Abstract
Using quantile regression, our results provide explanations for the inconsistent findings that use conventional OLS regression in the extant literature. While the direct effects of R&D investments, leverage, and main bank relationship on Tobin’s Q are insignificant in OLS regression, these effects do show significance in quantile regression. We find that firms’ advantages with high R&D investment over low R&D monotonically increase with firm value, appearing only for high Q firms; while firms’ advantages with low R&D over high R&D monotonically increase with firm value for low Q firms. Tobin’s Q is monotonically increasing with leverage for low Q firms; whereas it is decreasing in high Q firms. Main banks add value for low to median Q firms, while value is destroyed for high Q firms. Meanwhile, we find the interacted effect of main bank and R&D investment which increases with firm value, only appears in medium quantiles, instead of low or high quantiles. Results of this work provide relevant implications for policy makers. Finally, we document that industry quantile effect is larger than the industry effect itself, given that most of the firms in higher quantiles gain from industry effects while lower quantile firms suffer negative effects. We also find the results of OLS are seriously influenced by outliers. In stark contrast, quantile regression results are impervious to either inclusion or exclusion outliers.
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Notes
- 1.
The theoretical Tobin’s Q is the ratio of the market value to the replacement cost of the assets. Because some data is not available, it has been often estimated by a simple measure of Q (the “pseudo Q”): market value to book value of the total assets.
- 2.
For example, some firms are characterized by negative debt or negative sales without proper explanations in footnotes.
- 3.
The financial firms were excluded from the overall sample as the financial firms exhibit different balance sheet items from those of the non-financial firms.
- 4.
Our extreme value definition is different from Ho et al. (2006) who deleted the extreme value below the first quantile less 1.5 times the interquantile range or any value greater than the third quantile plus 1.5 times the interquantile range. The reason why we need to delete the outliers is that we find some firms with Tobin’s Q extremely high, which is beyond our understanding. We think it may be a result of the bubble Internet stock collapse before Asian Financial Crisis.
- 5.
The time trend utilizes the original sample without deleting the outliers. The summary statistics in Table 53.1 use the sample after deleting Tobin’s Q, which is less than 1% or larger than 99% in the whole sample
- 6.
Panel A uses the original sample, while Panel B uses the sample where Tobin’Q less than 1% or larger than 99% has been deleted.
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Acknowledgements
Hai-Chin YU is the contact author and currently serving as a visiting professor at Rutgers University. This paper was presented in 2008 Quantitative Finance and Risk Management Conference held by National Chou-Tong University in Shi-Chu, Taiwan. We are grateful to C.F. Lee, Ken Johnson, Joe Wright and the participants for their helpful comments.
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Yu, HC., Chen, CS., Hsieh, DT. (2010). Keiretsu Style Main Bank Relationships, R&D Investment, Leverage, and Firm Value: Quantile Regression Approach. In: Lee, CF., Lee, A.C., Lee, J. (eds) Handbook of Quantitative Finance and Risk Management. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-77117-5_53
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