The symbolic politics of delegation: macroprudential policy and independent regulatory authorities

This paper investigates the motivations that led policy-makers to delegate macroprudential authorities to newly created independent systemic regulatory authorities (SRAs). Three case studies are examined: the US Financial Stability Oversight Council, the European Systemic Risk Board and the UK’s Financial Policy Committee. Policy-makers’ motivations are captured by examining the specific institutional features of the newly created SRAs and by tracing the legislative debates that surrounded their creation. The findings of this empirical analysis call into question several of the conventional claims that are used to justify delegation to technocratic agencies from the functionalist and ideational scholarship. Given the limitations of the explanations based on efficiency considerations and socialisation of welfare losses, this paper suggests that the delegation of powers to SRAs was ultimately motivated by what is referred to as the ‘logic of symbolic politics.’ It is argued that the main motivation that emerges from the legislative debates for delegating this important task is that the SRAs provided a quick institutional ‘fix’ to signal to the public that in the wake of the international crisis of 2007–2009, policy-makers were redressing regulatory mistakes made prior to and during the crisis that had caused a severe deterioration of public’s wealth.     

Fair value accounting for liabilities: The role of disclosures in unraveling the counterintuitive income statement effect from credit risk changes

When liabilities are accounted for at fair value, a deterioration of a company’s credit risk results in the reporting of an income statement gain; an improvement in a company’s credit risk results in a loss. Many argue that these income statement effects are counterintuitive and that financial statement-users are likely to misinterpret fair value gains as positive signals and fair value losses as negative signals. Utilizing an experiment with CPAs as participants, we find that these arguments are indeed valid. Specifically, we find that over 70% of the participants incorrectly assess a company’s credit risk as improving (deteriorating) when a fair value gain (loss) is recognized. We also find that disclosures that explicitly specify the relation between the direction of the credit risk change and the income statement effect significantly reduce participants’ misinterpretations, and are more beneficial when fair value gains versus losses are recognized. These findings provide empirical evidence in the debate over the recognition of company-specific credit risk changes and offer direction for improving disclosures in the area of fair value accounting.     

The Effect of Measurement Subjectivity Classifications on Analysts' Use of Persistence Classifications When Forecasting Earnings Items

Earnings items are typically classified in financial reports based on their persistence and measurement subjectivity. Archival research examines investors' use of persistence and measurement subjectivity classifications for forecasting and valuation. However, this research typically examines only one of these classifications at a time and ignores the potential interactive implications of an earnings item's persistence and measurement subjectivity classifications. We recruited experienced financial analysts to participate in two experiments that examined the effect of measurement subjectivity classifications on analysts' use of persistence classifications when forecasting earnings items. We find that analysts rely less on an earnings item's persistence classification when measurement subjectivity is high relative to when measurement subjectivity is low. We also find that presentation format affects analysts' use of these two classifications. Specifically, we find that the matrix format (i.e., rows display persistence classifications and columns display measurement subjectivity classifications) facilitates analysts' combined use of persistence and measurement subjectivity classifications relative to the sequential format (i.e., the classifications are displayed separately). These findings suggest that archival research could improve its examination of market participants' use of earnings classifications for forecasting and valuation by recognizing that the implications of an earnings item's persistence classification can vary according to the item's measurement subjectivity classification. By also demonstrating how presentation format affects analysts' use of earnings classifications, our study provides further insights into this fundamental issue in accounting research and standard setting.     

Information Asymmetry Around Earnings Announcements

This study examines bid-ask spreads to determine how the anticipation and release of earnings announcements affect information asymmetry in the stock market. I use regression analysis and find that bid-ask spreads are negatively related to public information availability and positively related to earnings variability and the market reaction to prior unexpected earnings. The results suggest that firms for which earnings is expected to yield a relatively larger stock market reaction have greater information asymmetry than firms for which earnings are expected to yield a smaller market reaction.I also find that bid-ask spreads gradually increase in the four days prior to earnings announcements, and increase sharply the day prior to, the day of and the day after the earnings announcements. Bid-ask spreads seven to ten days after earnings announcements are not significantly different from bid-ask spreads seven to ten days prior to earnings announcements.     

