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Firm-level investor favoritism and the external financing and capital expenditure anomalies

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Abstract

Prior literature documents a positive (negative) relation between past (future) stock returns and both external financing and capital expenditures. In this study, we examine whether managers’ financing and capital expenditure decisions are associated with firm-level investor favoritism (neglect) and, therefore, whether managers exploit investor mispricing by issuing more (less) capital and investing more (less) in capital expenditures when firm-level investor sentiment is high (low), which leads to more negative future stock returns. We employ both a stock’s extreme return momentum and extreme trading volume to capture firm-level investor favoritism (neglect), which reflects firm-level investor overpricing (underpricing) due to investor sentiment. We find that both external financing and capital expenditure decisions are positively (negatively) associated with favoritism (neglect) and that the previously documented negative association between future stock returns and external financing is more pronounced in periods of favoritism. However, we find no association between future stock returns and capital expenditures after controlling for external financing. These findings suggest that managers’ financing and capital expenditure decisions are associated with firm-level investor favoritism/neglect, and that managers exploit investor mispricing in making financing decisions, resulting in lower future stock returns.

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Notes

  1. As detailed in Footnote 4, we find in sensitivity tests that our results are robust and not sensitive to the cutoffs used to measure firm-level investor favoritism/neglect.

  2. In addition, measuring analyst overoptimism involves a look-ahead bias since forward-looking realizations are needed and the measure suffers from data availability issues as it requires analyst forecasts. Still, to shed light on differences between firm-level investor favoritism and analyst overoptimism, we perform supplementary analyses (untabulated for brevity). We find that favoritism (neglect) is positively correlated with high (low) analyst overoptimism, defined as the top (bottom) decile of analysts’ forecasted long-term growth less actual long-term growth. Yet, the correlations are relatively low, with a correlation coefficient of 0.08 (0.06) between favoritism (neglect) and high (low) analyst overoptimism, suggesting that these measures relate to distinct constructs. This is confirmed by our results being robust to the inclusion of high and low analyst overoptimism in all of our analyses. Finally, we replicate our analyses after replacing favoritism (neglect) with high (low) analyst overoptimism. Overall, we find that the results are weaker (with individual results either similar or weaker) using high and low analyst optimism, corroborating investor favoritism and neglect as distinct and more appropriate measures of firm-level mispricing due to investor sentiment.

  3. We measure Favorit and Neglect as of the beginning of year t (i.e., end of year t-1) to reflect the fact that managers make financing and investment decisions in year t based on the pricing of the firm’s stock and the firm-level favoritism they observe and to avoid having financing and capital expenditure decisions made during year t affect these pricing measures.

  4. In sensitivity tests, we replicate our analyses using three alternative measures of Favorit/Neglect: i) Favorit (Neglect) is an indicator variable equal to one for the intersection of the highest (lowest) Momentum quintile and the highest (lowest) Volume quintile, zero otherwise; ii) Favorit (Neglect) is an indicator variable equal to one for the intersection of the highest (lowest) Momentum quartile and the highest (lowest) Volume quartile, zero otherwise; and iii) Favorit (Neglect) is an indicator variable equal to one for the intersection of the highest (lowest) Momentum tercile and the highest (lowest) Volume tercile, zero otherwise. The untabulated results are qualitatively similar to those tabulated and our inferences remain unchanged.

  5. Our results are qualitatively similar and our inferences remain unchanged if we i) exclude observations with Qefwa values greater than 10, in line with Baker and Wurgler (2002); ii) substitute Qefwa,t-1 with Qt-1, the ratio of the market value of total assets to the book value of total assets; iii) substitute Qefwa,t-1 with MBt-1, the ratio of the market value of equity to the book value of equity; iv) substitute Qefwa,t-1 with the three components of the decomposition of MBt-1, introduced by Rhodes et al. (2005), which relate to the firm-specific pricing deviation from short-run industry pricing, sector-wide, short-run deviations from firms’ long-run pricing, and long-run pricing to book.

  6. Prior research documents higher external financing and capital expenditures for firms with higher past stock returns (Morck et al. (1990), Titman et al. (2004)). Hence, in sensitivity analyses, we also include past stock returns (Momentum, defined as the 12-month raw stock returns in year t-1) to control for managers’ external financing decisions that are related to stock returns but are less likely to be associated with investor mispricing. Results (untabulated) are predominantly unchanged with this additional control variable. Favorit and Neglect are measured using extreme past stock returns (and trading volume). Thus, even if Favorit/Neglect and Momentum relate to two distinct constructs and play different roles in our analyses, they are measured using the same underlying variable. As a result, we do not include past stock returns as an additional control variable in our main specifications for concerns of multicollinearity, but include it in sensitivity analyses.

  7. FirmExpVol reflects expected idiosyncratic volatility and incorporates past and current stock return volatility, which is also in line with Lipson et al. (2011) who include current stock return volatility in their model.

  8. For all analyses, industry fixed effects are based on the Fama–French (1997) 48-industry classification. All results are robust to clustering standard errors by industry and year and controlling for the average level of market-level sentiment over year t-1 using the proxy developed in Eq. (2) of Baker and Wurgler (2006). This measure of market-level sentiment is obtained from Jeffrey Wurgler’s website: http://people.stern.nyu.edu/jwurgler/.

