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This paper aims to extend the research into company financial forecasts by modelling naïve earnings forecasts derived from normalised historic accounting data disclosed during Australian initial public offerings (IPOs). It seeks to investigate naïve forecast errors and compare them against their management forecast counterparts. It also seeks to investigate determinants of differential error behaviour.

IPOs were sampled and their prospectus forecasts, historic financial data and subsequent actual financial performance were analysed. Directional and absolute forecast error behaviour was analysed using univariate and multivariate techniques.

Systematic factors associated with error behaviour were observed across the management forecasts and the naïve forecasts, the most notable being audit quality. In certain circumstances, the naïve forecasts performed at least as well as management forecasts. In particular, forecast interval was an important discriminator for accuracy, with the superiority of management forecasts only observed for shorter forecast intervals.

The results imply a level of “disclosure management” regarding company IPO forecasts and normalised historic accounting data, with forecast overestimation and error size more extreme in the absence of higher quality third‐party monitoring services via the audit process. The results also raise questions regarding the serviceability of normalised historic financial information disclosed in prospectuses, in that many of those data do not appear to enhance the forecasting process, particularly when accompanied by published management forecasts and shorter forecast intervals.

This paper aims to provide an investigation into whether financial analysts' forecast accuracy differs between the pre‐ and post‐adoption of the international financial reporting standards (IFRS) in the Asia‐Pacific region, namely, for the countries of Australia, Hong Kong and New Zealand. In particular, this study seeks to examine whether the treatment of intangibles capitalized in the post‐IFRS period have positively aided analysts in forecasting future earnings of a firm.

Panel data analysis is applied over a period from 2001 to 2008.

Evidence is found to show intangibles capitalized under the new recognition and measurement rules of IFRS are negatively associated with analysts' earnings forecast errors. The results are robust to several model specifications across each of the countries, suggesting that the adoption of IFRS may indeed provide more value‐relevant information in financial statements for the users of financial reports.

This paper analyzed whether the adoption of IFRS has led to any changes in the accuracy of earnings forecasts. The results will be of help to analysts' earnings forecast activity and those with interest in the subject.

Sees the objective of teaching financial management to be to help managers and potential managers to make sensible investment and financing decisions. Acknowledges that financial theory teaches that investment and financing decisions should be based on cash flow and risk. Provides information on payback period; return on capital employed, earnings per share effect, working capital, profit planning, standard costing, financial statement planning and ratio analysis. Seeks to combine the practical rules of thumb of the traditionalists with the ideas of the financial theorists to form a balanced approach to practical financial management for MBA students, financial managers and undergraduates.

The purpose of this paper is to investigate the association between corporate governance mechanisms and quality of forward-looking information in the Chinese stock market which presents a mandatory disclosure environment for forward-looking information.

Using sales forecasts to proxy forward-looking information and using precision and accuracy to measure the quality of information disclosure, the authors investigate the impact of corporate governance attributes on the precision and accuracy of sales forecasts made by listed Chinese firms in their 2010 annual reports, using logistics and ordinary least squares regressions.

The authors find good corporate governance has a positive and significant impact on the precision choice of sales forecasts disclosure. Firms with good corporate governance are more likely to disclose more precise sales forecasts than providing qualitative discussions on firms’ sales trend. In addition, good corporate governed firms are found more likely to provide precise non-financial information. The authors also find that good corporate governance is positively associated with making more conservative sales forecasts disclosure. However, the authors find no significant relationship between good corporate governance and smaller forecast error.

The study makes significant contributions to corporate disclosure literature. The authors investigate the determinants of the quality of forward-looking information in a mandatory disclosure regime while most forward-looking information disclosure literature have been conducted in a voluntary-based disclosure environment. The authors examine whether in a mandatory disclosure regime, corporate governance mechanisms can play a positive role in precision choices and accuracy of forward-looking information. Further, the study is the first to examine corporate governance and the quality of non-financial forward-looking information (sales target and production goal). The research findings therefore extend forward-looking information disclosure research from financial information to non-financial information.

The empirical findings will provide regulators with evidence on the quality of forward-looking information in a mandatory disclosure regime and the influence of corporate governance on forward-looking disclosure. The properties of forward-looking information disclosure in China should be of interest to policy makers, investors and financial analysts in other international jurisdictions.

The study investigates forward-looking information in a mandatory disclosure regime while most extant forward-looking information studies have been conducted in a voluntary disclosure environment. The study is the first to examine the quality of non-financial forward-looking information such as operational goals and plans, and to investigate the association between the quality of non-financial forward-looking information and corporate governance mechanisms. The research findings extend forward-looking information disclosure research from quantitative financial information to quantitative non-financial information.

The purpose of this paper is to apply alternative GARCH‐type models to daily volatility forecasting, and apply Value‐at‐Risk (VaR) to the Taiwanese stock index futures markets that suffered most from the global financial tsunami that occurred during 2008.

Rather than using squared returns as a proxy for true volatility, this study adopts three range‐based proxies (PK, GK and RS), and one return‐based proxy (realized volatility), for use in the empirical exercise. The forecast evaluation is conducted using various proxy measures based on both symmetric and asymmetric loss functions, while back‐testing and two utility‐based loss functions are employed for further VaR assessment with respect to risk management practice.

Empirical results demonstrate that the EGARCH model provides the most accurate daily volatility forecasts, while the performances of the standard GARCH model and the GARCH models with highly persistent and long‐memory characteristics are relatively poor. In the area of risk management, the RV‐VaR model tends to underestimate VaR and has been rejected owing to a lack of correct unconditional coverage. In contrast, the GARCH genre of models can provide satisfactory and reliable daily VaR forecasts.

