Heterogeneity in Financial Market Participation: Appraising its Implications for the C-CAPM

This paper presents empirical evidence that accounting for heterogeneity in financial market participation is important for evaluating the empirical performance of the Consumption-based Capital Asset Pricing Model (C-CAPM). Using the US Consumer Expenditure Survey as a common testing ground, I re-assess three well-known characterizations of the equity premium puzzle (i) the inconsistency of the representative agent's IMRS with Hansen and Jagannathan bounds; (ii) Mehra and Prescott's calibration of a large representative agent's risk aversion; (iii) Hansen and the Singleton's large structural estimates of the preference parameters based on aggregate data. In all three cases, the estimates of risk aversion conditional upon financial market participation are not as far from reasonable values as the corresponding unconditional ones. The differences suggest that part of the equity premium puzzle can be accounted for by the use of a representative agent assumption rather than a more appropriate "representative stockholding agent assumption.     

The Impact of Ambiguity on Managerial Investment and Cash Holdings

Standard finance theory suggests that managers invest in projects that, in expectation, produce returns that justify the use of capital. An underlying assumption is that managers have the information necessary to understand the distributional properties of the pay‐offs underlying the decision. This paper examines firm investment behavior when managers are likely to find it more challenging to develop expectations of pay‐offs, namely during periods of increased macroeconomic ambiguity. In particular, we examine how macroeconomic ambiguity – proxied by the variance premium (Drechsler, 2010 ) and the dispersion in forecasts of corporate profits from the Survey of Professional Forecasters (Anderson et al., 2009 ) – impacts managerial capital investment and cash holdings. Consistent with ambiguity theory, we find that macroeconomic ambiguity is negatively associated with capital investment and positively associated with cash holdings. These results are robust to alternative explanations related to risk, investor sentiment and economic conditions. Moreover, consistent with recent theoretical real options literature, we find that ambiguity reduces the value of investment opportunities, while risk increases the value of such opportunities. Overall, these findings provide initial empirical evidence on the economic distinction between ambiguity and risk with respect to managerial investment and cash holdings.     

Auditor Independence,Current and Future NAS Fees and Audit Quality: Were European Regulators Right?

Abstract

European Union adopted the Regulation (EU) No 537/2014, which in practice prohibits the joint provision of audit and most types of non-audit services (NAS). Regulators presume that NAS fees weaken auditor independence and, as a result, impair audit quality. As the evidence at the European level does not support this view, the question of whether the new regulation will enhance auditor independence remains open. We examine the association between future NAS fees and audit quality by distinguishing among tax, audit-related and other services. We base the analysis on a sample of Spanish listed companies for the period between 2005 and 2016, finding a consistent negative association between future other NAS fees and audit quality. This suggests that the expectation of future purchases of this type of NAS may impair auditor independence. Conversely, for tax and audit-related services results are not significant. Taken together, results suggest that European regulators should seek for further evidence before banning NAS, as some of them may in fact enhance audit quality.     

PRIVATIZATION AND PERFORMANCE: THE CASE OF FOUR AUSTRALIAN FINANCIAL INSTITUTIONS

ABSTRACT: This paper investigates the performance after privatization of institutions in Australian banking and insurance. Privatization was anticipated to improve firm performance in Australia and elsewhere, yet findings are mixed. A comparative institutional approach is taken to analyzing firm performance in the longer term which allows for further structural change. A CAMEL analysis of performance before and after privatization events is undertaken for four privatized institutions, two each from banking and insurance sectors. These are matched with private peer institutions. Privatized institutions are found to perform quite similarly to private peer institutions both before and after privatization.     

An application of rule-based forecasting to a situation lacking domain knowledge

Rule-based forecasting (RBF) uses rules to combine forecasts from simple extrapolation methods. Weights for combining the rules use statistical and domain-based features of time series. RBF was originally developed, tested, and validated only on annual data. For the M3-Competition, three major modifications were made to RBF. First, due to the absence of much in the way of domain knowledge, we prepared the forecasts under the assumption that no domain knowledge was available. This removes what we believe is one of RBF’s primary advantages. We had to re-calibrate some of the rules relating to causal forces to allow for this lack of domain knowledge. Second, automatic identification procedures were used for six time-series features that had previously been identified using judgment. This was done to reduce cost and improve reliability. Third, we simplified the rule-base by removing one method from the four that were used in the original implementation. Although this resulted in some loss in accuracy, it reduced the number of rules in the rule-base from 99 to 64. This version of RBF still benefits from the use of prior findings on extrapolation, so we expected that it would be substantially more accurate than the random walk and somewhat more accurate than equal weights combining. Because most of the previous work on RBF was done using annual data, we especially expected it to perform well with annual data.     

