Ranking local authorities: The Lilley league tables

Public sector managers hear the slogan ‘do it like the private sector’ a lot, but the private sector actually functions in complex and differentiated ways. To get around uncertainty people use markets in ways that do not fit the stereotype. Public sector managers may learn from the way in which markets actually operate, but should ignore the slogans.     

The measurement of turnaround management strategies in local authorities

The use of turnaround management strategies (TMS) in the public context is beginning to be researched, however adjusted measurement tools are still needed. This article describes the construction and validation of a new scale for measuring TMS in local authorities.     

Management accounting,performance measurement and strategy in English local authorities

This paper analyses the results of an extensive survey of UK local government which explored the relationship between strategy, management accounting practices (MAPs), and performance measurement techniques (PMTs). The research investigated a resource-based view of strategic capabilities and Porter’s strategic typologies. PMTs and MAPs were shown to be associated with strategic capabilities. Strategic typologies, however, were found to be only weakly associated with the use of PMTs and MAPs.     

Audit fees and audit adjustments: evidence from Welsh local authorities

This paper exploits the availability of pre-audit financial statements to investigate the scale and incidence of audit adjustments and their impact on audit fees in Welsh local authorities. Adjustments to the politically-sensitive general fund, which represent a significant proportion of all adjustments, are associated with increased audit fees. The authors also found that audit adjustments on average result in more conservative reporting of the surplus/deficit and the balance on the general fund, with the number and value of downward adjustments exceeding those of upward movements.     

Risk management in local authorities: An application of Schatzki's social site ontology

Prior research has devoted limited attention to studying changes in organisational risk management (RM) practices. This is despite continuous dissatisfaction from academics and practitioners with organisations' ability to manage risks. We draw on Schatzki's social site ontology to study RM practices of two New Zealand local authorities that both experienced (earthquake) risk events and whose RM practices could be expected to change. We extend recent research utilising Schatzki, by finding that practical intelligibility and general understanding mutually affect each other in the organising of practices. Further, we extend Nama and Lowe’s (2014) addition to Schatzki by highlighting the importance of including teleological structures and accounting devices into the mutually constitutive relationship between general understanding and affectivity. Finally, we contribute to RM literature by proposing that changing the general understanding (in addition to the mere implementation of RM tools) is an important way of making RM change fundamental and sustainable.     

Department of education and local authorities: Breaking the partnership in order to survive

Propaganda to the contrary notwithstanding, schools have been saved from the worst of the expenditure cuts. But the price has been high, and the education service may never be quite the same again.     

Model-based earnings forecasts vs. financial analysts' earnings forecasts

Existing accounting-based forecasting models of earnings either do not fully consider information that is contained in stock prices or use an ad hoc specification that is not based on rigorous valuation theory. In this paper, we develop an earnings forecasting model built on the theoretical linkages between future earnings and stock prices as well as a number of accounting fundamental variables. We find that our model-based forecasts of earnings are in general less biased and more accurate than both existing model-based forecasts and analysts' consensus forecasts, at both shorter and longer horizons. We also show that the accuracy of both model-based forecasts and financial analysts' forecasts depend on firm-specific characteristics such as firm size and industry membership.     

Accurate forecasts attract clients; Biased forecasts keep them happy

We examine whether business relationships between mutual funds and sell-side analysts influence earnings forecasts using Chinese data from 2007 to 2019. Consistent with prior studies, our results support the commission pressure hypothesis. Analysts' forecasts are overly optimistic for the holdings of existing fund clients. Significantly, we propose the potential client hypothesis and show that analysts' forecasts are more accurate for the holdings of funds that are not clients than for holdings of clients or for stocks not held by any fund. Our results suggest that commission pressure from existing fund clients increases analysts' optimistic bias, while potential clients pressure inhibits analysts' optimistic bias to some extent. Finally, our evidence supports the conflicts of interest hypothesis. Commission pressure is reduced as economic uncertainty grows.     

Skewed exchange-rate forecasts

We used survey data on exchange-rate forecasts of the dollar/euro exchange rate and the yen/dollar exchange rate to analyze the correlation of the skewness of the distribution of heterogeneous forecasts with movements of the exchange rate. Using various measures of skewness, we found a negative correlation of skewness of 1-month-ahead forecasts with exchange-rate movements. In contrast, the correlation of skewness of 12-months-ahead forecast with exchange-rate movements is positive. The negative correlation arising in the case of 1-month-ahead forecasts is consistent with expected mean reversion in exchange rates. The positive correlation arising in the case of longer term forecasts, in turn, is consistent with longer term bandwagon effects.     

Understanding analysts forecasts

The purpose of this paper is to model analysts’ forecasts. The paper differs from the previous research in that we do not focus on how accurate these predictions may be. Accuracy may indeed be an important quality but we argue instead that another equally important aspect of the analysts’ job is to predict and describe the impact of jump events. In effect, the analysts’ role is one of scenario prediction. Using a Bayesian-inspired generalised method of moments estimation procedure, we use this notion of scenario prediction combined with the structure of the Morgan Stanley analysts’ forecasting database to model normal (base), optimistic (bull) and pessimistic (bear) forecast scenarios for a set of reports from Asia (excluding Japan) for 2007–2008. Since the estimation procedure is unique to this paper, a rigorous derivation of the asymptotic properties of the resulting estimator is also provided.     

