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The purpose of this paper is to analyze the ex ante projected future trajectories of real tourism exports and relative tourism export prices of the EU-15, conditional on expert real gross domestic product growth forecasts for the global economy provided by the Organisation for Economic Co-operation and Development for the years 2013-2017.

To this end, the global vector autoregression (GVAR) framework is applied to a comprehensive panel data set ranging from 1994Q1 to 2013Q3 for a cross-section of 45 countries. This approach allows for interdependencies between countries that are assumed to be equally affected by common global developments.

In line with economic theory, growing global tourist income combined with decreasing relative destination price ensures, in general, increasing tourism demand for the politically and macroeconomically distressed EU-15. However, the conditional forecast increases in tourism demand are under-proportional for some EU-15 member countries.

Rather than simply relying on increases in tourist income, the low price competitiveness of the EU-15 member countries should also be addressed by tourism planners and developers in order to counter the rising competition for global market shares and ensure future tourism export earnings.

One major contribution of this research is that it applies the novel GVAR framework to a research question in tourism demand analysis and forecasting. Furthermore, the analysis of the ex ante conditionally projected future trajectories of real tourism exports and relative tourism export prices of the EU-15 is a novel aspect in the tourism literature since conditional forecasting has rarely been performed in this discipline to date, in particular, in combination with ex ante forecasting.

This study examines the relation between the presence of analysts’ long-term growth (LTG) forecasts and the post-earnings-announcement drift (PEAD).

Using a sample of firm-quarters from 1995 to 2013, the author conducts various regression analyses.

The author finds that the magnitude of PEAD is significantly smaller for firms with LTG forecasts. The relationship holds after controlling for a wide range of explanatory variables for PEAD returns or for the presence of LTG forecasts. The author further investigates three nonexclusive hypotheses to explain this relationship. First, LTG forecasts may convey incremental value-relevant information that facilitates investors’ processing of short-term earnings information. Second, the presence of LTG forecasts may indicate superiority in analysts’ short-term forecast ability and identify firms with more efficient short-term forecasts. Third, the presence of LTG forecasts may be associated with cross-sectional differences in the persistence of earnings surprises. The author finds that none of these fully accounts for the negative relationship between the presence of LTG forecasts and PEAD returns. Instead, the relationship may be a result of the presence of LTG forecasts capturing some unobservable firm characteristics beyond those identified in prior studies.

This study contributes to the PEAD literature by identifying a novel analyst-based predictor of the cross-sectional variation in PEAD returns.

The purpose of this study is to investigate whether audit quality is associated with the speed with which managers revise earnings forecasts to arrive at the actual earnings through the lens of the auditor selection theory. This study examines this relationship in a unique institutional setting, Japan, where nearly all managers disclose earnings forecasts.

The authors pioneer an empirical proxy to capture the speed of management forecast revisions based on well-established principles from the finance and disclosure literatures. This proxy is tested alongside other disclosure proxies (namely, accuracy, frequency and timeliness) to assess the influence of audit quality on managerial forecasting behavior.

This empirical analysis shows that forecast revision speed is higher for firms that select higher-quality auditors. While firms that select higher-quality auditors revise forecasts in a more timely fashion, these firms revise less frequently. Moreover, the authors find that the influence of audit quality on forecast revisions is asymmetric. Specifically, the analysis of downward forecast revisions shows that higher-quality auditors are associated with firms that disclose bad news via forecasts revisions faster, more frequently and in a more timely fashion. However, the analysis of upward forecast revisions shows that higher-quality auditors have no effect on the speed with which firms disclose good news via forecast revisions, even though they are associated with less frequent but more timely forecast revisions. These findings have important implications for prior studies that consistently document an asymmetric response of the stock market to good news and bad news.

The authors provide evidence on the relationship between audit quality and management earnings forecasts using a novel and intuitive measure that captures forecast revision speed. This measure speaks to the growing interest in understanding the notion of speed and timing of voluntary disclosures. This study provides a more robust and comprehensive measure of the speed with which managers revise their earnings forecasts to arrive at the actual earnings. Furthermore, this study is among the first to document an asymmetric effect of audit quality on the type of news disclosed in forecast revisions.

This study aims to examine what underlies the estimated relation between idiosyncratic volatility and realized return.

Idiosyncratic volatility has a dual effect on stock pricing: it not only affects investors' expected return but also affects the efficiency of stock price in reflecting its value. Therefore, the estimated relation between idiosyncratic volatility and realized return captures its relations with both expected return and the mispricing-related component due to its dual effect on stock pricing. The sign of its relation with the mispricing-related component is indeterminate.

