Management deception, big-bath accounting, and information asymmetry: Evidence from linguistic analysis
Introduction
How does a firm's information environment change after accounting big baths? Prior literature provides evidence on both positive (Elliott & Shaw, 1988; Francis, Hanna, & Vincent, 1996; Haggard, Howe, & Lynch, 2015) and negative (Bens & Johnston, 2009; Kirschenheiter & Melumad, 2002; Kothari, Shu, and Wysocki 2009; Moore, 1973) consequences of big baths on the information environment. As managers have discretion regarding whether to incur a large write-off, and can decide the timing and amount of the write-off, management's incentives are important in studying the effects of big baths. However, such incentives are unobservable. Investors may use managerial characteristics to infer management incentives. Among the most salient managerial characteristics in this setting is truthfulness; thus this study examines how truthfulness (or conversely, deceptiveness) affects investors' perceptions of big baths.
According to upper-echelons theory (Hambrick & Mason, 1984), the ethical attentiveness throughout the organization is instilled by its leaders (Patelli & Pedrini, 2015). A series of accounting fraud scandals over the last decades put leadership ethics at the forefront of the heated debate on financial-reporting truthfulness (Mihajlov & Miller, 2012; Tourish & Vatcha, 2005). Ethics is an intrinsic part of managers' behavior (Solomon, 1992). As firms' high-level decision makers, top managers are likely to follow a cognitive and rational approach that revolves around moral judgments about the issues when making ethical decisions, just as an individual making a choice when facing an ethical dilemma (Albert, Scott, and Turan 2015; Kohlberg, 1981; Reynolds, 2006; Vitell, Lumpkin, and Rawwas 1991; Weber, 1990). Big baths are managerial decisions that can be the result of managers' ethical considerations of the firms' welfare, or can be the result of managers' incentives to maximize their personal utility. Being truthful or deceptive to investors and other stakeholders also indicates management's ethical choice of how they view their responsibility to the firm's stakeholders.
On one hand, big baths can manifest themselves as exceptionally large negative discretionary accruals. On the other hand, big baths can consist of one-time, large write-offs, and may include restructuring charges, asset impairments, and litigation losses. These write-offs are generally reflected as “special items” in the financial statements. There are two ways to look at a big bath. If a company reports a loss that is larger than expectations, it could be the case that there are certain issues within the firm that warrant such actions and that managers are truthfully conveying such information to the capital market and other stakeholders. In line with that view, some analysts interpret big baths as managers’ positive response to existing problems (Elliott & Shaw, 1988). Big baths can also “clear the air” (Haggard et al., 2015). That is, by writing off assets when their carrying values are less than the market values, the reported values of the assets are realigned with their economic values. As a result, firm-level information asymmetry following a big bath should decrease.
However, big baths are sometimes used as an earning-management technique to shift current earnings to future periods. As Levitt (1998) points out, if big-bath charges are overly conservatively estimated with “extra cushioning,” they can miraculously be reborn as income when future earnings fall short. Big baths can also be used to secure bonus payments. Often, managers' rewards are tied to meeting certain performance targets. In an economic downturn, managers may follow the big-bath approach by bundling as much bad news into the current period as possible, aiming to make their targets easier to achieve in the next period.1 In cases of new management, big-bath accounting can be used to mitigate top executives’ job-security concerns.2 The new manager can benefit from taking a big bath, blaming the low earnings on the previous manager so as to display an improved financial performance in future (Moore, 1973).
From investors' perspectives, the action of taking big baths by a firm is observable, but the motivations behind the action are not entirely clear. Hou (2015) finds that in a well-diversified market, idiosyncratic information risk is priced when information is subject to managers’ discretion and thus ambiguous. Can investors infer the motivations of managers by observing their types – truthful or deceptive – and associating their actions with the types? By taking advantage of newly-developed technologies, investors are now analyzing the linguistic patterns displayed in management speech. Investors have been using algorithmic textual analysis, CIA lie-detection techniques, and more recently, audio analysis of management speech, to seek an edge with stock calls, sector sentiment, and overall market direction.3
Earnings-conference calls, in which managers discuss their firms' financial performance with analysts and investors, are important information sources to search for signs of management deception. If a manager is discovered to be deceptive when discussing her firm's financial results, and the firm takes a big bath at the time, will investors perceive this big bath to have low credibility? Will investors associate this big bath with such motivations as meeting earnings targets or securing bonus payments? If this is the case, we would expect to observe an increase in information asymmetry following big baths taken by deceptive managers. Conversely, if a firm's manager takes a big bath and she is considered truthful, investors may perceive this big bath as having high credibility. In this case, the information asymmetry may decrease.
A primary reason both practicing accountants and researchers care about information asymmetry is that it reflects the information environment.4 Clearly an important issue related to big baths is whether these accounting events improve or deteriorate the firm's information environment. However, to broaden the scope of our study, in additional analyses we also test for trading volume (another commonly employed outcome variable in this line of literature).
