Jose Gonzalo Rangel UCSD Capri Workshop May 25, 2006

1. Macroeconomic Announcements, Price Discovery, and Order Flow Effects in the Stock Market: Evidence from Incomplete Data and Multiple Financial Markets Jose Gonzalo Rangel UCSD Capri Workshop May 25, 2006

2. Motivation Asset prices are affected by revisions in expectations driven by news about changing economic conditions (e.g. output, employment and inflation shocks). The ultimate objectives of monetary policy are expressed in terms of same macroeconomic variables (Bernanke and Kuttner, 2005). The stock market response to macroeconomic news is linked to market assessments (investor’s beliefs) of future states of the economy and/or Fed actions. However, the mechanism through which these beliefs enter equity prices remains an intriguing empirical question.

3. Main Approach • Announcements are pure symmetric information events. Beliefs are homogeneous. The transmission mechanism involves a nearly instantaneous price adjustment (jumps, little trading activity involved) Andersen et al.(2003, 2005), Boyd, Hu, and Jagannathan(2005), Bernanke and Kuttner(2005) Problem: under “asymmetric information” the market needs to aggregate heterogeneous beliefs. Transmission mechanism involves a learning process. Learning occurs through trades. Fundamental price is affected by the order flow (sum of signed trades). Important effects on price dynamic behavior (price discovery), liquidity, and volatility. Evans and Lyons (2004), Brandt and Kavajecz (2004)

4. Price Effects of Trading Activity on Announcement Days Data Evidence: Amihud (2002) illiquidity ratio

5. Public info Price Order Flow’s Role Graphically Symmetric InformationApproach Microstructure Approach Private info Order flow Price Hybrid Information Order flow Price

6. Main Empirical Results • Significant instantaneous news impacts of news related to real activity, investment, inflation, and monetary policy. • Significant order flow and/or asymmetric information effects on employment days due to: • Uncertainty on the implications of employment news for stock prices • Increases in the volatility of fundamental prices • Asymmetric Information effects come from the interest rate component of equity prices. • Evidence of excess sensitivity of long term interest rates to employment shocks • Private agents revise expectations about future Fed policies and/or long run states of the economy (Gurkaynak, Sack, and Swanson, 2005) • Revisions are not homogeneous

7. The Microstructure View Observed transaction price: Random walk representation of the unobserved fundamental (log) price mt qt=trade direction (1 if buy, -1 if sell) • Under symmetric information: where ut accounts for arrival of public information over (t-1,t]

8. The Microstructure View • Under asymmetric information (Hasbrouck, 2004, 2005): where 1(or -1) if transaction k was initiated by the buyer (or seller). Nt = number of trades over (t-1,t]. Similar results if Qt is proxied by signed volume, Vtqt, where Vt=f(volume), and qt represents sign(Qt).

9. A Simple Asymmetric Information Model (SAIM) Theoretical basis: Kyle (1985), and Glosten and Milgrom(1985) Suppose Kyle’s framework in a one period model:

10. Results and Comparative Statics of (SAIM) • Proposition A1: There is a unique linear equilibrium in which Comparative statics • λ↑ when fundamental price volatility ↑ (σ2 ↑) • λ↑ when volatility of liquidity demands ↓ (σu2↓) • λ↑ when precision of the signal ↓ (σε2 ↑, for M sufficiently large) • λ↓ when number of informed traders ↑ (M↑, for M sufficiently large)

11. Empirical Specification (SAIM) • Estimation of a structural microstructure model based on: • Hasbrouck (2004) extension of Glosten and Harris (1988), including price impacts of trades on fundamental prices. • Incomplete data, daily frequency • Additional extensions: • News effects on the efficient price • Average incremental effects of order flow on announcement regimes vs the non-announcement regime.

