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This study investigates the impact of uncertainty on the mean-variance relationship. We find that the stock market's expected excess return is positively related to the market's conditional variances and implied variance during low uncertainty periods but unrelated or negatively related to conditional variances and implied variance during high uncertainty periods. Our empirical evidence is consistent with investors' attitudes toward uncertainty and risk, firms' fundamentals and leverage effects varying with uncertainty. Additionally, we discover that the negative relationship between returns and contemporaneous innovations of conditional variance and the positive relationship between returns and contemporaneous innovations of implied variance are significant during low uncertainty periods. Furthermore, our results are robust to changing the base assets to mimic the uncertainty factor and removing the effect of investor sentiment.

The social relations model (SRM; Kenny, 1994) explicitly proposes that leadership simultaneously operates at three levels of analysis: group, dyad, and individual (perceiver and target). With this model, researchers can empirically determine the amount of variance at each level as well as those factors that explain variance at these different levels. This chapter shows how the SRM can be used to address many theoretically important questions in the study of leadership and can be used to advance both the theory of and research in leadership. First, based on analysis of leadership ratings from seven studies, we find that there is substantial agreement (i.e., target variance) about who in the group is the leader and little or no reciprocity in the perceptions of leadership. We then consider correlations of leadership perceptions. In one analysis, we examine the correlations between task-oriented and socioemotional leadership. In another analysis, we examine the effect of gender and gender composition on the perception of leadership. We also explore how self-ratings of leadership differ from member perceptions of leadership. Finally, we discuss how the model can be estimated using conventional software.

The paper seeks to introduce a method based on the Markowitz mean‐variance portfolio approach to outline a theory of future firm, its environment, and management accounting systems (MASs). The approach is based on the target to choose propositions for the theory to maximise expected credibility.

A conceptual approach with mathematical modelling is presented. It is illustrated by a set propositions extracted from a survey presented previously. The survey results are used to design a theory of future MASs by choosing an efficient sub‐set of propositions with the help of combinatory optimisation.

When maximising the credibility of a theory of future MAS, the trade‐off between the mean and the variance of expert judgments should be considered. The resulting theory depends on the level for disagreement aversion by the theory builder. The approach is a useful tool to design a theory of future MASs.

How to select the level of trade‐off between the mean and the variance of judgments should be analysed further. The methods to design propositions for a theory should be researched in more detail. Possible instrumentation and response biases should be assessed.

This approach provides a practical method to analyse judgments to form a consistent set (portfolio) of propositions. It can be applied to any theory building where propositions cannot be tested (such as in futures research) but only assessed by experts.

The paper includes a new approach for researchers, consultants, and teachers to form a theory of future phenomena such as future MASs.

This chapter introduces empirical studies of firm performance and related risk outcomes conducted in the management and finance fields presenting underlying theoretical rationales as they have evolved over time. Early finance studies of market-based returns predominantly found positively skewed return distributions that conform to assumptions about higher returns associated with more risky investments. Subsequent studies found that performance outcomes measured as accounting-based financial returns generally display left-skewed distributions that reflect negative risk-return relationships. This artifact was first observed by Bowman (1980), thus often referred to as the “Bowman paradox” because it contravened the conventional assumptions in finance. The management studies have largely confirmed the inverse risk-return observations but often following rather confined research streams. A contingency perspective inspired by prospect theory and behavioral rationales have investigated the lagged effects of performance on risk outcomes and vice versa. Another stream has focused on the spurious relationships between negatively skewed performance distributions and the inverse risk-return associations. A third approach considered the performance and risk outcomes as deriving from the firms responding in distinct ways to exogenous changes. These studies reach comparable results but underpinned by very different rationales. The finance studies observe deviations from the pure doctrine of positive risk-return associations embedded in the widely adopted capital asset pricing model (CAPM) and note deficiencies with alternative interpretations that even question the validity of CAPM. A more recent strain of studies in behavioral finance observes how many (even professional) investment managers have biases that lead to inverse relationships between perceived risk and return outcomes. While these diverse fields of study have different starting points, they uncover an increasing number of interesting commonalities that can inspire the ongoing search for explanations to observed left-skewed financial returns and negative risk-return correlations across firms.

