Further evidence of the effects of normalization on end-user query errors: an experimental evaluation

The proliferation of Windows-based programs for PCs, together with the requirement of many business managers for faster responses to their information needs, has led many business end-users to create, maintain, and query their own databases. These individuals use the output of these queries as the basis for operational, tactical, and strategic decisions. To maximize the benefit of end-users' time, querying these databases must be as efficient as possible. Of even greater importance, the effectiveness of managers' decision-making is directly related to the quality of the information extracted. Because end-user querying is error prone, characterizing the sources of query errors and using that knowledge to improve the effectiveness of end-user query development can improve the quality of information available for decision-making.IS professionals typically design databases in third normal form. End-user databases designed with the help of IS professionals are also likely to be in third normal form. Although third normal form is better for data capture, prior research indicates third normal form is not necessarily the most appropriate normal form for querying. This paper reports the results of an in-depth experiment into the effects of normalization level on the efficiency and effectiveness of end-user querying. The results confirm earlier findings that end-users querying first normal form data structures were both more efficient and more effective than those querying third normal form data structures. This research extends prior research by examining the specific areas where first normal end-users outperform third normal form end-users. In particular, the experiment revealed that first normal form end-users made significantly fewer errors in relation to attribute selection, table selection, and row restriction.     

Cognitive style factors affecting database query performance

As end-user computing becomes more pervasive, an organization's success increasingly depends on the ability of end-users, usually in managerial positions, to extract appropriate data from both internal and external sources. Many of these data sources include or are derived from the organization's accounting information systems. Managerial end-users with different personal characteristics and approaches are likely to compose queries of differing levels of accuracy when searching the data contained within these accounting information systems.This research investigates how cognitive style elements of personality influence managerial end-user performance in database querying tasks. A laboratory experiment was conducted in which participants generated queries to retrieve information from an accounting information system to satisfy typical information requirements. The experiment investigated the influence of personality on the accuracy of queries of varying degrees of complexity. Relying on the Myers–Briggs personality instrument, results show that perceiving individuals (as opposed to judging individuals) who rely on intuition (as opposed to sensing) composed queries more accurately. As expected, query complexity and academic performance also explain the success of data extraction tasks.     

Effects of winsorization: The cases of forecasting non‐GAAP and GAAP earnings

This study examines how the winsorization procedure affects the performance of regression‐based earnings forecasting models. I find that the impact is multifaceted and depends principally on three factors: the level of data errors in the tails, the characteristics of firms affected by the process, and the use of scaling. For a non‐GAAP earnings yield specification, where data input errors exist, winsorization changes the information set in a non‐systematic way and helps to improve the performance of regression‐based forecasts, especially when the least squares estimator is employed. However, for a non‐GAAP earnings per share specification, with fewer data input errors found in the tails of the distribution, winsorization has a particularly strong effect on very large companies, lowering the economic value of earnings predictions. I observe similar results for corresponding GAAP earnings specifications. Robust estimators, such as least absolute deviation, high breakdown‐point and Theil‐Sen, appear to be a more effective solution than winsorization. Their earnings forecasts consistently yield significant positive abnormal returns across non‐GAAP and GAAP earnings specifications.     

Hands-on training in relational database concepts

Accounting has often been criticized for providing summarized information that satisfies only a limited number of information views. Relational database models can facilitate the collection of an extensive amount of disaggregated data beyond what is available in the traditional accounting model. The ability to query the database provides the decision maker with more types of information, while avoiding the overload that can occur when too much information is presented that is irrelevant to the decision process of a certain group. This article presents a simple hands-on illustration in Microsoft Access that can be used in accounting courses to provide students with the fundamentals of using the relational database model in meeting the information needs of organizational decision makers.     

浅议网络环境下高校资料室特色数据库

本文论述了信息时代,高校资料室特色数据库是图书馆数字化建设的重要组成部分,同时它也是图书馆信息服务的重要内容之一。特色数据库建设应遵循标准化、规范化原则,联合建库与实用性原则,严格遵循特色数据库建设的标准。选择适合自己的建库软件,合适的专业工作人员,立足于对原始资料的收集整理,开展个性化的特色数据库建设。     

Some issues in blockchain for accounting and the supply chain,with an application of distributed databases to virtual organizations

This paper reviews some recent blockchain‐based applications for information capture, distribution and preservation. As part of that review, this paper examines two key concerns with current blockchain designs for accounting and supply chain transactions: data independence and multiple semantic models for the same information distribution problem. Blockchain applications typically integrate database, application and presentation tiers all in the same ledger. This results in a general inability to query information in the ledger and other concerns. Further, since most applications appear to be private blockchain applications, there is a concern of agents needing to accommodate multiple blockchains depending on who their trading partners are and what they request. Finally, this paper uses a distributed database to design a ‘blockchain‐like’ system for virtual organizations.     

Household Optimism and Overborrowing

We use Finnish household‐level data from 1994 to 2013 to measure how often and what kind of forecast errors households make and how the errors are linked to the households' borrowing behavior and overindebtedness. We find that those households that make the largest optimistic forecast errors have greater debt‐to‐income ratios. They also are more likely to report that they suffer from excessive debt loads and have problems in coping with their bills. There are no such systematic effects for the households that make pessimistic forecast errors.     