The estimated relation between idiosyncratic volatility and realized return decreases and switches from positive to negative as the estimation sample consists of proportionately more ex ante overvalued observations; it increases and switches from negative to positive as the estimation sample consists of proportionately more ex post overvalued observations. In sum, the relation of idiosyncratic volatility with the mispricing-related component dominates its relation with expected return in its estimated relation with realized return. Moreover, its estimated relation with realized return varies with research design choices and even switches sign due to their effects on its relation with the mispricing-related component.

The novelty of the study is evident in the implication of its findings that one cannot infer the sign of the relation of idiosyncratic volatility with expected return from its estimated relation with realized return.

The authors examine the role of analysts’ prior experience of forecasting for firms exposed to epidemics on analysts’ forecast accuracy during the COVID-19 pandemic.

The authors examine the impact of analysts’ prior epidemic experience on forecast accuracy by comparing the changes from the pre-COVID-19 period (calendar year 2019) to the post-COVID period extending up to March 2023 across HRE versus non-HRE analysts. The authors consider a full sample (194,980) and a sub-sample (136,836) approach to distinguish “Recent” forecasts from “All” forecasts (including revisions).

The study's findings reveal that forecast accuracy for HRE analysts is significantly higher than that for non-HRE analysts during COVID-19. Specifically, forecast errors significantly decrease by 0.6% and 0.15% for the “Recent” and “All” forecast samples, respectively. This finding suggests that analysts’ prior epidemic experience leads to an enhanced ability to assess the uncertainty around the epidemic, thereby translating to higher forecast accuracy.

The finding that the expertise developed through an experience of following high-risk firms in the past enhances analysts’ performance during the pandemic sheds light on a key differentiator that partially explains the systematic difference in performance across analysts. The authors also show that industry experience alone is not useful in improving forecast accuracy during a pandemic – prior experience of tracking firms during epidemics adds incremental accuracy to analysts’ forecasts during pandemics such as COVID-19.

The study findings should prompt macroeconomic policymakers at the national level, such as the central banks of countries, to include past epidemic experiences as a key determinant when forecasting the economic outlook and making policy-related decisions. Moreover, practitioners and advisory firms can improve the earning prediction models by placing more weight on pandemic-adjusted forecasts made by analysts with past epidemic experience.

The uncertainty induced by the COVID-19 pandemic increases uncertainty in global financial markets. Under such circumstances, the importance of analysts’ role as information intermediaries gains even more importance. This raises the question of what determines analysts’ forecast accuracy during the COVID-19 pandemic. Building upon prior literature on the role of analyst experience in shaping analysts’ forecasts, the authors examine whether experience in tracking firms exposed to prior epidemics allows analysts to forecast more accurately during COVID-19. The authors find that analysts who have experience in forecasting for firms with high exposure to epidemics (H1N1, Zika, Ebola, and SARS) exhibit higher accuracy than analysts who lack such experience. Further, this effect of experience on forecast accuracy is more pronounced while forecasting for firms with higher exposure to the risk of COVID-19 and for firms with a poor ex-ante informational environment.

This study attempts to conduct a comparative analysis between dynamic and static asset allocation to achieve the long-term target return on asset liability management (ALM). This study conducts asset allocation using the ex ante expected rate of return through the outlook of future economic indicators because past economic indicators or realized rate of returns which are used as input data for expected rate of returns in the “building block” method, most adopted by domestic pension funds, does not fully reflect the future economic situation. Vector autoregression is used to estimate and forecast long-term interest rates. Furthermore, it is applied to gross domestic product and consumer price index estimation because it is widely used in financial time series data. Based on asset allocation simulations, this study derived the following insights: first, economic indicator filtering and upper-lower bound computation is needed to reduce the expected return volatility. Second, to reach the ALM goal, more stocks should be allocated than low-yielding assets. Finally, dynamic asset allocation which has been mirroring economic changes actively has a higher annual yield and risk-adjusted return than static asset allocation.

This paper aims to analyze the ex ante and ex post economic efficiency of the preventive agreement (concordato preventivo) or composition with creditors as defined by the Italian Bankruptcy Law. This study examines four possible outcomes of the procedure: homologation (confirmation); the degree of dissent/consent of creditors; the revocation, admissibility or inadmissibility; the declaration of the company bankruptcy in preventive agreement.

This paper uses data from 728 Italian companies which filed for preventive agreement in 2016. In reference to each of the four possible outcomes, this study applies nine logit regressions to analyze the effects of a series of efficiency variables ex ante (corporate-based drivers) and ex post (procedure-based drivers).

Results show the relevance of the debt structure, ownership structure and virtuous behavior, corporate governance and management systems, as well as effectivity of the court control on the preventive agreement outcome.

