1. An empirical enquiry into the speed of information aggregation: The case of IPOs (Joint work with Jay Dahya, Baruch College, CUNY)

2. Research question How long does it take until asymmetric information is incorporated in the price? (how many hours, days, weeks?) Or, how long does it take until all profit opportunities for informed investors disappear? What drives this ‘speed of info-aggregation’?

3. Theory Kyle (1985), one insider  speed is exogenously determined –More insiders with same info (a.o. Holden and Subrahmaniam, 1992)  speed increases in the number of insiders –More insiders with different info (Vives, 1995)  speed decreases with number of insiders. Glosten and Milgrom (1985)  with twice as many insiders, speed quadrupled (problem: what’s ‘time’?)

4. Empirical Work Laboratory experiments –Copeland and Friedman (1987, 1991) speed (and volume!) higher when info is revealed simultanously (instead of sequentially) –Camerer and Weigelt (1991), Schnitzlein (1996) look at market mechanism Studies on real market data – ?

5. A measure of information aggregation In a normal (non-event) market setting, both information v, and pricing error  caused by information asymmetries are constantly renewed… ….so that in a ‘steady state’ trading process, the return volatility is constant. Even if there’s GARCH, we should find, in non-calendar event-time, a constant cross-sectional variance. In the ‘one-shot’ micro-microstructure models, the standard measure of information aggregation is the variance of the pricing error.

6. Information events, such as equity-offerings, will result in a shock in v,   ( v ) and   (  ). Immediately following the event,   ( v ) should fall back to its stationary level, while   (  ), the parameter that has our interest, may not.. Since cov( v,  ) = 0, we have that   ( p ) =   ( v )+   (  )..so that we can study the volatility process   ( p )( t ) to study how long it takes before event-related information is aggregated in the stockprice.

7. The Data - 2,531 U.S. IPOs from 1993-2000 - Exclude financials, utilities, Unit-offerings, REITs etc. - We distinguish between dot.com’s and non- dot.coms. - And identify “stabilized” IPOs, as those firms with initial return < 2%, and had two or more of the first five trading days with closing price = offer price (Weiss, Kumar, and Seguin, 1993)

8. 2281 on NASDAQ, 191 NYSE, 48 Amex, 1 in Boston.

9. How to find  2 ( t )? We assume the following return-generating model: R it - R mt = a(t)+  it ;  it  N(0,  it ),  it =  (i)K(t) Where R mt is the return on the market portfolio. The parameters to be estimated are: T ‘abnormal returns’ a(t), N idiosyncratic standard deviations  (i) AndT (event-time dependent) ‘volatility-multipliers’ K(t) These parameters were estimated with a home- made maximum likelihood procedure.

10. The input for the estimation is a matrix X of N  T observations where N is the number of securities and T the number of daily returns. The likelihood of seeing X given the (2T+N) parameter vector  ≡ ( a(t),  (i), K(t) ) is: I want to minimize the -log of this:

12. A look at the abnormal returns

14. Volatility as a function of event-time

17. How long does it take before the ex-ante dispersed information is aggregated in the stock price? Not long! It takes about 3-4 days A bit longer for dot.com firms Q: What drives this fast information aggregation?

19. Volume over time

22. Stabilized and Non-stabilized IPOs

23. Ellul and Pagano (337 British IPOs between 1998 and 2000)

24. The low B/A spreads can be explained by the huge volumes. Ellul and Pagano also document high turnover (first week 13%* vs. 3.5% stationary; U.S.: first day 80% vs. 3.5% stationary) Why do British marketmakers charge high spreads if volume is so high? Adverse selection! There’s a high probability of trading with an informed agent. Ellul & Pagano find high adverse selection which gradually decreases. We also did a bid/ask spread decomposition (using the methodology of Madhavan, et al. (1997)), And find that in our data there’s a low adverse selection component (which gradually increases)

27. Interpretation: In both the U.S. and the U.K. informed trading is abnormally high and decreasing. In the U.S. there is much more uninformed trading. Because in the U.S. the proportion of informed trading is lower than stationary, informed traders in the U.S. are not constraint by low liquidity. Thanks to spectacular volumes of uninformed trading on U.S. stock exchanges, information is aggregated fast. In the U.K., informed traders are constraint by low liquidity, information aggregation is slow.

28. How long does it take until stationarity sets in?

29. Initial return subsamples Thin lines gives the average  i within the sample Bold lines gives the K(t) multiplied with the ave(  i )

30. No difference except the average  i. For the sizzling hot IPOs, the time to full aggregation may be a bit longer..

31. Speed and underwriter prestige Average  i of IPOs floated by prestigious underwriters is higher Information aggregation for IPOs with prestigeous underwriters is quicker

32. To do:  How does speed of information aggregation depend on Volume, Size, %-age offered, syndicate-size Plan1: Split sample in two based on the above. Then do the MLE again to find the K(t)’s Plan 2: “MLE-regression”: Estimate the parameters in: AR i,t = R i,t - R bm,t = a(t)+  i,t,  i,t  N(0,  i,t )  i,t =  (i) ( K(t)+  (t)·V(t,i) ) or  i,t =  (i) ( K(t)+  (t)·UP(i) ) Do exactly the same for British data. Compare fixed price offerings vs. bookbuilt offerings.