Enhancing the government accounting information systems using social media information: An application of text mining and machine learning

This study demonstrates a way of bringing an innovative data source, social media information, to the government accounting information systems to support accountability to stakeholders and managerial decision-making. Future accounting and auditing processes will heavily rely on multiple forms of exogenous data. As an example of the techniques that could be used to generate this needed information, the study applies text mining techniques and machine learning algorithms to Twitter data. The information is developed as an alternative performance measure for NYC street cleanliness. It utilizes Naïve Bayes, Random Forest, and XGBoost to classify the tweets, illustrates how to use the sampling method to solve the imbalanced class distribution issue, and uses VADER sentiment to derive the public opinion about street cleanliness. This study also extends the research to another social media platform, Facebook, and finds that the incremental value is different between the two social media platforms. This data can then be linked to government accounting information systems to evaluate costs and provide a better understanding of the efficiency and effectiveness of operations.     

A taxonomy to guide research on the application of data mining to fraud detection in financial statement audits

This paper explores the application of data mining techniques to fraud detection in the audit of financial statements and proposes a taxonomy to support and guide future research. Currently, the application of data mining to auditing is at an early stage of development and researchers take a scatter-shot approach, investigating patterns in financial statement disclosures, text in annual reports and MD&As, and the nature of journal entries without appropriate guidance being drawn from lessons in known fraud patterns. To develop structure to research in data mining, we create a taxonomy that combines research on patterns of observed fraud schemes with an appreciation of areas that benefit from productive application of data mining. We encapsulate traditional views of data mining that operates primarily on quantitative data, such as financial statement and journal entry data. In addition, we draw on other forms of data mining, notably text and email mining.     

Forecasting Financial Market Volatility Using a Dynamic Topic Model

This study employs big data and text data mining techniques to forecast financial market volatility. We incorporate financial information from online news sources into time series volatility models. We categorize a topic for each news article using time stamps and analyze the chronological evolution of the topic in the set of articles using a dynamic topic model. After calculating a topic score, we develop time series models that incorporate the score to estimate and forecast realized volatility. The results of our empirical analysis suggest that the proposed models can contribute to improving forecasting accuracy.     

Detecting accounting fraud in companies reporting under US GAAP through data mining

The accounting fraud detection models developed on financial data prepared under US Generally Accepted Accounting Principles (GAAP) in the current literature achieve significantly weaker performance than models based on financial data prepared under different accounting standards. This study contributes to the US GAAP accounting fraud data mining literature through the attainment of higher model performance than that reported in the prior literature. Financial data from the 10-K forms of 320 fraudulent financial statements (80 fraudulent companies) and 1,200 nonfraudulent financial statements (240 nonfraudulent companies) were collected from the US Security and Exchange Commission. The eight most commonly used data mining techniques were applied to develop prediction models. The results were cross-validated on a testing dataset and then compared according to parameters of accuracy, F-measure, and type I and II errors with existing studies from the US, China, Greece, and Taiwan. As a result, the developed predictive models for accounting fraud achieved performance comparable to those achieved by models built on data from other accounting standards. Moreover, the developed models also significantly outperformed (accuracy 10.5%, F-measure 16.1%, type I error 12.2% and type II error 15.2%) existing studies based on US GAAP financial data. Furthermore, this study provides an extensive literature review encompassing recent accounting fraud theory. It enhances the existing US fraud data mining literature with a performance comparison of studies based on other accounting standards.     

Accounting practices of the St. Joseph Lead Company: 1864-1900

This paper presents the results of an inquiry into the accounting practices of the St. Joseph Lead Company during the nineteenth century. For several decades following its incorporation in 1864 the St. Joseph Lead Company maintained a very crude double-entry bookkeeping system that lacked detailed cost accounting records. In fact, there is little evidence of any type of industrial accounting prior to 1890 when a direct cost responsibility accounting system was established. Thus, the industrial accounting procedures of the St. Joseph Lead Company appear to have lagged far behind the practices of the contemporary British and American mining firms which have been the objects of recent studies. The investigation thereby reveals considerable diversity in the industrial accounting practices of the American mining industry during the second half of the nineteenth century.     