This paper draws on original data of bankruptcy in Italy and gives empirical evidence of the ex ante and ex post factors on the outcomes of the preventive agreement.

Robotic process automation (RPA) has recently emerged as a technology focusing on the automation of repetitive, frequent, voluminous and rule-based tasks. Despite a few practical examples that document successful RPA deployments in organizations, evidence of its economic benefits has been mostly anecdotal. The purpose of this paper is to present a step-by-step method to RPA investment appraisal and a business case demonstrating how the steps can be applied to practice.

The methodology relies on design science research (DSR). The step-by-step method is a design artefact that builds on the mapping of processes and modelling of the associated costs. Due to the longitudinal nature of capital investments, modelling uses discounted cashflow and present value methods. Empirical grounding characteristic to DSR is achieved by field testing the artefact.

The step-by-step method is comprised of a preparatory step, three modelling steps and a concluding step. The modelling consists of compounding the interest rate, discounting the investment costs and establishing measures for comparison. These steps were applied to seven business processes to be automated by the case company, Estate Blend. The decision to deploy RPA was found to be trivial, not only based on the initial case data, but also based on multiple sensitivity analyses that showed how resistant RPA investments are to changing circumstances.

By following the provided step-by-step method, executives and managers can quantify the costs and benefits of RPA. The developed method enables any organization to directly compare investment alternatives against each other and against the probable status quo where many tasks in organizations are still carried out manually with little to no automation.

The paper addresses a growing new domain in the field of business process management by capitalizing on DSR and modelling-based approaches to RPA investment appraisal.

This paper aims to analyze the antecedents of two variables concerning the presence of quality certifications in hotel chains: the (ex ante) decision to become a member of the quality system and the (ex post) trend to increase or decrease the number of certified properties. Six hypotheses are posed and tested.

The empirical investigation is carried out on the Spanish Q for Quality in Tourism using a database including 295 hotel chains and 2,727 hotels.

The results evidence the presence of differences in the behavior of hotel chains relative to certification depending on their size, market segment, customer origin and the geographical concentration of their establishments.

This research deepens in how the hotel chain characteristics affect the effectiveness of a quality certification. The consideration of two stages in investment decisions allows the authors to identify differences in the ex ante and ex post decision processes. As a result, one factor (geographical concentration) has been detected as being underrated by managers in the first stage.

Este artículo analiza los antecedentes de dos variables relacionadas con las certificaciones de calidad en cadenas hoteleras: La (ex-ante) decisión de formar parte de un sistema de calidad, y la (ex-post) tendencia a incrementar o reducir el número de establecimientos certificados. Seis hipótesis han sido propuestas y contrastadas.

La investigación empírica ha sido desarrollada en el marco de la marca Q de calidad para el turismo en España usando una base de datos que incluye 295 cadenas hoteleras y 2,727 hoteles.

Los resultados ponen de manifiesto la presencia de diferencias en el comportamiento de las cadenas hoteleras en materia de certificación dependiendo de su tamaño, segmento de mercado atendido, origen de la clientela y del grado de concentración geográfica de sus establecimientos.

El artículo profundiza en cómo las carfacterísticas de la cadena hotelera afectan a la eficacia de la certificación de calidad. Tener en consideración la existencia de dos etapas en las decisones de inversión nos permite identificar diferencias entre los procesos de decisión ex-ante y ex-post. Como resultado, hemos observado que un factor (la concentración geográfica) está siendo infravalorado por parte de os directivos en sus decisiones en la primera etapa.

Establishing the strength of a novel variable–mortgage debt as a fraction of US gross domestic product (GDP)–on forecasting capitalisation rates in both the US office and multifamily sectors.

The authors specify a vector error correction model (VECM) to the data. VECM are used to address the nonstationarity issues of financial variables while maintaining the information embedded in the levels of the data, as opposed to their differences. The cap rate series used are from Green Street Advisors and represent transaction cap rates which avoids the problem of artificial smoothness found in appraisal-based cap rates.

Using a VECM specified with the novel variable, unemployment and past cap rates contains enough information to produce more robust forecasts than the traditional variables (return expectations and risk premiums). The method is robust both in and out of sample.

This has direct implications for governmental policy, offering a path to real estate price stability and growth through mortgage access–functions largely influenced by the Fed and the quasi-federal agencies Fannie Mae and Freddie Mac. It also offers a timely alternative to interest rate-based forecasting models, which are likely to be less useful as interest rates are to be held low for the foreseeable future.

This study offers a new and highly explanatory variable to the literature while being among the only to model either (1) transactional cap rates (versus appraisal) (2) out-of-sample data (versus in-sample) (3) without the use of the traditional variables thought to be integral to cap rate modelling (return expectations and risk premiums).