The value relevance of effective investor relations

In this study, we test formally the market value of investor relations (IR) activity employing the annual Investor Relations Magazine Best Overall IR Awards data from 2000 to 2002 to proxy for the quality of firm investor relations. We find firms perceived by survey respondents to have effective IR strategies have significantly higher market value, and, also, earn superior abnormal returns, both before and after the award nominations. We also find that, not surprisingly, higher analyst following is associated with more nominations, suggesting analysts tend to favor the stocks they follow, although being nominated for best overall IR is also consistent with a significant increase in analyst following in the following year. Finally, in line with effective IR leading to lower information risk, liquidity of nominated firms, measured by stock turnover, increases in the year subsequent to the award nominations. Overall, our evidence is consistent with good IR successfully reducing the risk to investors associated with high information asymmetry, as predicted by information risk and agency theories

Do enhanced derivative disclosures work? An informational perspective

Firms use derivatives both for hedging and nonhedging purposes. The Statement of Financial Accounting Standards No. 161 (SFAS 161) requires firms to disclose the purposes of their derivatives usage, thereby helping investors to evaluate the effects of derivatives usage on firm performance. Using a hand-collected sample of US listed firms and a difference-in-differences research design, we find that, compared with nonderivative-users, derivative-users compliant with SFAS 161 experience a significantly greater reduction in stock illiquidity and the probability of informed trading in the post-SFAS 161 period, and such impact is evident only for firms with a high degree of investor attention

A panel analysis of UK industrial company failure

We examine the failure determinants for large quoted UK industrials using a panel data set comprising 539 firms observed over the period 1988-93. The empirical design employs data from company accounts and is based on Chamberlain’s conditional binomial logit model, which allows for unobservable, firm-specific, time-invariant factors associated with failure risk. We find a noticeable degree of heterogeneity across the sample companies. Our panel results show that, after controlling for unobservables, lower liquidity measured by the quick assets ratio, slower turnover proxied by the ratio of debtors turnover, and profitability were linked to the higher risk of insolvency in the analysis period. The findings appear to support the proposition that the current cash-flow considerations, rather than the future prospects of the firm, determined company failures over the 1990s recession

The dynamic model of enterprise revenue management

The article presents the dynamic model of enterprise revenue management. This model is based on the quadratic criterion and linear control law. The model is founded on multiple regression that links revenues with the financial performance of the enterprise. As a result, optimal management is obtained so as to provide the given enterprise revenue, namely, the values of financial indicators that ensure the planned profit of the organization are acquired

Measuring efficiency and productivity in professional football teams: Evidence from the English Premier League

Professional football clubs are unusual businesses, their performance judged on and off the field of play. This study is concerned with measuring the efficiency of clubs in the English Premier League. Information from clubs’ financial statements is used as a measure of corporate performance. To measure changes in efficiency and productivity the Malmquist non-parametric technique has been used. This is derived from the Data Envelopment Analysis (DEA) linear programming approach, with Canonical Correlation Analysis (CCA) being used to ensure the cohesion of the input-output variables. The study concludes that while clubs operate close to efficient levels for the assessed models, there is limited technological advance in their performance in terms of the displacement of the technological frontier

CEO Profile and Earnings Quality

This paper introduces the PSCORE, which aggregates nine personal characteristics of chief executive officers (CEOs), to signal the quality of earnings. The PSCORE is a composite score based on publicly available data on CEOs. The study reports strong positive relationships between the PSCORE and two different proxies for earnings quality, (i) discretionary accruals and (ii) financial statement errors, measured by deviations of the first digits of figures reported in financial statements from those expected by Benford’s Law. Further analyses indicate that the relationships between the PSCORE and the proxies for earnings quality become more pronounced when CEOs have high equity-based compensation incentives. The findings have some implications for practitioners

Multi-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributions

YesAlthough many modelling and prediction frameworks for corporate bankruptcy and distress have been proposed, the relative performance evaluation of prediction models is criticised due to the assessment exercise using a single measure of one criterion at a time, which leads to reporting conflicting results. Mousavi et al. (Int Rev Financ Anal 42:64–75, 2015) proposed an orientation-free super-efficiency DEA-based framework to overcome this methodological issue. However, within a super-efficiency DEA framework, the reference benchmark changes from one prediction model evaluation to another, which in some contexts might be viewed as “unfair” benchmarking. In this paper, we overcome this issue by proposing a slacks-based context-dependent DEA (SBM-CDEA) framework to evaluate competing distress prediction models. In addition, we propose a hybrid crossbenchmarking- cross-efficiency framework as an alternative methodology for ranking DMUs that are heterogeneous. Furthermore, using data on UK firms listed on London Stock Exchange, we perform a comprehensive comparative analysis of the most popular corporate distress prediction models; namely, statistical models, under both mono criterion and multiple criteria frameworks considering several performance measures. Also, we propose new statistical models using macroeconomic indicators as drivers of distress