What's New in New Forms of Organizing? On the Construction of Gender in Project‐Based Work

abstract    In several industries, projects are now the normal form of work for individuals. The consequences of project work have not so far been subject to critical inquiry, however. This implies inquiry not only on how people handle project work at work, it also means inquiring into how they live their lives when working by projects. In this paper, we study this from a constructionist gender perspective, in which project work is seen as an ongoing construction of patterns of femininity and masculinity in society. The aim of the paper is to contribute to an understanding of how project work is related to the ongoing construction of femininity and masculinity in the work and lives of human beings.
From a narrative study of individuals in the same project team in an IT-consultancy company, we discuss masculinization and femininization in project-based work. It appears that current project work practices imply reproduction of masculinities such as rationality, efficiency, control, devotion to work etc, while femininization is instead found in the rhetoric of the organizational context and the expectations on newly recruited women. The organization was in the process of femininization through rhetoric on 'family friendliness', but everyday life for consultants was not spent at the organization but in project teams in the customers' offices. Projects are special in the sense that they are clearly delimited episodes of work in which it is possible to apply entirely different norms than 'outside' the project – which makes the tendency to reproduce traditional masculinities even stronger.     

Time-varying combinations of predictive densities using nonlinear filtering

We propose a Bayesian combination approach for multivariate predictive densities which relies upon a distributional state space representation of the combination weights. Several specifications of multivariate time-varying weights are introduced with a particular focus on weight dynamics driven by the past performance of the predictive densities and the use of learning mechanisms. In the proposed approach the model set can be incomplete, meaning that all models can be individually misspecified. A Sequential Monte Carlo method is proposed to approximate the filtering and predictive densities. The combination approach is assessed using statistical and utility-based performance measures for evaluating density forecasts of simulated data, US macroeconomic time series and surveys of stock market prices. Simulation results indicate that, for a set of linear autoregressive models, the combination strategy is successful in selecting, with probability close to one, the true model when the model set is complete and it is able to detect parameter instability when the model set includes the true model that has generated subsamples of data. Also, substantial uncertainty appears in the weights when predictors are similar; residual uncertainty reduces when the model set is complete; and learning reduces this uncertainty. For the macro series we find that incompleteness of the models is relatively large in the 1970’s, the beginning of the 1980’s and during the recent financial crisis, and lower during the Great Moderation; the predicted probabilities of recession accurately compare with the NBER business cycle dating; model weights have substantial uncertainty attached. With respect to returns of the S&P 500 series, we find that an investment strategy using a combination of predictions from professional forecasters and from a white noise model puts more weight on the white noise model in the beginning of the 1990’s and switches to giving more weight to the professional forecasts over time. Information on the complete predictive distribution and not just on some moments turns out to be very important, above all during turbulent times such as the recent financial crisis. More generally, the proposed distributional state space representation offers great flexibility in combining densities.     

Is social protection a necessity or a luxury good? New multivariate cointegration panel data results

The aim of this paper is to test the claim that social protection is a luxury good. Therefore, GDP elasticity of selected social protection expenditure is estimated using a new econometric approach developed first by Kao and Chiang (Advances in Econometrics, 15, 179–222, 2000). Time series properties of selected social expenditure in 18 OECD countries from 1981 to 1998 are examined. Using panel data cointegration tests and OLS, FMOLS and DOLS estimators, results were found which differ from previous analyses reporting substantially higher income elasticities. With the FMOLS, selected social expenditure has income elasticities smaller than one but greater than one with the DOLS. It is noteworthy that whether selected social expenditure is stationary or nonstationary may have critical implications for researchers and policy makers desiring to model and explain the impact of this expenditure on a country economic system.     

Development and validation of the Remote Working Benefits & Disadvantages scale

The changes that are constantly occurring in the labour sector have led organisations and companies to move towards digital transformation. This process was accelerated by the COVID-19 pandemic and  conducted to a massive recourse to the practice of remote working, which in this study is understood as the term for the way of performing work outside the usual workplace and with the support of ICT. Currently, there are no flexible scales in the literature that allow measuring the benefits and disadvantages of remote working with a single instrument. Thus, the distinction between the positive and negative consequences of working remotely, substantiated by a solid literature, provides a framework for a systematical understanding of the issue. The aim of the present study is to develop and validate a scale on remote working benefits and disadvantages (RW-B&D scale). For this end, a preliminary Exploratory Factor Analysis (EFA) with 304 participants, a tailored EFA with a sample of 301 workers and a Confirmatory Factor Analysis (CFA) with 677 workers were conducted. Participants were all Italian employees who worked remotely during the period of the COVID-19 health emergency. Data were collected between October 2020 and April 2021. The psychometric robustness of the model was assessed through bootstrap validation (5000 resamples), fit indices testing and measurement of factorial invariance. The statistical analyses demonstrated the bifactorial nature of the scale, supporting the research hypothesis. The model showed good fit indices, bootstrap validation reported statistically significant saturations, good reliability indices, and convergent and discriminant validity. Measurement invariance was tested for gender and organisational sector. The results suggested that the novel scale facilitates the quantitative measurement of the benefits and disadvantages associated with remote working in empirical terms. For this reason, it could be a streamlined and psychometrically valid instrument to identify the potential difficulties arising from remote working and, at the same time, the positive aspects that can be implemented to improve organisational well-being.