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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.

This study examines the accuracy and bias of profit forecasts disclosed in prospectuses by New Zealand companies for initial public offerings during the period 1987 to 1994. The results show that profit forecasts in this period are, on average, more accurate titan those disclosed prior to 1987, which were examined in prior studies. However, the results reject the null hypothesis that profit forecasts are accurate. In examining forecast bias, the evidence shows that the forecasts are, on average, somewhat pessimistic, but not sufficiently to reject the hypothesis that profit forecasts are unbiased. Tests of the determinants of error show that larger companies make more accurate forecasts, and forecasts made in the year 1987 are less accurate than in other years. Tests of the determinants of bias show that forecasts made in 1987 are also more optimistic, and that companies with longer trading histories and pessimistic forecasts make less biased forecasts. Forecast period and industry type are not significantly related to error or bias.

This study analyzes trends in analyst forecast properties from 1987 through 1998 in the United States and seven Pacific Rim countries: Australia, New Zealand, Taiwan, Hong Kong, Japan, South Korea, and Thailand. Analyst forecast properties in the United States have become less dispersed, more accurate, and less optimistic during the sample period. Similar trends exist in Australia and New Zealand, but not in the other sample countries. In contrast, the forecast property trends of most Asian countries are the exact opposite. For example, in Japan and Korea, forecast dispersion, forecast error, and optimism all significantly increase over the sample period. The results suggest that Asian firms do not play the U.S.‐style earnings game in which managers guide analysts toward a certain target number and then report earnings that beat the target.

In this chapter, inventory and sales data from a small business with seven showrooms are evaluated to forecast future sales and maximize total profits. In each showroom, three major brands of ceiling fans are sold and a limited amount of products from each brand are displayed. Each showroom varies in their sales volume, display capacity, and profit margins. Using historical data, the optimal display configuration was determined for each showroom; that is, the proportion of products from each brand to display in the limited display grid, while acknowledging existing constraints. Next using the optimal displays, profit for the next year is forecasted. Finally a comparison is made between actual and forecasted results and profits pre and post the optimal product display.

Profit forecasts are rarely disclosed in the UK except in prospectuses, circulars and during takeover bids. There are few regulations governing the content of profit forecasts. Under stock exchange rules these forecasts must be reported on by both reporting accountants and the merchant bankers advising on the deal. The format of the forecasts is at the discretion of individual companies. This paper summarises the regulations, including professional pronouncements, governing accountants’ reports on profit forecasts. Practical examples of such accountants’ reports extracted from 250 profit forecasts published during 701 UK takeover bids in the period 1988 to 1992 are reproduced and discussed. These examples provide useful precedent material for practitioners involved in reporting on a profit forecast. The paper concludes with a discussion of policy issues and suggestions for policy makers.

Changes in Australian segment reporting standards over the last decade changed the required disaggregation of segment information. The purpose of this paper is to investigate whether increased disaggregation has implications for users' confidence in decisions based on segment reports and perceptions of segment reporting usefulness.

Using an experiment based on the differences between the original AASB 1005 and the more detailed requirements of AASB 114, the authors test whether segment report users' confidence in forecasting and their perceptions of segment report usefulness differ between the different information sets provided under these standards.

It was found that the more disaggregated or finer reports based on AASB 114 provide significantly more confidence to users, compared to the coarser segment reports based on the original AASB 1005, but this is not associated with differences in segment report usefulness scores.

The authors' experiment is based on AASB 1005 and AASB 114 and the results cannot be generalized to differences with other reporting standards. Examination of differences in recently released AASB 8 may reveal different implications for users' confidence and perceptions of usefulness. More generally, other tests of usefulness are needed to confirm whether opinions of usefulness that are not confirmed by decision‐making practices provide a reliable basis for determining usefulness.

By confirming that decision makers' confidence can be increased by the provision of finer information sets, the authors' results have practical implications for accounting standard setting.

By testing the impact of report differences on user decision confidence, the paper addresses a previously overlooked issue.

Accounting information systems are mainly rule-based, and data are usually available and well-structured. However, many accounting systems are yet to catch up with current technological developments. Thus, artificial intelligence (AI) in financial accounting is often applied only in pilot projects. Using AI-based forecasts in accounting enables proactive management and detailed analysis. However, thus far, there is little knowledge about which prediction models have already been evaluated for accounting problems. Given this lack of research, our study aims to summarize existing findings on how AI is used for forecasting purposes in financial accounting. Therefore, the authors aim to provide a comprehensive overview and agenda for future researchers to gain more generalizable knowledge.

