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This paper explores how analysts assess signal quality amidst varying informational environments. It examines the interplay between analysts and market conditions, focusing on noisy rational expectations in modeling signal generation. By utilizing a semi-game-theoretic approach, the study endogenizes the cost and quality of private signals, derived from empirical models to yield testable implications. The findings suggest that analysts tend to underperform when high-quality information prevails, and opportunities for further research are identified. This work contributes to understanding analyst behavior and forecast reliability.

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  1. Paper available at druh•com (together with an extended version of this presentation)

  2. Robert Czernkowski, UNSW Generation of Private Signals by Analysts EAA2006, Dublin, Eire

  3. What ? The Issue How do analysts decide on signal quality ? • i.e. how do they respond to aspects of the informational environment / upcoming public signal / quality of Π ?

  4. Why ? Background • Empirical regularities regarding analyst behaviour have been documented • Much of this is atheoretical; in particular: there is no modelling of the equilibrium • e.g. what came first: • the analyst • or an informationally rich market ?

  5. How ? Approach • Noisy Rational Expectations and cognate signalling literature models simple markets where a signal is generated in a semi-game-theoretic way, i.e. actors have rational, linear expectations • Given an objective function, expected characteristics of the signal can be modelled

  6. How ?Approach Demski and Feltham (1994) • two exogenous signals • … allow for the purchase of a private signal (i.e. costly acquisition of private information) • … have derived some testable implications • Analysts can be included as producers of the private signal

  7. How ?Method I extend D&F (1994) by endogenising: • cost • quality of the private signal  use a simple objective function  derive implications for forecast quality I derive additional comparative statics (ERC, Volume, Price informativeness) first some terms…

  8. How ?Terms final realisation (wealth, future price): x~N(0,σx2) public signal (earnings): y2=x +ε2, ε2~N(0,σ22) private signal (forecast): y1=y2+ε1, ε1~N(0,σ12) quality of forecast: 1/σ12 price of forecast: c proportion of investors buying y1 (forecast): λ Investors have a choice: buy or free-ride

  9. How ? Objective Function • Noisy rational expectations modelling • Simple objective function for analysts Π: • Solve for [σ12, λ]: Algebra messy  Simulation Hypothes(e)s Empirics

  10. Preview of findings Prediction (figure 2 coming up):Analysts under-exert themselves when the information environment is of high quality Finding(s):The prediction explains analyst signal quality(although driven by size quintiles 2 & 3)

  11. How ? Method, Caveats • My focus is on supply ("sell-side") analysts • Motivations are more complex than buy-side analysts’ ? • As information intermediaries, how do they interplay with other sources of information ? • I assume analyst is a monopolist

  12. High quality forecast High quality information environment link to slide 30 Solving for Equilibrium (3) – Basic Result (Figure 2)

  13. Intuition At high earnings quality, if the analyst’s signal is very good, info will leak through trades of purchasers benefits of free-riding are substantial analyst makes lower $$$ link to slide 30

  14. Empirics – Data • Earnings forecasts from I/B/E/S International Inc. • Income statement data and release dates from Standard and Poor's Compustat service • Price and volume data from Center for Research in Security Prices (CRSP)

  15. Empirics – Measures σ12 measured by deviation of forecast from earnings ultimately announced  FNOISE = [ (y0acteps-mean) / y0acteps ]2 σ22 ENOISE 1/R2 from Foster (1977) model of earnings (+other measures) Logic ?

  16. Empirics - Approach • just use extreme quintiles on ENOISE

  17. Results

  18. Table 4, Panel A link to Figure 2

  19. Robustness • removing insignificant controls makes no difference (Table 4, Panel B) • adding analyst following makes no difference (Table 5) • regression by size quintiles – all the action is happening in quintiles 2 and 3 (Table 6)

  20. Further research • What is the analyst following in the various quintiles?

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