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Book-to-market effect and product life cycle

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Abstract

This paper examines the relationship between product life cycle and book-to-market effect on cross-sectional stock returns. While previous papers suggest that the book-to-market effect is related to a firm’s market value and fundamental value, this paper examines the product life cycle, which directly affects future cash flows. We find that the book-to-market effect is stronger for firms with a long product life cycle, which is consistent with the mispricing story in explaining the book-to-market effect. We further find that the role of product life cycle is more critical for firms with high investor limited attention, and that the product life cycle in part explains intangible returns.

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Notes

  1. Fama and French (1992) demonstrate the significant role of the book-to-market ratio in explaining average stock returns. Fama and French (1993) introduced the influential three-factor model, which includes the market risk factor, size factor, and book-to-market factor. Based on the three-factor model, Fama and French (2015) further developed the five-factor model by incorporating profitability and investment factors. Park (2019) found that the Intangible-adjusted Book-to-Market Ratio is a better predictor of cross-sectional returns compared to the traditional Book-to-Market Ratio.

  2. Chen and Zhang (1998) indicate that value firms usually have high financial leverage and that their future earnings are uncertain. Vassalou and Xing (2004) show that the book-to-market effect only exists among the highest default risk firms. They argue that the book-to-market is largely related to financial distress.

  3. Several papers support Zhang (2005). García-Feijóo and Jorgensen (2010) show a positive relationship between a firm’s book-to-market and operating leverage, between operating leverage and its stock return, and operating leverage and systematic risk. Overall their results support the book-to-market effect associated with systematic risk. Furthermore, Choi (2013) find that the leverage and asset betas are increased in the recession period but the growth firm’s asset betas are not sensitive to the economic conditions.

  4. Numerous papers provide evidence showing that the book-to-market effect is due to investor mispricing. Dechow and Sloan (1997) indicate that the book-to-market effect is caused by erroneous market expectations of long-run earnings growth. Ali et al. (2003) find that the book-to-market effect is stronger in firms with higher idiosyncratic volatility, higher transaction cost, and lower investor sophistication. Griffin and Lemmon (2002) find that small firms and firms with less analyst coverage exhibit stronger book-to-market effects.

  5. Taylor (2001) indicates that high tech firms usually have short product life cycles, and Ross et al. (2008) indicate that high-tech firms, retailers, and automotive firms have higher beta due to the large sensitivity of their revenues to different business conditions. Zhang (2015) explains that R&D expenditures are inflexible and have high adjustment costs. When R&D intensive firms face serious financial constraints, they may discontinue R&D projects, causing R&D expenditures to be positively associated with distress risk.

  6. The idea of limited attention is that investors do not react to the information until they recognize the information. Bernard and Thomas (1989) find that the equity market underreacts to the post earnings announcement drift (PEAD). Barber and Odean (2008) demonstrate that investors are net buyers when the firm attracts their attention. Hirshleifer et al. (2018) indicate that investors undervalue a firm’s innovative originality because of their limited attention.

  7. Prior literature has often determined the product life cycle using financial statement information (Dickinson 2011; Faff et al. 2016; Cantrell and Dickinson 2020). For instance, Dickinson (2011) identifies the life cycle based on the patterns of operating, investing, and financing cash flows. However, it is important to note that financial statement data may include noisy information, such as real earnings management (Roychowdhury 2006).

  8. We use the industry product life cycle in our main analysis. In subSect. 3.3, we conduct the industry product life cycle only when the firm’s product life cycle is missing. The results show that our findings are robust for different product life cycle construction.

  9. We present the factor loadings from the Fama–French five factor model in Table A2 of the Internet Appendix.

  10. We show the results in the Table A6 of the Internet Appendix.

  11. We show the results in the Table A7 of the Internet Appendix.

  12. We show the results in the Table A9 of the Internet Appendix.

  13. The decomposition was first introduced by Rhodes-Kropf et al. (2005). They use the market-to-book decomposition to investigate the link between misvaluation and investment in the form of acquisitions.

  14. \({m}_{it}={\alpha }_{0jt}+{\alpha }_{1jt}{b}_{it}+{\alpha }_{2jt}{ni}_{it}^{+}+{\alpha }_{3jt}{I}_{\left(<0\right)}\left({ni}_{it}^{+}\right)+{\alpha }_{4jt}{LEV}_{it}+{\varepsilon }_{it}\), where \({m}_{it}\) is log market value of equity, \({b}_{it}\) is log book value of common equity, \({ni}^{+}\) is log absolute value of net income, \({LEV}_{it}\) is book leverage, and \({\varepsilon }_{it}\) is an error term. An indicator variable \({I}_{\left(<0\right)}\) is interacted with log absolute net income \({(ni)}^{+}\) to separately estimate the earnings multiple for firms with negative net income.

  15. \({m}_{it}-{b}_{it}= {m}_{it}-v\left({\theta }_{it};{\alpha }_{jt}\right)+v\left({\theta }_{it};{\alpha }_{jt}\right)-v\left({\theta }_{it};{\alpha }_{j}\right)+v\left({\theta }_{it};{\alpha }_{j}\right)-{b}_{it}\), where i denotes firm, t denotes time, j denotes industry. \({m}_{it}\) is fundamental value. \(v\left({\theta }_{it};{\alpha }_{jt}\right)\) is fitted value from cross-sectional regression.\(v\left({\theta }_{it};{\alpha }_{j}\right)\) is predicted fundamental value which is calculated from average multiples over time. \({b}_{it}\) is book value.

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Acknowledgments

We thank two anonymous reviewers, Konan Chan, Hung-Kun Chen, Sheng-Syan Chen, Yan-Shing Chen, Po-Hsuan Hsu, Chia-Wei Huang, Wei-Chuan Kao, Cheng-Few Lee (the editor), Woan-lih Liang, and seminar and conference participants in 2019 NCTU International Finance Conference and National Chiao Tung University for their valuable comments. This work was financially supported by the Center for Research in Econometric Theory and Applications (Grant no. 112L900201) from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education in Taiwan. This work was also supported by Ministry of Science and Technology in Taiwan (NSTC 112-2410-H-002 -151 -MY2).

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Authors and Affiliations

  1. Department of Finance, College of Business, Chung Yuan Christian University, Taoyuan City, Taiwan

    Ming-Che Hu

  2. Department of Information Management and Finance, National Yang Ming Chiao Tung University, Hsinchu City, Taiwan

    Alex YiHou Huang & Dan-Liou Yu

  3. Department of Finance, College of Management, Center for Research in Econometric Theory and Applications, National Taiwan University, Taipei City, Taiwan

    Yanzhi Wang

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Hu, MC., Huang, A.Y., Wang, Y. et al. Book-to-market effect and product life cycle. Rev Quant Finan Acc (2024). https://doi.org/10.1007/s11156-024-01270-8

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