Selecting efficient forecasts

The purpose of this enquiry into the nature of efficient forecasts is to identify the choice process utilised by the analyst and/or forecasting agency when selecting one particular forecast rather than any other. Two properties of a forecast, reliability and precision, are treated as joint products of r and d expenditures. Rational planners will select forecasts which maximise their preferences subject to the r and d budget. Thus, the selection of an optimum forecast can be treated as a constrained maximisation problem.     

Time-varying forecasts by variational approximation of sequential Bayesian inference

Methods developed for making time-varying forecasts in economic and financial analysis include (a) equal-weighted moving-window (or rolling) regression, (b) time-weighted (e.g. exponentially weighted) regression, (c) the Kalman filter (KF) and (d) adaptive Kalman filters. This paper developed a new method based on variational approximation of sequential Bayesian inference (VASB). Concepts and notions of the sequential Bayesian analysis and the variational approximation of an intractable posterior are simple and straightforward. Our VASB algorithm is not complicated and is easy to code. For a regression on multiple time-series, the regression coefficients, standard errors, prediction and residual error are time-varying and are estimated jointly at every time step. For a single time-series (e.g. price returns of an asset), its mean and variance are time-varying and are predicted jointly at every time step. The VASB algorithm performs better than the rolling and time-weighted statistics or regressions and the KF in terms of higher predictive power and stronger robustness. Derivations of the VASB algorithm are presented in the appendices.     

Quantile forecasts of daily exchange rate returns from forecasts of realized volatility

Quantile forecasts are central to risk management decisions because of the widespread use of Value-at-Risk. A quantile forecast is the product of two factors: the model used to forecast volatility, and the method of computing quantiles from the volatility forecasts. In this paper we calculate and evaluate quantile forecasts of the daily exchange rate returns of five currencies. The forecasting models that have been used in recent analyses of the predictability of daily realized volatility permit a comparison of the predictive power of different measures of intraday variation and intraday returns in forecasting exchange rate variability. The methods of computing quantile forecasts include making distributional assumptions for future daily returns as well as using the empirical distribution of predicted standardized returns with both rolling and recursive samples. Our main findings are that the Heterogenous Autoregressive model provides more accurate volatility and quantile forecasts for currencies which experience shifts in volatility, such as the Canadian dollar, and that the use of the empirical distribution to calculate quantiles can improve forecasts when there are shifts.     

Management earnings forecasts and analyst forecasts:Evidence from mandatory disclosure system

Distinct from the literature on the effects that management earnings forecasts(MEFs) properties, such as point, range and qualitative estimations, have on analy...     

Neural network volatility forecasts

We analyse whether the use of neural networks can improve ‘traditional’ volatility forecasts from time-series models, as well as implied volatilities obtained from options on futures on the Spanish stock market index, the IBEX-35. One of our main contributions is to explore the predictive ability of neural networks that incorporate both implied volatility information and historical time-series information. Our results show that the general regression neural network forecasts improve the information content of implied volatilities and enhance the predictive ability of the models. Our analysis is also consistent with the results from prior research studies showing that implied volatility is an unbiased forecast of future volatility and that time-series models have lower explanatory power than implied volatility. Copyright © 2008 John Wiley & Sons, Ltd.     

Economically significant stock market forecasts

It has been known for some time that a small, but statistically significant portion of the monthly variation in excess returns on the S&P 500-stock index is predictable using ex ante information. This paper presents a model whose out-of-sample forecasts have economic significance. Specifically, a switching rule conditioned on out-of-sample forecasts of stock excess returns, produces investment outcomes that mean-variance dominate the buy-and-hold. The switching strategy yields superior risk-adjusted returns as judged by the composite performance measures of Treynor, Sharpe, and Jensen.     

Earnings forecasts and idiosyncratic volatilities

We test the theoretical relation between idiosyncratic return volatilities and the volatilities of cash-flow news based on the expected returns on equity (ROE) for CRSP stocks over the period 1977–2008. Consistent with economic intuition, we find that using analyst forecasts of earnings is superior to using realized earnings to proxy for market expectations about future cash flow news. Our findings are consistent with a market where stock return volatilities are positively and asymmetrically related to changes in the volatilities of expectations for a fundamental driver of cash flow news (ROE). Our findings are robust after correcting for forecast biases, various fundamental variables, newly-listed and mature firms, and periods with and without earnings announcements.     

A comparison of inflation forecasts

Interest rate models provide slightly better monthly forecasts and substantially better eight- and fourteen-month forecasts of inflation than a univariate time series model. The Livingston surveys underestimate eight- and fourteen-month inflation rates, especially during the high inflation period of 1978–81. In contrast, eight- and fourteen-month inflation forecasts extrapolated from one-month interest rates show little bias and track ex post eight- and fourteen-month inflation rates better than the survey forecasts.