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Portfolio Size

Portfolio Size. Cumming and Johan (2013 Chapter 17). 1. Chapter Objectives.

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Portfolio Size

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  1. Portfolio Size Cumming and Johan (2013 Chapter 17) 1

  2. Chapter Objectives • Consider 4 main factors that influence the determinants of portfolio size in terms of the number of entrepreneurial firms in the venture capital fund portfolio, as well as portfolio size per fund manager per year of investment: • The fund’s characteristics, such as type of fund, fund size and fund management firms that operate more than one fund; • The characteristics of the entrepreneurial firms in the venture capitalist fund managers’ portfolio, including entrepreneurial firm development stage, technology and location; • The structure and characteristics of the financing transactions, including staging, syndication and forms of finance; and • Market conditions. 2

  3. Theory Data Regressions Conclusion/Summary Issues • How many entrepreneurial firms do VCs have in their portfolios? • Is efficient portfolio size affected by: • Venture capital fund characteristics? • Entrepreneurial firm characteristics? • The nature of the financing transaction? • Market Conditions? • Nonlinearities in the predicted relationships? • Are returns to scale increasing or decreasing? 3

  4. Theory Data Regressions Conclusion/Summary Basic Conjecture • Expansion of portfolio size eventually becomes unprofitable as active VC investing and managerial advice gets diluted (Kannianen and Keuschnigg, 2003,2004) • Add another entrepreneurial firm to the VC portfolio where the projected marginal benefits (PMB) outweigh the projected marginal costs (PMC) • Marginal benefits and marginal costs vary according to • Type of entrepreneurial firm • Type of venture capital firm • Nature of the deal ($ amount, forms of finance, syndication, etc.) 4

  5. Figure 17. 1. Efficient Venture Capital Portfolio Size and Comparative Statics Benefits / PMC 1 Costs per Firm in Portfolio PMC 0 Increase in staging frequency, Inc rease in fundraising, proportion of early stage improvement in market And high - tech firms; etc. conditions; etc. PMB 1 PMB 0 Number of Entrepreneurial EF EF 1 0 Firms in VC ’ s Po rtfolio 5

  6. Theory Data Regressions Conclusion/Summary Hypotheses: Venture Capital Fund Characteristics

  7. Theory Data Regressions Conclusion/Summary Hypotheses: Entrepreneurial Firm Characteristics and Market Conditions

  8. Theory Data Regressions Conclusion/Summary Hypotheses: Financing Characteristics

  9. Figure 17. 2a. Comparative Statics and Nonlinearities Benefits / PMC 1 Costs per Firm in Portfolio PMC 0 PMC 2 PMB (Concave) P MB (Linear) EF EF 3 4 EF EF EF 2 1 0 Number of Entrepreneurial Firms in VC ’ s Portfolio

  10. Figure 17. 2b. Comparative Statics and Nonlinearities Benefits / Costs per PMC ( Convex) Firm in Portfolio PMC (Linear) PMB 2 PMB 0 PMB 1 EF EF 3 4 EF EF EF 2 1 0 Number of Entrepreneurial Firms in VC ’ s Portfolio

  11. Figure 17. 3. Nonlinearities in Hypothesis Tests Number of Entrepreneurial Firms in VC ’ s Portfolio The concave shape follows from the conve x shape of the PMB curve and the convex shape of the PMC curve (see Figures 1 8 .2a and 1 8 .2, r espectively). The econometric tests consider nonlinearities (both concave and convex) in the effect of the hypothesized effects on venture capital por tfolio size. The curvature of the slope is determined by grid search methods on various Box - Cox transformations. Fundraising, Proportion of Early Stage Entrepreneurial Firms, etc.

  12. Theory Data Regressions Conclusion/Summary Canadian VC Data • 1991-2000 • 214 venture capital funds • Corporate (18) • Government (15) • LSVCC (29) • Other (Institutional) Investors (48) • Private independent Limited Partnerships (104) • Source: Macdonald & Associates, Ltd. • Exclude funds with (possibly) unobserved investments 12

  13. Theory Data Regressions Conclusion/Summary Variables • Dependent Variable: • Number of firms in VC fund portfolio • Independent Variables: • VC Fund Characteristics • Fund type, # funds in VC firm, duration, # VC Managers, fundraising (drawdowns) • Entrepreneurial Firm Characteristics • Development stages, life sciences, other high-tech • Financing Characteristics • Frequency of use of Convertible Pref Equity, Staging, Syndication, $ Invested / Firm Capital • Market Conditions • TSX Index 17

  14. Theory Data Regressions Conclusion/Summary Regressions • (1) All funds together • with dummy variables for the type of fund) • (2) Private Independent Funds Only • Different types of funds face different restrictive covenants; different objectives • (3) For Portfolio Size and Portfolio Size / Manager / Year 18

  15. Theory Data Regressions Conclusion/Summary Box-Cox Transformation • Grid search over various values of to allow for both concavity and convexity • Transformation applies to independent variables, not the dependent variable 19

  16. Theory Data Regressions Conclusion/Summary Heteroscedastic Box-Cox • Box – Cox grid-search specification as per last slide • Also consider where w=fund duration possibility of truncated observations in the data • Likelihood dominance tests  superior • High Adjusted R2 (>.88) 20

  17. Theory Data Regressions Conclusion/Summary Summary of Some Key Results (refer also to Ch. 18) • Empirical support for Kannianen and Keuschnigg (2003,2004): • expansion of portfolio size eventually becomes unprofitable as active VC investing and managerial advice gets diluted • VCs have fewer firms in their portfolios when marginal costs are greater and monitoring and value added active assistance is more intensive • Greater frequency of staging and syndication • Private independent VCs • VCs have more firms in their portfolios when marginal costs are lower and marginal benefits are greater • VC fund in VC firm with 2 or more VC funds • More VC managers • Convertible securities (for private independent funds) 25

  18. Theory Data Regressions Conclusion/Summary Summary of Key Results (Con’t) • Specialization and complementarities are important • Life Sciences, Other High-Tech, Early Stage Firms • Subsample of private independent funds only: different results • Differences in the results across different types of VC funds • Private independent funds have smaller portfolios • Value added investors • Concave function for full sample (λ =0.4) • Less concave function for subsample of private independent funds  Less pronounced diminishing returns to scale (λ =0.8) 26

  19. Theory Data Regressions Conclusion/Summary Conclusions • Importance of context: type of entrepreneurial firm, type of venture capital firm, and nature of the financial contract • Risk-taking and capital structure affect optimal portfolio size • Future research • Differences across countries? • Impact on entrepreneurial firm innovation? 27

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