Financial process mining - Accounting data structure dependent control flow inference

The increasing integration of computer technology for the processing of business transactions and the growing amount of financially relevant data in organizations create new challenges for external auditors. The availability of digital data opens up new opportunities for innovative audit procedures. Process mining can be used as a novel data analysis technique to support auditors in this context. Process mining algorithms produce process models by analyzing recorded event logs. Contemporary general purpose mining algorithms commonly use the temporal order of recorded events for determining the control flow in mined process models. The presented research shows how data dependencies related to the accounting structure of recorded events can be used as an alternative to the temporal order of events for discovering the control flow. The generated models provide accurate information on the control flow from an accounting perspective and show a lower complexity compared to those generated using timestamp dependencies. The presented research follows a design science research approach and uses three different real world data sets for evaluation purposes.     

Beyond the boring grey: The construction of the colourful accountant

The accounting profession has long laboured under the weight of the stigma of the accounting stereotype. This unappealing persona may pose a potential problem for recruitment into the profession. How is the gregarious graduate to be tempted into the tentacles of the dull and the dreary? Drawing on Goffman's work on stigma and impression management, this paper examines the recruitment literature of the ‘big four’ accounting firms and six of the professional institutes in an attempt to unravel the techniques deployed by the profession to camouflage the spectre of the stereotype. The investigation reveals how the recruitment discourse, an important stage in the process of professional socialization, is used to construct an image of the trendy and fun loving accountant. Through text and image, a carefully orchestrated campaign of impression management casts aside the boring bookkeeper in favour of an altogether more colourful characterisation.     

外商投资企业亏损问题的分析与对策

本文结合在实际税收征管工作中遇到的问题,利用现代计量经济学的方法,对我国外商投资企业(以下简称“外企”)账面亏损问题进行了分析,发现主要是中小企业特别是小企业亏损面大,建议应该从抓好对信息资源的深入挖掘利用、加强系统内部和外部的工作配合、搞好征管手段的整合配置、处理好对中小税源查账征收和核定征收的关系等方面强化对中小外企所得税的征管。     

The development of fixed asset accounting in South African gold mining companies: confronting the issues of prudence,matching, periodicity and capital maintenance

The ‘Appropriation Method’ of accounting applied by South African gold mining companies is fundamentally different from mine accounting elsewhere and results in reported earnings and asset values that are not comparable with those of mining companies in other countries. This paper traces the development of the Method, in an historical context, in an attempt to understand why, and how, it emerged and became established. Particular attention is paid to 19th century writings of local accountants, ‘transactions’ of professional bodies, and to the special characteristics of the South African gold mining industry. Transitional processes are illustrated by reference to the published accounts of the Crown Reef Gold Mining Company. The persistence of the Appropriation Method is a reminder that while assumptions of uniform accounting periods, matching, business continuity and the need for capital maintenance underpin most conventional accounting, nevertheless useful accountings can exist without these assumptions.     

Embedding process mining into financial statement audits

The audit of financial statements is a complex and highly specialized process. Digitalization and the increasing automation of transaction processing create new challenges for auditors who carry out those audits. New data analysis techniques offer the opportunity to improve the auditing of financial statements and to overcome the limitations of traditional audit procedures when faced with increasingly large amounts of financially relevant transactions that are processed automatically or semi-automatically by computer systems. This study discusses process mining as a novel data analysis technique which has been receiving increased attention in the audit practice. Process mining makes it possible to analyse business processes in an automated manner. This study investigates how process mining can be integrated into contemporary audits by reviewing the relevant audit standards and incorporating the results from a field study. It demonstrates the feasibility of embodying process mining within financial statement audits in accordance with contemporary audit standards and generally accepted audit practices. Implementation of process mining increases the reliability of the audit conclusions and improves the robustness of audit evidence by replacing manual audit procedures. Process mining as novel data mining technique provides auditors the means to keep pace with current technological developments and challenges.     

