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Optimal Firm Structureunder Imperfect Information        Fei Ding and Peter MacKayHong Kong University of Science and Tech...
Motivation & Ambition  ~ Notice & Explain Diversity ~Motivation: Organizational Diversity Small, Private, Entrepreneurial ...
Main AssumptionsAll agents (Investors, HQ): Maximize Expected Profit (risk-neutral) Observe Common Productivity PriorsHQ I...
Motivation & Ambition                ~ Notice & Explain Diversity ~    Entrepreneurial                 Standalone         ...
INVESTORS                                                             Uncertainty                Uncertainty        Capita...
f =0                                   INVESTORS C(f)                                                                Poste...
INVESTORS                                                                                        Posterior beliefs communi...
Imperfect Information 2 Ways    ~ Production Technology ~Uncertain Payoffs: Project Productivity is Fixed but unknown (“no...
Imperfect Information 2 Ways   ~ Information Technology ~Project Investigation (Benefit): Signals → Posteriors → Improve A...
Imperfections NOT Modeled ~ Information Asymmetry (IA) ~IA (Manager Credibility): Managers know more than Investors HQ can...
Imperfections NOT Modeled ~ Information Asymmetry (IA) ~Stein (1997): IA is insurmountable HQ winner-picks in lieu of inve...
Imperfections NOT Modeled    ~ Principal-Agency (PA) ~PA (Manager Self-Interest): Managers capture investor resources Mana...
Enumerative Solution Approach   ~ Dominant Firm Structure ~   Firm-Value       Best     MonikerEp1HQ>Ep2HQ>Ep0HQ   1HQ    ...
Potential Applications~ Organization under Limited Info ~Real-side Firms: Our Base Context  Internal-external oversight of...
Some Implications           ~ Ball of Wax ~All Decisions Modeled Interrelated Firm Structure (Number of Projects) Organiza...
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Optimal Firm Structure under Imperfect Information

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NES 20th Anniversary Conference, Dec 13-16, 2012
Optimal Firm Structure under Imperfect Information (based on the article presented by Peter MacKay at the NES 20th Anniversary Conference).
Authors: Fei Ding and Peter MacKay, Hong Kong University of Science and Technology

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Transcript of "Optimal Firm Structure under Imperfect Information"

  1. 1. Optimal Firm Structureunder Imperfect Information Fei Ding and Peter MacKayHong Kong University of Science and Technology December 14, 2012
  2. 2. Motivation & Ambition ~ Notice & Explain Diversity ~Motivation: Organizational Diversity Small, Private, Entrepreneurial Firms Mid-Cap, Public, Standalones Large, Diversified ConglomeratesAmbition: Get Diversity (space, time) Neoclassical setting, minimal assumptions Optimize internal & external firm structure NO info asymmetry or principal-agency
  3. 3. Main AssumptionsAll agents (Investors, HQ): Maximize Expected Profit (risk-neutral) Observe Common Productivity PriorsHQ Is credible (no IA) and selfless (no PA) Investigates projects (if net benefit > 0) Relays posterior beliefs to investors
  4. 4. Motivation & Ambition ~ Notice & Explain Diversity ~ Entrepreneurial Standalone Conglomerate$ $ Investigative Hierarchical Large Cap Small Cap Administrative
  5. 5. INVESTORS Uncertainty Uncertainty Capital allocation Based on Prior beliefs On productivity k1 k2 prior, p1 prior, p2y1{L1,H1} y2{L2,H2} Project 1 Project 2
  6. 6. f =0 INVESTORS C(f) Posterior beliefs communication on productivity =0 1-f f =f* Headquarters C(f*) Information flow investigation f s1 s2 prior, p1 prior, p2 y1{L1,H1} y2{L2,H2}f =1 Project 1 Project 2C(f) s {L ,H }  s = s2{L2,H2}  s2= = 1 1 f 1 1 Pr[informative signal] f Pr[s ≠ |f]
  7. 7. INVESTORS Posterior beliefs communication Capital flow on productivity 1-f l-fraction K 1-l fraction Investigate P1 Headquarters Investigate P2 k1 k2 Information flow investigation f s1 s2 prior, p1 prior, p2 1/2y1{L1,H1} y2{L2,H2} Relatedness: r = Pr{y1= y2} Project 1 Project 2s1{L1,H1}  s1= s2{L2,H2}  s2= Pr[informative signal] lf (1-l)f Pr[s ≠ |l,f]
  8. 8. Imperfect Information 2 Ways ~ Production Technology ~Uncertain Payoffs: Project Productivity is Fixed but unknown (“noisy”) → Investigation (discover posterior)Random known (“risky”)→ Risk Management (real flexibility)→ Contract |posterior - prior| Pr[Posterior = Prior] ≠ 1
  9. 9. Imperfect Information 2 Ways ~ Information Technology ~Project Investigation (Benefit): Signals → Posteriors → Improve AllocationInvestor Communication (Cost): Distill & Report Findings to Investors → Less investigation time (opportunity cost) → Increasing marginal cost (convex) Balance Marginal Cost & Benefit
  10. 10. Imperfections NOT Modeled ~ Information Asymmetry (IA) ~IA (Manager Credibility): Managers know more than Investors HQ cannot fully convey posterior beliefsIn Our Model: Investors & HQ always equally informed: Ex-ante: Common productivity priors Ex-post: Common productivity posteriors Information is costly to produce & relay
  11. 11. Imperfections NOT Modeled ~ Information Asymmetry (IA) ~Stein (1997): IA is insurmountable HQ winner-picks in lieu of investors Internal allocation better than externalIn Our Model: Investors & HQ always equally informed Internal allocation same as external (?)Extend as convex cost of capital
  12. 12. Imperfections NOT Modeled ~ Principal-Agency (PA) ~PA (Manager Self-Interest): Managers capture investor resources Manager risk aversion distorts allocationLiterature (Scharfstein & Stein, 2000): Unfettered PA distorts capital allocation Migating action by HQ in conglomeratesExtensions possible – why?
  13. 13. Enumerative Solution Approach ~ Dominant Firm Structure ~ Firm-Value Best MonikerEp1HQ>Ep2HQ>Ep0HQ 1HQ ConglomerateEp1HQ<Ep2HQ>Ep0HQ 2HQ StandaloneEp1HQ<Ep2HQ<Ep0HQ 0HQ Entrepreneurial
  14. 14. Potential Applications~ Organization under Limited Info ~Real-side Firms: Our Base Context Internal-external oversight of projects Internal-external allocation of capitalVenture Capital PortfoliosBank Loan PortfoliosFund Management CompaniesServices / HR / Team Management
  15. 15. Some Implications ~ Ball of Wax ~All Decisions Modeled Interrelated Firm Structure (Number of Projects) Organization (Vertical & Horizontal) Capital Raised, Internal-external AllocationKey Drivers Productivity (Symmetry, Changes: M&A ) Relatedness (Corporate Diversification) Information Tech (Corporate Governance)
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