1. 1 Enhancing Firm Profitability: A Dynamic Perspective Using Quantile Regression Ming-Yuan Leon Li Department of Accountancy Graduate Institute of Finance and Banking National Cheng Kung University, Taiwan June, 2007

2. 2 Outline This Paper Financial data for U.S. firms listed during 1996- 2005 Examine numerous determinants of firm profitability Quantile regression model versus OLS and LAD estimates The nonlinearities derived from conditional quantile regression size, significance and sign The results are consistent with the theory of firm lifecycle

3. 3 Motivations Limitations of OLS and LAD methods Central behaviors only Conditional mean Qunantitle Regression Whole distribution Conditional distribution

4. 4 Limitations of the Conventional OLS Method

5. 5

6. 6 Key Question The impacts of the identified determinants of profitability performance on firms are consistent with different levels of firm’s profitability quantiles?

7. 7 Motivations Firm life cycle Adizes (1988): Business strategies and organizational structures of firms vary according to the problems faced at different life cycle stages of the organization

8. 8 Prior Studies on Life-cycle Theory The concept of corporate life-cycle stage has generated considerable applied interest Dodge et al. (1994) on the relationship between operation strategies and life cycles Beldona et al. (1997) and Robinson (1998) on effects of operation strategies on performance at various firm life cycle stages Kimberley and Miles (1980) and Dodge and Robins (1992) positing that organization structure reflects current life cycle stage Adizes (1979); Miller and Friesen (1984); Alexander et al. (1993); and Maturi (1999) examining the relationship between CEO leadership styles and life cycle stages Anthony and Ramesh (1992), Black (1998) and Jorion and Talmor (2001) testing the impact of life-cycle on corporate earnings.

9. 9 Key Limitation of Prior Studies Segment sample companies into various subsets Use criteria such as earnings and/or age Apply traditional optimization techniques such as ordinary least squares (OLS) and least absolute deviation (LAD) to fit their subsets

10. 10 Key Limitation of Prior Studies The analytical framework in these studies was based on unconditional distribution of firm samples This form of “truncation of samples” may yield invalid results As demonstrated by Heckman (1979), such methods often exhibit sample selection bias

11. 11 Key Advantage of Quantitle Regression A valid alternative is the quantile regression framework, which segments the sample into subsets defined by conditioning covariates Moreover, in comparison with the least square method, quantile regression offers a relatively rich description of the conditional mean for extreme cases in the samples

12. 12 Key Advantage of Quantitle Regression Firm lifecycle theory revealed : The behavior of firms with higher profitability significantly differs from firms with lower profitability Firms at the growth (decline) stage tend to exhibit higher (lower) profitabililty. Further, corporate lifecycle theory would indicate that profitable firms differ from less profitable firms in their strategies for enhancing profitability

13. 13 Empirical Methods No quantile models: OLS and LAD

14. 14 Empirical Methods Quantile regression model

15. 15 Empirical Methods Quantile regression model

16. 16 Data Sample S&P500 firms over the 10-year period from 1996 to 2005 were analyzed Financial firms were excluded from the sample because the nature of their liabilities and capital structure intrinsically differ from those of nonfinancial firms Firms were also excluded from the analysis if the specified financial data were not available for the entire 10-year period The final sample included 2,078 firm observations All data were obtained from the Compustat database.

17. 17 Data Return on equity (ROE) was selected as the proxy variable for firm profitability Five determinants of profitability were recorded: R&D expense Company size Debt ratio Total asset turnover Current ratio

18. 18 Empirical Results Researches on R&D investment Grabowski and Mueller (1978); Reinhard (1985); Guerard, et al. (1987); Brown (1988); Kraft (1989): Chan, et al. (1990); Morbey and Reithner (1990); Sougiannis (1994); Deng, et al. (1999) and Schoenecker and Swanson (2002) One-period lag per R&D expense was employed as the proxy variable for R&D expense to examine its effect on profitability

19. 19 Empirical Results The effect of size on the profitability of a firm is controversial Ferri and Jones (1979) and Smith and Watts (1992), Panzar and Willig (1979), Eckard (1990) and Paul (2001) Williamson (1967), Holmes, et al. (1991); Lever (1996); Chuang (1999); Pull (2003)

20. 20 Empirical Results The effect of capital structure on profitability presents a puzzle Kim (1978), Schneller (1980) and Bradley, et al. (1984) Titman and Wessels (1988) and Baskin (1989)

21. 21 Empirical Results Logistics management is an important area in the rapidly growing information technological (IT) industry A good logistics system can improve firm profitability Lee and Billington (1992) and Beamon (1999) Two proxy variables: (1) total asset turnover rate and (2) current ratio

22. 22 Empirical Results

23. 23 Empirical Results

24. 24 Empirical Results Table 6 Estimation Results for the Quantile Regressions Variables θ=0.05θ=0.10θ=0.25θ=0.50θ=0.75θ=0.90θ=0.95 RD-0.0043853*** (0.0002746) -0.0025503*** (0.0001405) -0.0009476*** (0.0001108) 0.000063 (0.0000801) 0.0008034*** (0.0000901) 0.0016108*** (0.0001399) 0.0021056*** (0.000176) SIZE0.0188183* (0.0111912) 0.0089778** (0.0047091) -0.0033467 (0.0033348) -0.0022368 (0.0024291) -0.0038403 (0.0036918) -0.0132911** (0.0067661) -0.0280788*** (0.011351) DR-0.3723856*** (0.1022445) -0.2242124*** (0.039101) -0.0178845 (0.0255833) 0.1174058*** (0.0180686) 0.3248761*** (0.0284634) 0.7018887*** (0.0649025) 1.112786*** (0.122592) TAT0.0364582 (0.0235623) 0.025958*** (0.0100341) 0.0193181*** (0.0068847) 0.0226042*** (0.0040579) 0.0313039*** (0.0052302) 0.0591628*** (0.0086657) 0.1244339*** (0.014783) CR-0.0113385 (0.0083466) -0.0062991* (0.0034024) -0.0006634 (0.002672) 0.0018459 (0.0021069) 0.0078969*** (0.0026265) 0.0269411*** (0.0041234) 0.0423439*** (0.005585) Pseudo R 2 0.14670.09560.04260.01640.02320.06780.1033

25. 25 Empirical Results

26. 26 Empirical Results

27. 27 Empirical Results

28. 28 Conclusions This study reveals that traditional OLS and LAD optimization techniques capture central behaviors only and misestimate the effect of determinants of firm profitability, including size, significance and even sign, particularly in firms with extremely high/low profitability