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Time-varying managerial overconfidence and pecking order preference

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Abstract

This paper examines whether managerial overconfidence enhances or weakens pecking order preference. We construct time-varying managerial words-based (i.e. tone of Chairman’s Statement) and action-based (i.e. firm investment and directors’ trading) overconfidence measures. Both optimistic tone and industry-adjusted investment have significant and negative impacts on the pecking order coefficient in the Shyam-Sunder and Myers (J Financ Econ 51:219–244, 1999) regression framework. Overconfident managers tend to use more equity than debt to finance deficits. This new evidence is consistent with the proposition that overconfident managers who underestimate the riskiness of future earnings believe that their debt (equity) is undervalued (overvalued) and therefore prefer equity to debt financing. Thus, managerial overconfidence can lead to a reverse pecking order preference. We also find that managerial overconfidence significantly weakens pecking order preference especially in firms with high earnings volatility and small firms.

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Notes

  1. De Jong et al. (2010) refer to Frank and Goyal’s (2003) finding that firm size is positively related to the degree of pecking order as pecking order puzzle or size anomaly.

  2. The Myers–Majluf (1984) type model shows that the pecking order is conditional on the asymmetric information between managers and outside investors. Managers with more inside information are reluctant to use external financing, especially the equity, which is undervalued by the outsiders.

  3. Similarly, Fama and French’s (2005) study suggests that asymmetric information problems are neither the only nor perhaps even an important determinant of capital structure. They further argue that “any forces that cause firms to systematically deviate from pecking order financing imply that the pecking order, as the complete model of capital structure proposed by Myers (1984) and Myers and Majluf (1984), is dead”. Other potential conditions of pecking order include agency costs (Leary and Roberts 2010), corporate taxes (Stiglitz 1973; Hennessy and Whited 2005) and transaction costs (Welch 2006).

  4. Similarly, Malmendier et al. (2011) develop a model of capital structure with overconfident managers who overestimate firms’ mean future cash flow and therefore believe that their firms are undervalued by the market. Their model also predicts a pecking order preference arised from managerial overconfidence, conditional on raising risky external capital.

  5. The standard pecking order preference refers to a preference for debt over equity financing, In contrast, the reverse pecking order preference refers to a preference for equity over debt financing. Empirically, we expect that at least more than half of the financing deficit is financed by equity if a firm follows a reverse pecking order preference.

  6. Importantly, Hackbarth (2008) argues that the ambiguous effects of managerial overconfidence on the pecking order may shed light on the inconclusive cross-sectional findings on the standard pecking order prediction.

  7. There is an emerging literature that attempts to capture managerial overconfidence based on computational linguistic analysis of corporate disclosures (Ataullah et al. 2017; Hilary et al. 2016).

  8. The literature (e.g., Frank and Goyal 2003; Low and Chen 2004; Bharath et al. 2009; Brick and Liao 2017) often considers information aysmmetry as a key condition of the pecking order preference.

  9. A good description of conditional theory is as follows: “… the theory finds support when its basic assumptions hold in the data, as should reasonably be expected of any theory” (Bharath et al. 2009).

  10. Their model allows for two frictions including tax benefit of debt and financial distress cost. Overconfidence is defined as “the overestimation of mean returns to investment”. Managerial overconfidence can lead to either overinvestment or underinvestment, depending on the availability of internal funds or riskless debt financing. In particular, overconfident managers with sufficient internal or riskless financing are prone to overinvest. Another implication of overconfidence is that overconfident manager may have a biased perception of the cost of external financing. For this reason, if there is financing deficit, overconfident managers may underinvest.

  11. Shareholders have a call option on the firm with an exercise price of X. In a call-option graph where the horizontal axis is cash flow to firm and vertical axis is cash flow to shareholders, if firm’s cash flow is beyond X, shareholders will exercise the option by buying the firm from the debt holders for the price X. If firm’s cash flow is below X, shareholders will not exercise the call option and debt holders receive entire firm’s cash flow.

  12. As shown in Black–Scholes model, the value of call option is positively related to the variance of the continuous stock returns.

  13. They propose the following model to capture the asymmetric pecking order behaviour: \( \Delta D_{it} = \alpha + \beta_{1} d_{it} + \beta_{po} DEF_{it} + \beta_{sur} d_{it} *DEF_{it} + \varepsilon_{it} \), where, \( d_{it} \) is a dummy variable that equals one if \( DEF_{it} < 0 \), and zero otherwise. The pecking order coefficient is \( \beta_{po} \) and (\( \beta_{po} + \beta_{sur} \)) respectively for the firms with financing deficits and financing surpluses. They find that the estimated pecking order coefficient is 0.90, 0.74 and 0.09 respectively for financing surpluses, normal deficits and large deficits.

  14. This can also be calculated as the sum of proceeds from stock options and other proceeds from sale/issue of common/preferred stock.

  15. See Appendix A in Bessler et al. (2011) for a detailed discussion on the calculation of the DEF. Bessler et al. (2011) also use Worldscope data for their international study.

