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The purpose of this paper is to examine the circumstances under which high quality audits reduce a firm's cost of debt. The paper extends previous research by Pittman and…
The purpose of this paper is to examine the circumstances under which high quality audits reduce a firm's cost of debt. The paper extends previous research by Pittman and Fortin by considering how auditor quality relates to the capital cycle and industry of the firm.
The paper uses a sample of US initial public offerings (IPOs) from 1986 to 1998 to analyze a firm's debt costs for the five years following the IPO. The paper uses a firm's private age as a proxy for its capital cycle and existing banking relationships to capture the likely extent of debt dependence prior to IPO. The authors separately analyze technology firms from other firms.
Consistent with prior literature, it is found that firms that are young at the time of an IPO pay higher interest rates and auditor quality plays a significant role in lowering the cost of debt financing. Consistent with the hypotheses made, the authors also observe that the effect of auditor quality is larger for firms in the high tech industry sector. Further, the relationship between auditor quality and age depends on industry, with the benefits of hiring a high quality auditor primarily accruing to younger tech firms and older non‐tech firms.
While the issue of auditor quality and cost of debt has been examined by previous researchers, the additional insight that the effect of auditor quality depends on both capital cycle (age) and industry of a firm, increases understanding of the circumstances under which the audit of financial statements is socially desirable and economically valuable to investors and other stakeholders.
While markets are vulnerable to a sharper unwinding of bullish bets on the tech sector, comparisons with the 2000 dotcom bubble are misguided: Big Tech firms enjoy a…
Big data are indispensable in scientific endeavours ranging from nuclear research to climate studies. However, there is a growing misperception that congeries of data can…
Big data are indispensable in scientific endeavours ranging from nuclear research to climate studies. However, there is a growing misperception that congeries of data can be easily reconstructed into competitive business insights. Such notions have been encouraged by a plethora of mainstream techno-utopian forecasts.
This paper investigated such claims and related big data developments, including its “systems-first” and oligopolistic orientations. Due to the paucity of current scholarship on an admittedly pessimistic topic, the paper studied contrarian developments in the industry by relying on secondary data. The study of experts and scholars; industrial trends; and discrepancies and critical gaps in the mainstream data narrative were sourced to prognosticate the likely trajectory of many data giants.
A key finding was that the big data industry faces an untenable market bubble worth trillions of dollars. This will have severe consequences for common digital access and social stability worldwide. Evidence presented also suggests that the data industrial complex may undergo a function creep by facilitating a transition from surveillance capitalism to surveillance society.
Primary data for a study of this nature may take years to materialize. This is a “first-pass” study that seeks to illuminate latent dangers facing the big data/AI sector. There is a paucity of scholarly study that even remotely touches on this topic. Therefore, supporting arguments was sourced from contemporary reports and expert study (secondary data).
As control of data may have geostrategic implications, balkanization of the wired ecosystem may be underway with Russia and China leading the way. Future superpowers may be defined by the way they handle data. The concentration of data in fewer hands may also affect citizen innovation.
A break-down of the data industrial complex may lead to social mayhem as the monetization of presently free software, blogs and social media platforms may be unfeasible.
This topic has hardly been explored due to the novelty of big data, its applications and the daily hype over its potentials. This paper boldly describes dark countercurrents in the industry.