pedersen Asset Pricing with Liquidity Risk. Journal of Financial Economics 77(2): Amihud, Y., and H. Mendelson Asset Pricing and the Bid-Ask Spread. Journal of Financial Economics 17: Anand, A., C. Tanggaard, and D.G. Weaver Paying for Market Quality. Technical Report, Rutgers University. Arellano, M., and S.R. Bond Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations. Review of Economic Studies 58: Arellano, M., and O. Bover Another Look at the Instrumental Variables Estimation of Error- Components Models. Journal of Econometrics 68: Bernhardt, D., and E. Hughson Splitting Orders. Review of Financial Studies 10(1): Biais, B., T. Foucault, and F. Salanié Floors, Dealer Markets, and Limit Order Markets. Journal of Financial Markets 1: Biais, B., L. Glosten, and C. Spatt A Survey of Microfoundations, Empirical Results, and Policy Implications. Journal of Financial Markets 8: Biais, B., D. Martimort, and J.C. Rochet Competing Mechanisms in a Common Value Environment. Econometrica 68(4): Glosten, L.R Insider Trading, Liquidity, and the Role of the Monopolist Specialist. Journal of Business, no. 62: Hausman, J.A., and W.E. Taylor Panel Data and Observable Individual Effects. Econometrica 49(6): Ho, T., and H. Stoll Optimal Dealer Pricing under Transaction Cost and Return Uncertainty. Journal of Financial Economics 9: The Dynamics of Dealer Markets Under Competition. Journal of Finance 38: Nimalendran, M., and G. Petrella Do Thinly-Traded Stocks Benefit from Specialist Intervention? Journal of Banking and Finance 27:

31 Pastor, L., and R.F. Stambaugh Liquidity Risk and Expected Returns. Journal of Political Economy 111(3): Stoll, H.R The Supply of Dealer Services in Securities Markets. Journal of Finance 33(4): Venkataraman, K., and A.C. Waisburd The Value of a Designated Market Maker. Journal Financial and Quantitative Analysis (forthcoming). 19-2