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Controlling corporate governance decision-making RISK is one of the prime safeguarding directives in the PCAOB’s requirement that the financials of Market Trading Firms [MTFs] are reasonably free from material error and thus are reliable indications of the results of operations. In this Corporate Governance Risk [CGR] context, it is important to understand the Interacting- & Integrated-Systemic Components of CGR and how they have evolved over time. We examine these aspects from Circa 1985 to date. We argue that the Driving Control System is: The Firm’s decision-making system of Internal Control over Financial Reporting [ICoFR] that impacts the Nature of CGR—to wit, Increasing CGRisk results as management experiences Increasing difficulties in maintaining adequate ICoFR. We examine the historical development of these systemic components and how they have spawned an Economic Sector of Firms that Rate & Score MTFs on the nature of their CGR profile, one currently predominate aspect of which is: The Nature of the MTF’s Caretaker Persona. With this as context, we present a duel-effect inferential design that examines the reliability of the Bloomberg™ Market Navigation Platform’s Institutional Shareholder Services™ [ISS]-Profiler as an information-link to be used in selecting MTFs to create CGR-sensitive Investment Portfolios. Our inferential results suggest for the MTFs in the ISS-Groups [{1: Lowest CGR} & {5: Mid-Level CGR} & {10: Highest CGR]}, it is likely that: (i) The decisions made by all involved in Management’s System of ICoFR, in its current COSO-version, are reflective of these MTF’s Caretaker Personas, (ii) The effects of these Caretaker Personas are analytically perceptible as reflected in the MTF’s Financials, and (iii) are aligned with the expected Nature of the ISS-Groups.

AS5[v.Dec:2017] issued by the Public Company Accounting Oversight Board [PCAOB] requires the use of Analytical Procedures [AP] at the Planning and Substantive Phases of Assurance Audits for firms traded on active exchanges. We argue that one aspect of AP, relative to risk-setting, should be vetting the information that is produced/published by the audit client pertaining to Regulation G [v.SEC:2003] called: Non-GAAP information. In our research, we intend to leverage the longstanding Reg[G] requirements to extend the Non-GAAP information to firm performance profiles reported for the Environment, Social, and Governance[ESG]Platform on BloombergÒ. There are two research foci: (1) Offer an AP-Model that uses GAAP & ESG variables to contribute audit evidence useful in making the decision to launch an AP-Extended Procedures examination of the firm’s Enterprise Resource Planning & Control [ERP&C] protocols, and (2) Profile a random accrual-set of firms indexed on Bloomberg so as to offer population parameter estimates for refining the AP-Model. The AP-Model is based upon correlational associations for the ESG- & GAAP-variables from the: Income, Balance Sheet & Cash Flow Statements. If there seems to be a disconnect between the nature of these associations for the ESG-variables and those of the GAAP-variables, the auditor may use this as audit evidence in making the decision to conduct an Extended Procedures Examination of the firm’s [ERP&C] protocols. As for the other focus, we found that for the accrual of firms tested there is no inferential evidence that the ERP&C-protocols are consistent drivers for both the ESG- and the GAAP variable sets.

Univariate Time Series Models [TSM] use only a Panel of historical data to produce forecasts. The tacit belief in using TSM is that the past information portends the future of the longitudinal data-stream. This is likely in certain cases such as strictly Ergodic Panel segments of sufficient size in the overall Panel. A question of interest is: Is the success of TSM in these contexts generalizable? The test of this question used a Litmus-Test design to examine the performance profile of TSM for a longitudinal time series the last point of which is a Turning Point [TP]. Specifically, the inference measure will use the Relative Absolute Error [RAE] of the TSM tested over three forecasting horizons. In this testing, five TSM configurations were employed; the TPs are identified using a fixed screening filter applied to randomly selected firm Panels actively traded on the S&P500 from 2005 through 2013.  There is no evidence that any of the five TSM outperformed the RW model which is incidentally the TP. The impact of these results is that one cannot assume that the effectiveness of TSM generalizes to all domains—in particular—forecasting after TPs that seems to be a Domain Lacuna where the effectiveness of TSM will be compromised.