  16. Existing behavioural finance studies (e.g., Malmendier and Tate 2005; Malmendier et al. 2011) tend to model managerial overconfidence as a habitual behaviour which is static. This static approach can be problematic because other behavioural biases, especially self-attribution bias, may affect the confidence level.

  17. Tone analysis (and more generally textual analysis) is becoming increasingly popular in recent accounting and finance studies. For example, Rogers et al. (2011) examine the relation between disclosure tone and shareholder litigation. For a review on studies of corporate disclosures, please see Li (2010a).

  18. In Diction, optimism is defined as “language endorsing some person, group, concept or event, or highlighting their positive entailments”.

  19. As a unique feature of Diction software, there is standardization procedure when calculating a particular item. In particular, we compare our collected Chairman’s Statements to three alternative norms in Diction including (1) all cases, (2) corporate financial reports and (3) corporate public relations. Our empirical results are qualitatively similar using alternative norms.

  20. The terms “positive/negative” and “optimistic/pessimistic” are often used interchangeably in the literature (e.g., Davis et al. 2012). Li (2010b) standardize the terms to “positive/negative” instead of “optimistic/pessimistic”.

  21. An earlier version of LIWC has a category named “optimism”, however in the 2007 version words are classified more broadly into “positive emotion” and “negative emotion”.

  22. In Diction, certainty is defined as “language indicating resoluteness, inflexibility, and completeness and a tendency to speak ex cathedra”.

  23. The eigenvalue of second component is close to one (i.e. 1.135).

  24. In terms of the determinants of tone (e.g., current performance, growth opportunities, operating risks and complexity), Huang, Teoh and Zhang (2013) find that tone, as measured using Loughran and McDonald (2011) wordlist, is positively related to market-to-book and volatility of stock returns and negatively related to firm size, age and number of business segments. Our orthogonalized tone measure (TONE_RES) controls for four standard determinants of capital structure (i.e. market-to-book, size, tangibility and profitability). The results are similar when we further control for stock price performance and firm age in Eq. 2.

  25. The eigenvalues of first and second components are 2.509 and 1.139 respectively.

  26. Many previous studies on UK accounting narratives focus on Chairman’s Statement (see e.g., Smith and Taffler 2000; Clatworthy and Jones 2003, 2006). Smith and Taffler (2000) use Chairman’s Statement to predict firm bankruptcy. Schleicher and Walker (2010) conduct manual content analysis of the tone of forward-looking statements (i.e. outlook sections) in the UK annual report (most of which are located at the end of Chairman’s Statement).

  27. For example, Clatworthy and Jones (2003) argue that accounting narratives such as UK Chairman’s Statement allow “management” to describe corporate financial performance. In addition, Schleicher and Walker (2010) attribute the bias in the tone of outlook statements to “managers”. In particular, they argue that “managers with a willingness to engage in impression management are likely to target forward-looking statements”, while 73.5% of the forward-looking narratives are located in Chairman’s Statement (Schleicher and Walker 2010).

  28. In terms of the procedure of content analysis, we first extract Chairman’s Statements from annual report. Next, we detect transformation errors in the combined text file using the Spelling & Grammar function in Microsoft Word 2010. Finally, various types of errors are manually corrected before the texts are inputted in the content analysis software.

  29. Most of the observations are after 2000 because machine readable annual reports are almost not available in the 1990s.

  30. This is consistent with 36.2% in Lin et al.’s (2008) Taiwan firm sample.

  31. Take debt capacity into consideration, the financing decisions of firms with “low to moderate” leverage are more likely to follow pecking order behaviour, while dynamic trade-off theory becomes the primary explanation for the financing behaviour of firms with “high” leverage (and consequently high financial distress costs) (Lemmon and Zender 2010).

  32. Another way to test the impact of managerial overconfidence on the preference for debt over equity financing is logistic analysis which examines the probability of debt issues relative to equity issues. However, this approach fails to control for firm fixed effects.

  33. We thank an anonymous referee for suggesting we use this orthogonalized investment-based measure of overconfidence.

  34. Over 14% of the observations (i.e. 326 out of 2283 observations) have zero leverage in our sample.

  35. The consumer confidence data can be downloaded from the European Commission website (https://ec.europa.eu/info/business-economy-euro/indicators-statistics/economic-databases), and has been used in previous studies (e.g., Schmeling 2009) to measure investor sentiment.

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Authors and Affiliations

  1. Loughborough University, Loughborough, UK

    Andrew Vivian

  2. University of Leeds, Leeds, UK

    Bin Xu

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  1. Andrew Vivian
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  2. Bin Xu
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Correspondence to Bin Xu.

Appendix

Appendix

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Table 9 Variable definitions
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Vivian, A., Xu, B. Time-varying managerial overconfidence and pecking order preference. Rev Quant Finan Acc 50, 799–835 (2018). https://doi.org/10.1007/s11156-017-0647-8

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