  9. We examine capital expenditures instead of growth in assets as in Lipson et al. (2011) or growth in net operating assets as in Fairfield et al. (2003) because capital expenditures is an explicit investing decision made by management while growth in assets or net operating assets is more likely influenced by forces outside of management’s control. As a sensitivity analysis with respect to the measurement of capital expenditures, we replicate our tests using the following alternative measures: i) capital expenditures, scaled by beginning-of-year total assets, less the median capital expenditures scaled by beginning-of-year total assets for the corresponding Fama French 48-industry for the prior fiscal year, and ii) capital expenditures, scaled by beginning-of-year total assets, less the average capital expenditures scaled by beginning-of-year total assets for the firm over the prior three years. The results (untabulated) are qualitatively similar using the industry-adjusted measure but marginally weaker using the time-series-adjusted measure.

  10. We incorporate delisting return adjustments following Beaver et al. (2007).

  11. Bootstrapped t-statistics for the null hypothesis of zero long-short stock returns are based on Monte Carlo simulations. We randomly assign firm-quarter observations into each decile by matching the number of observations in each decile. We simulate 1,000 long-short stock returns to construct a bootstrapped empirical distribution and calculate the standard deviation of the hedge returns from the empirical distribution. We then calculate a bootstrapped t-statistic as the realized hedge return minus the mean of the bootstrapped empirical distribution divided by the bootstrapped standard deviation.

  12. As a proxy for arbitrage costs, Lipson et al. (2011) include idiosyncratic volatility, measured as stock return volatility, in their analysis of the relation between growth in assets and future stock returns. In additional analyses, we include in Eq. (3) scaled decile-ranked expected volatility (FirmExpVoli,t) and the interactions between expected volatility and both external financing and capital expenditures. We find the inferences from the regression results remain identical with the inclusion of these additional control variables.

  13. Our final sample of firms are distributed across different stock exchanges: specifically, NASDAQ (59.8 percent of sample firms), NYSE (31.4 percent), and Amex (8.8 percent). We find that the results in this study are robust to the inclusion of exchange fixed effects.

  14. We note that equity financing is larger for neglect firms than for the full sample of firms in Table 2, which is inconsistent with neglect being negatively associated with external financing. However, these statistics are unconditional. We show in Table 3 that the relation between neglect and equity financing is negative in a regression.

  15. 0.0938/0.0644 = 145.7% and -0.0497/0.0644 = -77.2%

  16. 0.0766/0.0388 = 197.4% and -0.0345/0.0388 = -88.9%

  17. 0.0096/0.0212 = 45.4% and -0.0109/0.0212 = -51.5%

  18. In line with Fedyk et al. (2017), we define STEM industries as the industries with the following three-digit SIC codes: 283, 355, 357, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 381, 382, 384, 386, 481, 737, 873.

  19. 0.0225/0.0674 = 33.4%, 0.0113/0.0674 = 16.8%, -0.0140/0.0674 = -20.8%, and -0.0079/0.0674 = -11.7%

  20. Consistent with the relation between capital expenditures and favoritism/neglect occurring primarily through the external financing channel, we do not find that the association between capital expenditures and firm-level favoritism/neglect is significantly stronger for firms in STEM industries or firms with lower analyst following compared to firms in non-STEM industries or firms with higher analyst following, respectively, yet we find that the association between capital expenditures and firm-level favoritism is significantly stronger for firms with lower institutional ownership compared to firms with higher institutional ownership.

  21. In a supplementary analysis, Titman et al. (2004) attempt to shed light on this by excluding firms with secondary equity offerings in the prior five years. However, this approach does not consider all external financing undertaken by firms nor does it provide evidence on the relation between capital expenditures and future stock returns after controlling for the extent of external financing.

  22. To test whether the relations between either external financing or capital expenditures and future stock returns are more pronounced during periods of extreme stock return momentum, we estimate Eq. (3) after including the variables LowRet and HighRet, where LowRet (HighRet) equals one if the observation is in the lowest (highest) Momentum decile, zero otherwise. We also include interaction variables between LowRet and HighRet and the external financing and capital expenditure variables. In untabulated analyses, we find that the results in Table 6 are robust to the inclusion of these additional variables, which suggests our results are not driven solely by extreme stock return momentum but rather investor favoritism/neglect.

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Authors and Affiliations

  1. Arizona State University, Tempe, AZ, USA

    Lucile Faurel

  2. University of Southern California, Los Angeles, California, USA

    Mark Soliman

  3. University of Notre Dame, Notre Dame, IN, USA

    Jessica Watkins

  4. Emory University, Atlanta, GA, USA

    Teri Lombardi Yohn

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  1. Lucile Faurel
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  2. Mark Soliman
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  4. Teri Lombardi Yohn
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APPENDIX

APPENDIX

Table 9 Variable Definitions
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Faurel, L., Soliman, M., Watkins, J. et al. Firm-level investor favoritism and the external financing and capital expenditure anomalies. Rev Quant Finan Acc (2024). https://doi.org/10.1007/s11156-024-01299-9

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