The unobservable volatility can be proxied using parsimonious daily price range with freely available prices when applied to Taiwanese futures markets. Meanwhile, the GARCH‐type models remain valid downside risk measures for both regulators and firms in the face of a turbulent market.

Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.

This paper aims to provide a chronological review of changes in the institutional setting regulating Australian initial public offering (IPO) firms’ earnings forecasts over the period from 1994 to 2012. The changing forecasting environment covers both IPO firms’ prospectus earnings forecasts and post-listing updates to those forecasts.

This historical analysis reviews the changes in corporate regulation and enforcement, Australian Securities Exchange listing requirements and the outcomes of securities class actions (SCA) that affect IPO firms’ earnings forecasts.

A review of the institutional setting regulating Australian IPO firms’ earnings forecasts reveals two inter-temporal shifts in (increasing) litigation risk over 1994-2012 period which have arisen from more onerous regulations, stronger regulatory enforcement and a more active SCA market. The authors document the corporate responses to those shifts.

This is the first study to comprehensively document research of an inter-temporal litigation risk shift on IPO firms’ earnings forecasting behaviour. It therefore provides a formative base and a useful resource for researchers, practitioners and investigators (regulators, forensic accountants, etc.) when examining the impact of the changes on IPO firms’ forecasting behaviour following regulatory change and enforcement.

This paper looks at the importance of forecasted information as a key input to investors decision models. The research design uses accounting variables suggested by financial accounting theory, industry variables and economic variables. Analysis of the data indicated that only return on investment yield and capital intensity were associated with earnings. In an environment of rapidly changing economic conditions and attendant uncertainty, the scrutiny of forecast accuracy has is crucial. For a firm, the allocation of resources is based upon forecasts of financial information that may affect its survival. Earnings and dividends forecast, and the growth rates in these forecast are key informational inputs in investor decision models (Chang and Most, 1980). In addition, Securities and Exchange Commission, recognizing the importance of this subjective and non‐verifiable information, permits and encourages firms to include financial forecasts in their annual reports by granting them a “safe harbor”. The accounting profession responded to this demand for forecasted information by producing audit guidelines for these forecasts. Accounting‐based financial forecasts are used in a variety of ways. Banks and non bank financial institutions use forecasts to evaluate credit. Earnings forecasts are useful to financial analysts who attempt to isolate a firm's intrinsic characteristics such as residual income after removing the effect of economy and industry conditions through the use of index models. Auditors use accounting forecasts as a basis for expectations concerning reported items to determine the extent of detailed tests (Stringer, 1975; Kinney, 1978). Lev (1980, p.525) stated that the “… crucial stage of the analytical review process is the generation of expected, or reasonable, values of financial statement items.” Managers use internally generated earnings forecasts for resource allocation decisions concerning present operations as well as future operations and expansions/contractions.

The purpose of this study is to investigate the volatility and forecast accuracy of the Islamic stock market for the period 1999–2017. This period is characterized by the occurrence of several economic and political events such as the September 11, 2001, terrorist attack and the 2007–2008 global financial crisis.

This study constructs a new hybrid generalized autoregressive conditional heteroskedasticity (GARCH)-type model based on an artificial neural network (ANN). This model is applied to the daily Dow Jones Islamic Market World Index during the period June 1999–January 2017.

The in-sample results show that the volatility of the Islamic stock market can be better described by the fractionally integrated asymmetric power ARCH (FIAPARCH) approach that takes into account asymmetry and long memory features. Considering the out-of-sample analysis, this paper has applied a hybrid forecasting model, which combines the FIAPARCH approach and the ANN. Empirical results reveal that the proposed hybrid model (FIAPARCH-ANN) outperforms all other single models such as GARCH, fractional integrated GARCH and FIAPARCH in terms of all performance criteria used in the study.

The results have some implications for Islamic investors, portfolio managers and policymakers. These implications are related to the optimal portfolio diversification decision, the hedging strategy choice and the risk management analysis.

The paper develops a new framework that combines an ANN and FIAPARCH model that introduces two important features of time series, namely, asymmetry and long memory.

The purpose of this paper is to shed light on the nature of the work that financial analysts actually do in the context of the market for information and to further open up research in this area to qualitative and sociological inquiry.

A field study with 49 financial analysts (both buy-side and sell-side) was undertaken in order to understand the work that they actually do. This field study was theoretically informed by the sociology of Pierre Bourdieu.

The authors find, in contrast to both conventional wisdom and assumptions in prior (mostly quantitative) literature, that the primary value of sell-side analyst work lies not in the recommendations that analysts ultimately produce, but in the rich contextual information that they provide to buy-side analysts. In order to successfully provide this information, analysts have to embody large amounts of technical capital into their habitus.

Much research in this area erroneously presumes that forecasting is the primary function of analysts. Analyst work needs to be understood as multifarious and requiring a well-developed habitus that is attuned to the accumulation of both technical and social capital. Future qualitative research might usefully explore in more detail the way in which corporate managers interact with analysts. The present study solicits the viewpoints only of the analysts themselves. The organisational context of the analysts was not explored in detail and the interviews were pre-crisis, which possibly explains why the technical capital of sell-side analysts was extolled by interviewees rather than lambasted.

The paper is one of few studies to look at analysts from a qualitative and sociological perspective. It both complements and extends both emerging sociological work on financial intermediaries and qualitative work on the “market for information”.