Markowitz’s mean‐variance approach is used to identify the returns to vertical investment in the pork industry. In addition to previous efforts, this paper considers not only returns to stock ownership, but uses operating return on investment in pork slaughter and hog production to evaluate the impacts of vertical investment within the industry segment. Results suggest there are indeed diversification incentives for vertical investment in the pork industry. However, results do differ for vertical direct investment versus investment through stock ownership.

The purpose of this paper is to investigate if Bowman’s Paradox (negative association between risk and return) is caused by managerial myopia. It also attempts to disentangle whether results are more consistent with one or more potential explanations.

The paper uses univariate statistics and OLS regressions. Empirically examines the relationship between four risk and return proxies, across a wide ranging time period and utilizing a number of model specifications. Results hold after using three-way clustered errors and using a more robust rolling five year, fixed regression methodology measure.

Confirms the existence of the Paradox. Also documents that the association between risk and return is positive in “winner” firms and negative in “loser” firms. Upon further analysis, the earlier negative risk-return relationship is found to entirely be due to the volatility of the (short term) income statement component of the performance terms. Results imply that executives of winner (loser) firms are less (more) likely to manage earnings or engage in other value destroying activities.

The study is confined by the typical archival study limitations; including potential endogeneity, selection biases and generalizability of the results.

Anecdotal evidence indicates that the business community makes extensive use of these performance measures. These performance measures are also pervasive in academic research. Given the importance of controlling for both managerial and firm performance, a good performance proxy is quintessential.

Although over 30 years have passed since Bowman (1980) first observed the negative correlation, to date, no consensus explanation exists. Findings suggest that Bowman’s Paradox, is potentially a manifestation of managerial myopia. Thus, this result contributes to several existing research streams.

This chapter investigates the relative magnitude of the benefits of global diversification from the viewpoint of domestic investors in various countries by forming time-rolling efficient frontiers. To enhance feasibility of asset allocation strategies, the constraints of short-sales and over-weighting investments are taken into account. The empirical results suggest that local investors in less developed countries, particularly in Latin America, East Asia, and Southern Europe, comparatively benefit more from global diversification. Investors in the countries of civic-law origin tend to benefit more from global investment than the ones in the common-law states. Although the global market has become more integrated over the past decades, diversification benefits for domestic investors declined but did not vanish. The results of this chapter are useful for asset management professionals to determine target markets to promote the sales of international funds.

This study aims to draw on a less explored predictor – the average correlation of pairwise returns on industry portfolios – to predict stock market returns (SMRs) in the USA.

This study uses the average correlation approach of Pollet and Wilson (2010) and predicts the SMRs in the USA. The model is estimated using monthly data for a long time horizon, from July 1963 to December 2018, for the portfolios comprising 48 Fama-French industries. The model is extended to examine the effects of a longer lag structure of one-month to four-month lags and to control for the effects of a number of variables – average variance (AV), cyclically adjusted price-to-earnings ratio (CAPE), term spread (TS), default spread (DS), risk-free rate returns (R_f) and lagged excess market returns (R_s).

The study finds that the two-month lagged average correlation of returns on individual industry portfolios, used individually and collectively with financial predictors and economic factors, predicts excess returns on the stock market in an effective manner.

The methodology and results are of interest to academics as they could further explore the use of average correlation to improve the predictive powers of their models.

Market practitioners could include the average correlation in their asset pricing models to improve the predictions for the future trend in stock market returns. Investors could consider including average correlation in their forecasting models, along with the traditional financial ratios and economic indicators. They could adjust their expected returns to a lower level when the average correlation increases during a recession.