12. Empirical Specification (SAIM) Observed and efficient prices: where • Ik,t= type k announcement Indicator • Vt= f(trading volume) = (1, Vt)’ • qt= trade direction: Yk,t=Realization of type k macro variable (Yhat=forecast)

13. Empirical Specification (SAIM) • Observed Price: • Returns: • Conditional variance of returns:

14. Estimation Issues and Econometric Approach (SAIM) • Remarks: • Model keeps the same form under time aggregation • Parameters of interest: λ, , and βs • Observed variables: prices, volume, news variables • Unobserved (latent) variables: qtand mt • 2T possible paths of qt (#Qt=2T, T=Sample size, e.g. 3,120 days) Likelihood:

15. Estimation Issues and Econometric Approach (SAIM) How to estimate? • Simulated Maximum Likelihood (SMLE) • Bayesian Markov Chain Monte Carlo (MCMC) • Hasbrouck (2004, 2005) MCMC advantages • Computationally convenient • Parameter uncertainty • Uncertainty about news effects • Uncertainty about asymmetric information

16. The MCMC Algorithm Desired posterior: Given the set of parameters and q(0): Step1: Draw Θ(1)from Step2: Draw q(1)from Step3: Continue in this fashion until generate a sequence whose limit is the desired posterior F

17. Data • S&P500 (daily) data on closing prices and volume from CRSP. Sample period: 1992-2003. • Futures (daily) data on closing prices and volume for S&P500, bonds (5Y, 10Y) and exchange rates (US/YEN) from Datastream • 19 Macroeconomic announcements and forecasts from MMS regarding: • Real Activity: IP, RS, NFP, UMP, CU, PINC, and CCR • Consumption: NHS, PCE • Investment: DGO, CS, and BI • Trade: GSTB • Price Level: CPI, PPI • Forward Looking: LI, NAPM, and HS • Monetary Policy: FOMC/FFR

18. Hist

20. Slide lambda

21. Comparative statics • λ↑ when fundamental price volatility ↑ • λ↑ when volatility of liquidity demands ↓ (σu2↓ ) • λ↑ precision of the signal ↓ (σε2 ↑, for M sufficiently large) • λ↓ when number of informed traders ↑ (M↑, for M sufficiently large) Back

22. Robustness: Evidence from Bond Markets

23. Evidence from Exchange Rates

24. Summary of Empirical Results • For the stock market • Instantaneous fundamental news impacts (asymmetric) • Order flow effects on employment days • For long term bond markets • Fundamental news effects as predicted by the asset pricing view • Strong order flow effects on employment days • For exchange rates • Just fundamental news effects on employment days

25. Results consistent with recent literature Pasquariello and Vega (2005): Day-to-day bond yield changes and order flow are most sensitive to Nonfarm Payroll Employment announcements (based on intradaily data) . Morris and Chin (2002): Overreaction to employment news. Bond yields are most reactive to the types of news emphasized by the press. Does employment convey more information about future growth? No evidence Does employment convey more information about future inflation? More likely

26. Contribution: Why is this distinction interesting? Relevant for practitioners and policy makers. • Provides new methods for measuring impacts of output and inflation shocks in financial markets. • Provides new measures on how homogeneous is the market evaluation of future Fed reactions to these shocks. Provides an explanation for observed patterns in different price “characteristics”, such as volatility and liquidity. Contributes to a better understanding of link between macroeconomic information and the price discovery process (one of the main functions of financial markets). “Assets trade in markets, markets provide liquidity and price discovery, and asset prices are influenced by the transaction costs of liquidity and the risk of price discovery” (O’Hara, 2003)

27. Concluding Remarks • Evidence of incremental asymmetric information costs on employment days. • Changes in the asymmetric information coefficient on employment days due to: • Uncertainty on the implications of employment news for asset prices • Increases in volatility of fundamental prices. • Bond markets point to asymmetric information on the interest rate component of stock prices. • Consistent with the excess sensitivity of long-term interest rates Not only investors change their long run expectations of the state of the economy and long-run Fed policies, but also they have heterogeneous beliefs.

28. Future Research • Analysis with “complete” information • More flexible specification for conditional volatility • Time varying news effects • Time varying order flow effects • Explore correlations in the trade direction (or order flow) • Analysis of individual stocks • Include earnings announcements

30. Conditional Posterior for the latent trade direction: Where, Back

31. Observed Price: • Returns:

32. Posterior Distributions for News Effects and Asymmetric Information Parameters

33. Asymmetric Information Effect S&P500 Cumulative Impact (Basis Points)

34. Asymmetric Information Effect (Futures S&P500) Cumulative Impact (Basis Points)

35. Assumptions • Post announcement “true” value, • M informed traders get noisy signals about the “true” price impact of a particular news event, • Informed agent i demands xi units of the asset • Noise traders demand • A market maker sets prices after observing aggregated order flow. Fundamental post announcement price satisfies market efficiency