Unit root testing via the stationary bootstrap

A nonparametric, residual-based stationary bootstrap procedure is proposed for unit root testing in a time series. The procedure generates a pseudoseries which mimics the original, but ensures the presence of a unit root. Unlike many others in the literature, the proposed test is valid for a wide class of weakly dependent processes and is not based on parametric assumptions on the data-generating process. Large sample theory is developed and asymptotic validity is shown via a bootstrap functional central limit theorem. The case of a least squares statistic is discussed in detail, including simulations to investigate the procedure's finite sample performance.     

R&D and Tobin's q in an emerging financial market: the case of the Athens Stock Exchange

This paper aims at providing further evidence on the consequences of R&D investment on Tobin's q for firms publicly traded in an emerging financial market. Panel data methodology is applied using data for the manufacturing and computer firms listed in the Athens Stock Exchange, a market classified as emerging by the major securities analysts, for the period 1996–2004. The empirical findings show first, that the Greek firms' R&D investment effect on the market value of a firm is consistent with other US and European studies. Second, the impact of the R&D investment on the market value is higher for small firms. The findings of this paper may have significant industrial and technological policy implications for other emerging markets sharing similar characteristics to Greece. Copyright © 2010 John Wiley & Sons, Ltd.     

Audit‐firm group appointment: an artificial intelligence approach

Auditor appointment can be regarded as a matter of pursued audit quality and is driven by several factors. The adoption of an effective auditor procurement process increases the likelihood that a company will engage the right auditor at a fair price. In this study, three techniques derived from artificial intelligence (AI) are used to propose models capable of discriminating between cases where companies appoint a Big 4 or a Non‐Big 4 auditor. These three AI methods are then compared with the broadly used method of logistic regression. The results indicate that two of the AI techniques outperform logistic regression. In addition, one method further improves its performance by applying bagging. Finally, significant factors associated with auditor appointment are revealed. Copyright © 2009 John Wiley & Sons, Ltd.     

Local block bootstrap inference for trending time series

Resampling for stationary sequences has been well studied in the last couple of decades. In the paper at hand, we focus on nonstationary time series data where the nonstationarity is due to a slowly-changing deterministic trend. We show that the local block bootstrap methodology is appropriate for inference under this locally stationary setting without the need of detrending the data. We prove the asymptotic consistency of the local block bootstrap in the smooth trend model, and complement the theoretical results by a finite-sample simulation.     

Perceived Environmental Uncertainty in scenario planning

Scenario planning is a strategy tool with growing popularity in both academia and practical situations. Current practices of scenario planning are largely based on existing literature which utilises scenario planning to develop strategies for the future, primarily considering the assessment of perceived macro-external environmental uncertainties. However there is a body of literature hitherto ignored by scenario planning researchers, which suggests that Perceived Environmental Uncertainty (PEU) influences the micro-external as well as the internal environment of the organisation. This paper reviews the most dominant theories on scenario planning process and PEU, developing three propositions for the practice of scenario planning process. Furthermore, it shows how these propositions can be integrated in the scenario planning process in order to improve the development of strategy.     

Cyclicality of liquidity creation: Nonlinear evidence from US bank holding companies

Using a panel smooth transition regression framework on a new proxy of the business cycle (BC) index and quarterly data of US bank holding companies from 1993Q1 to 2020Q1, our results provide empirical support for the theory that the BC has a nonlinear effect on liquidity creation. We find a positive and highly significant nonlinear effect of the BC on liquidity creation, which not only supports the pro-cyclicality of liquidity creation but also improves the liquidity creation estimation compared to previous studies. The results are robust to different proxies of the BC and model specifications. We also document that US bank holding companies create liquidity more during the expansion phase (normal times) than during the recession phase (crisis times) of the BC, suggesting an asymmetrical effect of BC changes on liquidity creation. Our findings have important implications for financial market participants by suggesting that banks should keep alternative sources of funding on hand during the BC recession phase. Insights from our study also provide policy implications for central banks and prudent supervisors to consider when incentivizing banks, for instance, by lowering regulatory requirements, adjusting the policy rate, or implementing any other quantitative easing policy during the BC recession phase to keep the financial system efficient.