The finding that recession periods have effects on the SMRs would be useful for the policymakers. The understanding of the co-movement of returns on industry portfolios during a recession would be useful for the formulation of policies aimed at ensuring the stability of the financial markets.

The study contributes to the literature on three counts. First, the study uses industry portfolio returns – as compared to individual stock returns used in Pollet and Wilson (2010) – in constructing average correlation. When stock market becomes more volatile on returns, the individual stocks are more diverse on their performance; the comovement between individual stock returns might be dominated by the idiosyncratic component, which may not have any implications for future SMRs. Using the industry portfolio returns can potentially reduce such an effect by a large extent, and thus, can provide more reliable estimates. Second, the effects of business cycles could be better identified in a long sample period and through several sub-sample tests. This study uses a data set, which spans the period from July 1963 to December 2018. This long sample period covers multiple phases of business cycles. The daily data are used to compute the monthly and equally-weighted average correlation of returns on 48 Fama-French industry portfolios. Third, previous studies have often ignored the use of investors’ sentiments in their prediction models, while investors’ irrational decisions could have an important impact on expected returns (Huang et al., 2015). This study extends the analysis and incorporates investors’ sentiments in the model.

Based on more than 2.7 million online reviews (ORs) collected with big data analytical techniques from Booking.com and TripAdvisor.com, this paper aims to explore if and to what extent environmental discourse embedded in ORs has an impact on electronic word-of-mouth (e-WOM) helpfulness across eight major destination cities in North America and Europe.

This study gathered, by means of Big Data techniques, 2.7 million ORs hosted on Booking.com and TripAdvisor, and covering hospitality services in eight different destinations cities in North America (New York City, Miami, Orlando and Las Vegas) and Europe (Barcelona, London, Paris and Rome) over the period 2017–2018. The ORs were analysed by means of ad hoc content analytic dictionaries to identify the presence and depth of the environmental discourse included in each OR. A negative binomial regression analysis was used to measure the impact of the presence/depth of online environmental discourse in ORs on e-WOM helpfulness.

The findings indicate that the environmental discourse presence and depth influence positively e-WOM helpfulness. More specifically those travelers who write explicitly about environmental topics in their ORs are more likely to produce ORs that are voted as helpful by other consumers.

Implications highlight that both hotel managers and platform developers/managers should become increasingly aware of the importance that customer attach to environmental practices and initiatives and therefore engage more assiduously in environmental initiatives, if their objective is to improve online review helpfulness for other customers reading the focal reviews. Future studies might include more destinations and other operationalizations of environmental discourse.

This study constitutes the first attempt to capture how the presence and depth of hospitality services consumers’ environmental discourse influence e-WOM helpfulness on multiple digital platforms, by means of a big data analysis on a large sample of online reviews across multiple countries and destinations. As such it makes a relevant contribution to the area at the intersection between big data analytics, e-WOM and sustainable tourism research.

Purpose: This work provides an empirical analysis of investor behaviour's simultaneous influence due to the surprise effect caused by COVID-19 cases and government responses to market risk. This analysis compares tourism assets risk with other sectors and different types of investors' assets and categories in Europe.

Design: The paper applies an ARIMA with a GARCH model to predict conditional volatility of models for market uncertainty. Nonlinear models, factor analysis and time series linear regression for stationary variables in first differences are applied to predict market uncertainty.

Findings: We demonstrate that market risk does not arise from COVID-19 cases but instead from the surprise effect, as the market accurately predicts future cases. Only the volatility of the sectors Travel, Airline, and Utility are influenced by both surprise effect and government response, but only the travel sector reveals an interaction effect with both government response effort and surprise effect.

Originality: The article mutually studies the simultaneous interactions among investor behaviour due to the surprised effect caused by COVID-19 and government responses to the pandemic and the influence on professional investors' volatility in two asset types and between different sectors.

Practical implications: With this model and results, investors and financial service providers may verify whether or not government intervention during pandemic periods is effective in reducing uncertainty and risk levels on sectors, types of investors and different sorts of assets.