Initial public offering prospectus forecast errors and mandatory explanations: evidence from New Zealand

This paper examines management forecast errors in initial public offering (IPO) prospectuses of New Zealand firms and subsequent management explanations for earnings forecast errors in recent years. New Zealand has several unique features and recent changes that are worthy of research, including the requirement for management to make forecasts; a requirement to explain differences between forecast and actual; and a recent change that provides the voluntary opportunity to obtain a negative assurance opinion on forecasts. Using hand-collected IPO data between 1998 and 2014, we find that firms that include a negative assurance opinion on the prospective financial information in the prospectus have more accurate earnings and cash flow forecasts. We investigate the subsequent explanations for forecast errors, which are mandated by a financial reporting standard. We show that management tends to attribute negative and large forecast errors to external causes rather than their own actions. Our findings have implications for regulators and investors in New Zealand and other settings.     

TOWARDS A FRAMEWORK TO STUDY INFLUENCE AND ACCOUNTING USE

The autonomous regions of Spain have responsibility for healthcare. The Galician Health Service aims to achieve a higher level of health for Galicians with an appropriate primary healthcare system. The law requires healthcare to be promoted within a system of participative management directed by an upper level of management. The healthcare system is changing, and the upper level managers are introducing new systems to improve the provision and administration of care. These changes will include the provision of accounting data in the normal course of managing.This paper is before a study of the changes. The paper provides the background to constructing a Framework and then constructs a Framework for our empirical work. It uses the constructs of information and influence with markers of the information areas and styles of influence in the form of an Information–Influence Matrix (Purdy, 1993b). It also uses personal construction (Kelly, 1955) to help create a dynamic approach to change and the use of accounting information. It finally provides a framework of our anticipations of what might happen to a Health Centre Manager and an Upper Manager.     

Does foreign exchange risk matter to equity research analysts when forecasting stock prices? Evidence from U.S. firms

We study the relationship between the foreign exchange risk and analyst target price forecast error using U.S. firm-level data for the sample period between 1999 and 2014. We find that the target price forecast error is higher when foreign exchange risk is higher. The relationship is stronger for smaller firms and less pronounced among financial firms. Collectively, the findings suggest that analysts make fewer errors when forecasting for firms that are more capable of managing foreign exchange risks. These findings imply that either analyst does not make a significant effort in incorporating foreign exchange risk into their forecast models, or they do not have the skills for that task.     

The intraday impact of company responses to exchange queries

This study investigates Australia’s unique continuous disclosure regime using intraday data on the Australian Securities Exchange (ASX) over the period January 2010–April 2012. We examine abnormal returns and trading volumes that accrue to shareholders immediately after an announcement responding to a trading induced query. The use of intraday data permits us to examine the direct impact of these events, and the length of time the market takes to incorporate this information with a higher degree of precision than the research currently on offer. The study is framed within an event study methodology, with a number of robustness measures: a matched sample approach; analysis of cross-sectional determinants; the removal of penny stocks; and, procedures to account for sample selection bias. We find significant share price reversals following a query announcement, with a reversal of 3.3% by the end of the widest event interval. Our study also provides evidence that the market takes up to 60 min to impound this information. Overall, we provide support for the efficacy of the query framework administered by the ASX.     

Arrogance can be a virtue: Overconfidence,information acquisition,and market efficiency

In behavioral finance, overconfidence has been established as a prevalent psychological bias, which can make markets less efficient by creating mispricing in the form of excess volatility and return predictability. In this paper, we develop a model in which overconfidence causes investors to overinvest in information acquisition when this information could improve market efficiency by driving prices closer to true values. We study the impact of overconfidence on mispricing and information acquisition, comparing their net effect on prices. We derive several novel implications. First, overconfidence generally improves market pricing provided the level of overconfidence is not too high. Pricing can also improve even when overconfidence is arbitrarily high, depending on the amount of private information acquired relative to publicly available information.     

Determinants of No-Fault Insurance Measures

In spite of being touted as the panacea for rising premiums and unfair settlements, no‐fault automobile insurance provisions exist in fewer than one third of U.S. states. Few researchers have examined why such measures exist in some states but not in others. This article focuses directly on this issue by looking at the factors that help explain the type of no‐fault regime in place. The article conducts an empirical analysis using a data set that spans all 50 states over the 19‐year period from 1972 to 1990. Among other things, the analysis finds that the structure of the insurance industry and the type of rate regulation under which it operates are determinants of these decisions.     

Order flow information and spot rate dynamics

This paper examines why order flows are empirically important drivers of spot exchange rate dynamics. We consider a decomposition for the depreciation rate that must hold in any model and show that order flows will appear as important proximate drivers when they convey significant incremental information about future interest rate differentials, risk premiums and/or long-run exchange rate levels (i.e., information that cannot be inferred from publicly observed variables). We estimate the importance of these incremental information flows for the EURNOK spot exchange rate using eight years of high-quality, disaggregated, end-user order flow data collected by the Norges Bank.     