The authors identify existing research on AI-based forecasting in financial accounting by conducting a systematic literature review. For this purpose, the authors used Scopus and Web of Science as scientific databases. The data collection resulted in a final sample size of 47 studies. These studies were analyzed regarding their forecasting purpose, sample size, period and applied machine learning algorithms.

The authors identified three application areas and presented details regarding the accuracy and AI methods used. Our findings show that sociotechnical and generalizable knowledge is still missing. Therefore, the authors also develop an open research agenda that future researchers can address to enable the more frequent and efficient use of AI-based forecasts in financial accounting.

Owing to the rapid development of AI algorithms, our results can only provide an overview of the current state of research. Therefore, it is likely that new AI algorithms will be applied, which have not yet been covered in existing research. However, interested researchers can use our findings and future research agenda to develop this field further.

Given the high relevance of AI in financial accounting, our results have several implications and potential benefits for practitioners. First, the authors provide an overview of AI algorithms used in different accounting use cases. Based on this overview, companies can evaluate the AI algorithms that are most suitable for their practical needs. Second, practitioners can use our results as a benchmark of what prediction accuracy is achievable and should strive for. Finally, our study identified several blind spots in the research, such as ensuring employee acceptance of machine learning algorithms in companies. However, companies should consider this to implement AI in financial accounting successfully.

To the best of our knowledge, no study has yet been conducted that provided a comprehensive overview of AI-based forecasting in financial accounting. Given the high potential of AI in accounting, the authors aimed to bridge this research gap. Moreover, our cross-application view provides general insights into the superiority of specific algorithms.

The purpose of this paper is to measure the accuracy of management profit forecast in initial public offerings (IPO) prospectuses and investigate the determinants of any observed forecast error in Indonesia.

A sample of 105 Indonesian IPO firms over a ten-year period, 1999-2008, is used in the present study. The accuracy of management profit forecasts, or forecast errors, in IPO prospectuses is calculated, following Lee et al. (2006), over the ten-year sample period. Then, a multivariate model, following the extant literature, is used to identify the determinants of any observed forecast error in Indonesia.

A mean (median) forecast error of 19 percent (9 percent) is reported over the entire sample period. Multivariate analysis shows that, among the explanatory variables used in the present study, forecast horizon and management optimism seem to be the most significant determinants of forecast error in Indonesia.

The ordinary, specifically small, investors in Indonesia lack the sophistication needed to evaluate new issues while alternative independent sources of information or analysis on IPOs are virtually non-existent. Consequently, whether the forecasts made by the managers during IPOs are reliable or not is of particular importance in Indonesia.

Indonesia is a significant emerging market in Asia. However, to date, no published work has examined the accuracy of management profit forecasts or forecast errors in this market. The present study attempts to fill this gap in the literature and is the first to capture the magnitude/degree of forecast accuracy or error and investigate the determinants of the documented forecast error in Indonesia using a sample of 105 IPO firms over the period 1999 through 2008.

A NEW word is being thrown at management from every direction. It is Participation. Just what does it mean, what does it entail? To many board members it means little but a confounded nuisance. Not only in private or public companies; also some of the nationalised industries To workers it can mean many different things: the realisation of a dream; or a nightmare. For the fact must be faced: participation in management and direction must entail participation in responsibility. So it is sharing blame as well as grabbing credit. Unfortunately even apportioning blame doesn't keep a sinking company afloat; and the end result is the same whether the inexpert direction came from one end of the industry or the other. Inefficiency has to be paid for by bitter experience.

The purpose of this paper is to assess management earnings forecasts in a continuous disclosure environment.

A large sample of hand checked Australian management earnings forecasts are examined. These data are analysed using a series of logistic regressions. Hypotheses are proposed and tested based on Skinner's litigation cost hypothesis. Increases in non‐routine management earnings forecasts post‐2000; and increases in the proportion of such forecasts that contain bad news are predicted. The relationship between forecast specificity and forecast news content is investigated.

It was found that, post‐2000, legislative changes and increased enforcement action by ASIC were followed by increased disclosure of non‐routine management earnings forecasts. For routine forecasts, no significant increase in forecast disclosure is observed. This result is consistent with Skinner as is the finding that the increased disclosure is only apparent for bad news non‐routine forecasts. For the second objective, evidence was found that the larger the gap between market expectations and actual performance the more specific the forecast, but only for bad news forecasts.

The study extends the small amount of research investigating the characteristics of management earnings forecasts. It also provides an assessment of the effectiveness of efforts by ASIC to ensure that management meet their continuous disclosure obligations.