This study builds on Larcker and Zakolyukina (2012) who find that certain words are significantly associated with management deception. For instance, deceptive CEOs use more “reference to general knowledge” and “extreme positive emotion” words, and use fewer “anxiety” and “shareholder value” words. The linguistic approach proposed by Larcker and Zakolyukina (2012) is based on psychological theories linking deception to linguistic behavior (Vrij, 2008), and is built up by applying a well-developed and frequently used psychosocial dictionary – LIWC. There are an increasing number of applications of LIWC analyses in deception detection, and also in personality, forensic, clinical, relationship, and cultural assessments (Chung & Pennebaker, 2012, chap. 12). Providing further validity to Larcker and Zakolyukina (2012), Loughran and McDonald (2013) demonstrate that the credibility of managers is diminished by having an overly positive S-1 in the IPO process, consistent with Larcker and Zakolyukina (2012) who find that deceptive CEOs use significantly more positive emotion words in conference calls. Another piece of evidence substantiating the usefulness of CEOs’ linguistic patterns in signifying deception is provided by Hobson, Mayew, Peecher, and Venkatachalam (2017), who demonstrate that once given instructions on the “cognitive dissonance” in the CEOs remarks, auditors are able to more precisely detect fraudulent companies as well as the unidentified “red flag” sentences in earnings-conference calls.
We use Larcker and Zakolyukina (2012) approach to identify truthful and deceptive managers in this paper, and examine whether the change in information asymmetry around a big-bath event is a function of managerial deception. As CEOs play the largest role in corporate decision making, we focus on the linguistic pattern of CEOs.
In our primary analyses we employ a difference-in-differences research design coupled with propensity-score matching of treatment and control firms. This approach provides strong control for potential confounding events as well as omitted-variable biases. We find evidence that investors are able to discern managers’ deception levels from conference calls and that information asymmetry is affected accordingly. Specifically, we find that information asymmetry (proxied using Amihud, 2002 illiquidity measure and bid-ask spreads) increases significantly after big baths taken by deceptive CEOs as compared to those taken by less deceptive CEOs. In additional analyses, we find that this effect is more pronounced when a CEO who has been truthful in the past becomes deceptive in the bath year. Our inferences are robust to a variety of regression specifications and other robustness tests.
The study adds to the big-bath line of research by examining a potentially important factor that could affect the impact of big-bath taking on information asymmetry. Second, our study contributes to research on how investors use ex-ante credibility of CEOs to interpret financial reporting quality (e.g., Loughran & McDonald, 2013). We do this by applying textual-analysis techniques to accounting issues to infer management's intentions using subtle linguistic cues. Finally, the study adds to management-ethics research by examining management deception and its associated capital-market consequences in the setting of big-bath taking, an economically important event as documented in prior research.5 This paper thus also contributes to the literature by studying the financial outcomes of CEO idiosyncratic characteristics and psychological patterns.
Section snippets
Big-bath literature
Prior research has examined the timing, motivation for, and consequences of taking big baths. Moore (1973) finds that discretionary accounting decisions that reduce income are more likely to be made in a period of management changes. The write-offs, many of which have substantial economic consequences, reflect decisions by corporate management. Kirschenheiter and Melumad (2002) construct a theoretical model to demonstrate that managers under-report earnings the most when the news is
Big baths
Identifying big-bath events is not straightforward. Generally speaking, the trade-off is between having a large enough sample that utilizes an objective (or “less subjective”) approach versus the researcher using her intuition to attempt to capture the spirit of big baths by coming up with a self-constructed measure.11
Sample selection and effectiveness of propensity-score matching
Table 1 presents the sample-selection process for the final treatment firms. Table 2 exhibits the logistic regression of the big-bath indicator on important predictors of such big baths.19 Of all the matching variables used in the propensity-score matching under the Accruals Approach in defining big baths, BTM_LAG (lag of book to market ratio), TURNOVER_A_LAG (lag of annual share turnover), LEVERAGE, and
Additional analyses and robustness tests
In this section, we first provide robustness tests related our primary analyses. In particular, we examine the effects of our matching procedures as well as other research-design choices. Next, we attempt to investigate the “mechanisms” through which investors learn about the big baths. Finally, we explore the effects of investors' prior experience with CEOs’ deceptive (or truthful) speech.
Conclusion
While some studies find that big baths can improve the information environment, others find that they degrade the information environment. To expand upon prior literature, this paper looks at big baths in conjunction with management deception. In particular, we examine whether management deception can decrease the credibility of big baths and alter investors’ perceptions. We find that information asymmetry (proxied for using the Amihud illiquidity measure and bid-ask spreads) increases
Acknowledgements:
We thank the Michael Lee-Chin research grant for financial support. We thank Stefan Anchev, Muhammad Azim, Stephanie Cheng, Mahfuz Chy, Bingxu Fang, Shushu Jiang, Ross Lu, Gordon Richardson, Barbara Su, Aida Wahid, Eyub Yegen, Wuyang Zhao, two anonymous reviewers, and seminar participants at the Rotman School of Management and the Canadian Academic Accounting Association for their comments and inputs. Hope gratefully acknowledges funding from the Deloitte Professorship.
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