THE USE OF MULTIPLE INSTRUMENTS FOR MEASUREMENT OF EARNINGS FORECAST ERRORS, FIRM SIZE EFFECT AND THE QUALITY OF ANALYSTS'FORECAST ERRORS

This paper develops an instrumental variables framework to form a better proxy for earnings forecast errors. The key aspect of the approach is to extract information from alternative proxies for the same underlying variable, namely a portion of realized earnings signals unexpected by the market. We use signs of various proxies for earnings forecast errors obtained from different time-series forecasting models as multiple instruments. The results show that the instrumental variables approach is effective for reducing measurement errors inherent in various proxies for earnings forecast errors. It produces not only a smaller magnitude but also a narrower dispersion of earnings forecast errors. The paper provides evidence that the instrumental variables approach performs better for small-firm samples than for large-firm samples. Finally, we observe that analysts' forecast errors seasoned with the signs of various time-series forecast errors (as well as the signs of their own forecast errors) outperform those without seasoning. This indicates that analysts' forecast errors can still be improved by employing the instrumental variables technique.     

THE INFORMATION CONTENT AND USEFULNESS OF THE TERM STRUCTURE OF THE NEW ZEALAND BANK BILL MARKET

This paper applies the rationality concept and expectations hypothesis to test the information efficiency of the term structure of the New Zealand bank bill market. Weekly data is collected from June 1986 to November 1988. The sample period is partitioned into two subperiods by the sharemarket crash in October 1987. The empirical results suggest the presence of a time varying risk premium. This is reflected by the significantly positive volatility measure in the first subperiod and the significant interest rate level variable in both subperiods. The forecast errors correlate significantly with the growth in money supply and overseas interest rate variables. Factors other than market information inefficiency could be responsible for the significant correlation; namely the impact of the sharemarket crash on market perceptions about inflation expectations and the non-simultaneous data problem in calculating the differential costs of borrowing. Despite the rejection of the joint hypothesis, forward rates are found to have information about future spot rates beyond that contained in past spot rates, and are able to predict interest rates at least 30 days ahead.     

财务披露管理方式的维度观

财务披露是一个多维向量 ,其维度包括信息内容、组织形式、信息冗余度、信息置信度、披露者的解释、披露媒介以及披露时机。对该向量的任何一个维度施加影响 ,都会导致其发生某些变化 ,从而都属于财务披露管理活动。我国企业对财务披露的管理手段比较单一 ,倾向于直接操纵盈余数据。财务披露管理方式的多样化可以减少直接操纵盈余数据的行为 ,这对于改善我国目前存在的财务信息严重失真问题大有裨益。     

Estimating exponential affine models with correlated measurement errors: Applications to fixed income and commodities

Exponential affine models (EAMs) are factor models popular in financial asset pricing requiring a dynamic term structure, such as for interest rates and commodity futures. When implementing EAMs it is usual to first specify the model in state-space form (SSF) and then to estimate it using the Kalman filter. To specify the SSF, a structure of the measurement error must be provided which is not specified in the EAM itself. Different specifications of the measurement errors will result in different SSFs, leading to different parameter estimates. In this paper we investigate the influence of the measurement error specification on the parameter estimates. Using market data for both fixed income and commodities we provide evidence that measurement errors are cross-sectionally and serially correlated, which is not consistent with the independent identically distributed (iid) assumptions commonly adopted in the literature. Using simulated data we show that measurement error assumptions affect parameter estimates, especially in the presence of serial correlation. We provide a new specification, the augmented state-space form (ASSF), as a solution to these biases and show that the ASSF gives much better estimates than the basic SSF.     

Financial density selection

We propose and study simple but flexible methods for density selection of skewed versions of the two most popular density classes in finance, the exponential power distribution and the t distribution. For the first type of method, which simply consists of selecting a density by means of an information criterion, the Schwarz criterion stands out since it performs well across density categories, and in particular when the DGP is normal. For the second type of method, general-to-specific density selection, the simulations suggest that it can improve the recovery rate in predictable ways by changing the significance level. This is useful because it enables us to increase (reduce) the recovery rate of non-normal densities by increasing (reducing) the significance level, if one wishes to do so. The third type of method is a generalisation of the second type, such that it can be applied across an arbitrary number of density classes, nested or non-nested. Finally, the methods are illustrated in an empirical application.     

Measurement Errors and Information Content of Segment Reporting

This paper assesses the measurement errors inherent in segment reporting. Measurement errors are gauged by comparing the correlation of segment results with their industry to the corresponding correlation for single line-of-business firms operating in the same industry. The findings show that the measurement errors in segment information, particularly earnings, are larger than those in the financial information reported by single line-of-business firms. The cross-sectional variation in the measurement errors can be traced to cost/revenue allocations, management intervention in segment reporting, and the operational structure of multi-segment firms. Market tests indicate that the information content of segment information is inversely related to the estimated measurement errors. This revised version was published online in November 2006 with corrections to the Cover Date.