Optimal Firm Structure under Imperfect Information
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Optimal Firm Structure under Imperfect Information

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NES 20th Anniversary Conference, Dec 13-16, 2012 ...

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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    Optimal Firm Structure under Imperfect Information Optimal Firm Structure under Imperfect Information Presentation Transcript

    • Optimal Firm Structureunder Imperfect Information Fei Ding and Peter MacKayHong Kong University of Science and Technology December 14, 2012
    • 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
    • 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
    • Motivation & Ambition ~ Notice & Explain Diversity ~ Entrepreneurial Standalone Conglomerate$ $ Investigative Hierarchical Large Cap Small Cap Administrative
    • 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
    • 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]
    • 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]
    • 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
    • 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
    • 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
    • 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
    • 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?
    • Enumerative Solution Approach ~ Dominant Firm Structure ~ Firm-Value Best MonikerEp1HQ>Ep2HQ>Ep0HQ 1HQ ConglomerateEp1HQ<Ep2HQ>Ep0HQ 2HQ StandaloneEp1HQ<Ep2HQ<Ep0HQ 0HQ Entrepreneurial
    • 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
    • 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)