The impact of faking on Cronbach’s alpha for dichotomous and ordered rating scores

In many psychological inventories (i.e., personnel selection surveys and diagnostic tests) the collected samples often include fraudulent records. This confronts the researcher with the crucial problem of biases yielded by the usage of standard statistical models. In this paper we applied a recent probabilistic perturbation procedure, called sample generation by replacement (SGR)—(Lombardi and Pastore, Multivar. Behav. Res 47:519–546, 2012), to study the sensitivity of Cronbach’s alpha index to fake perturbations in dichotomous and ordered data, respectively. We used SGR to perform two distinct SGR simulation studies involving two sample size conditions, three item set sizes, and twenty levels of faking perturbations. Moreover, in the second SGR simulation study we also evaluated an additional factor, type of faking model, to study sample reliability under different modulations of graded faking (uniform faking, average faking, slight faking, and extreme faking). To simulate these more complex faking models we proposed a novel extension of the SGR perturbation procedure based on a discrete version of the generalized beta density distribution. We also applied the new procedure to real behavioral data on emotional instability.     

Specialized markets and Chinese industrial clusters: The experience of Zhejiang Province

Local specialized markets and industrial clusters have been a driving force in China's transition from a rural to a market economy, as they have constituted a means for small local producers to access markets since the reform and the launching of the “open door” policies. In this article the industrial development of local industrial clusters endowed with specialized markets is outlined on the basis of fieldwork case studies in Zhejiang Province. In this area of China, tight linkages between the development of industrial clusters and specialized markets are based on social and economic foundations that have propelled the growth of market relations since the late 1970s. Together with local traditions and skills in both handcraft production and commerce, specialized markets accompanied the take-off of industrial clusters following the so-called “Zhejiang model” of development. The role of specialized markets is still crucial nowadays as a powerful channel for the distribution of consumer goods as well as a means of coordination of fragmented productions in China and abroad.     

A theoretical and empirical investigation of job satisfaction and intended turnover in the large CPA firm

This research reports on the development and test of a model of job satisfaction and intended turnover in the large CPA firm environment. On an overall basis, the model was generally supported. Satisfaction showed strong significance in a multiple regression equation predicting the probability of turnover. In addition, eleven dimensions of job satisfaction developed through factor analysis, as well as a “career preparation” variable, correlated positively with a measure of overall job satisfaction and negatively with a measure of the probability of turnover.     

Information friction and investor home bias: A perspective on the effect of global IFRS adoption on the extent of equity home bias

This paper provides a perspective on the effect of IFRS adoption on the tendency of investors to under-invest in foreign equities. We consider explanations for the equity home bias described in prior research and discuss research relevant to the informational consequences of global adoption of IFRS. Specifically, we evaluate whether IFRS adoption reduces information processing costs or decreases investor uncertainty about either the quality of financial reporting or the distribution of future cash flows. We predict that the effect of any reduction in information processing costs from the adoption of IFRS is likely to be small relative to the effects of other determinants of home bias such as the strength of investor protection mechanisms in foreign countries, behavioral biases toward familiar equities, and informational advantages related to geographical proximity. We argue that the quality of the information that investors have (or perceive they have) decreases with distance, conclude that global IFRS adoption is unlikely to affect home bias, and propose avenues for future research.     

Bayesian prior elicitation in DSGE models: Macro- vs micropriors

Bayesian approaches to the estimation of DSGE models are becoming increasingly popular. Prior knowledge is normally formalized either directly on deep parameters' values (‘microprior’) or indirectly, on macroeconomic indicators, e.g. moments of observable variables (‘macroprior’). We introduce a non-parametric macroprior which is elicited from impulse response functions and assess its performance in shaping posterior estimates. We find that using a macroprior can lead to substantially different posterior estimates. We probe into the details of our result, showing that model misspecification is likely to be responsible of that. In addition, we assess to what extent the use of macropriors is impaired by the need of calibrating some hyperparameters.