Predicting industry sectors from financial statements: An illustration of machine learning in accounting research

The main aim and contribution of this study is to outline and demonstrate the usefulness of a machine learning approach to address prediction-based research problems in accounting research, and to contrast this approach with a more conventional explanation-based approach familiar to most accounting scholars. To illustrate the approach, the study applies machine learning to predict a firm's industry sector using the firm's publicly available financial statement data. The results show that an algorithm can predict an industry sector with just this data to a high degree of accuracy, especially if a non-linear classifier is used instead of a linear classifier. Additionally, the algorithms were able to carry out an industry-firm pairing exercise taken from introductory accounting text books and MBA cases, with predicted answers showing a high degree of accuracy in carrying out this exercise. The study shows how machine learning approaches and algorithms can be valuable to a range of accounting domains where prediction rather than explanation of the dependent variable is the main area of concern.     

Narrative accounting disclosure: Its role in the gold mining industry on the Gold Coast 1900–1949

Gold mining played a major role in the extraction of surplus value by colonial powers in Africa. The paper examines the role of narrative accounting disclosure in the gold mining industry in colonial Ghana (the Gold Coast) during the period 1900–1949. Narrative accounting disclosure was deployed in the extraction of value by English gold mining companies, in raising capital via a series of “gold booms” and subsequently in defending the companies’ labour policies in the face of trade union resistance. The paper concludes that an accounting disclosure was an important part of the strategy of the gold mining companies in promoting and legitimising themselves.     

Taking the customer into account: Transcending the construction of the customer through the promotion of self-accounting

The importance that businesses have accorded their customers during the past thirty years has not, as yet, been fully matched by the development of accounting for the customer. A range of customer-related techniques has emerged, including customer profitability analysis, the balanced scorecard and several strategic management accounting approaches. In large part these can be characterised as attempts to construct the customer in a very conventional manner, one which serves the interests of business rather than customers. Similar emphases can also be identified in the marketing management literature, which in recent years has shown worrying signs of becoming focused on the adoption of a form of hard number accounting. The purpose of this paper is to provide a review and critique of extant customer accounting techniques and approaches, as well as identifying some of the fundamentals of a more appealing attempt at ‘taking the customer into account’.     

CORPORATE DIVIDEND POLICY DETERMINANTS: INTELLIGENT VERSUS A TRADITIONAL APPROACH

Dividend is the return that an investor receives when purchasing a company's shares. The decision to pay these dividends to shareholders concerns several other groups of people, such as financial managers, consulting firms, individual and institutional investors, government and monitoring authorities, and creditors, just to name a few. The prediction and modelling of this decision has received a significant amount of attention in the corporate finance literature. However, the methods used to study the aforementioned question are limited to the logistic regression method without any implementation of the advanced and expert methods of data mining. These methods have proven their superiority in other business‐related fields, such as marketing, production, accounting and auditing. In finance, bankruptcy prediction has the vast majority among data‐mining implementations, but to the best of the authors’ knowledge such an implementation does not exist in dividend payment prediction. This paper satisfies this gap in the literature and provides answers that help to understand the so‐called ‘dividend puzzle’. Specifically, this paper provides evidence supporting the hypothesis that data‐mining methods perform better in accuracy measures against the traditional methods used. The prediction of dividend policy determinants provides valuable benefits to all related parties, as they can manage, invest, consult and monitor the dividend policy in a more effective way. Copyright © 2013 John Wiley & Sons, Ltd.     

The Application of Management Accounting Techniques to Marketing

This paper examines the accounting techniques presently used to assist decision making and control within the marketing function of companies that operate in a highly competitive industry. The overall observation is that, while the use of these techniques is greater than in earlier studies conducted in less competitive industries, only a handful of management accounting techniques considered superior by the conventional wisdom have found their way into regular usage. No evidence was found to indicate that better performing firms employ these techniques on a more regular basis.     

Machine Learning and Expert Judgement: Analyzing Emerging Topics in Accounting and Finance Research in the Asia–Pacific

In this paper, we focus on the question to what extent machine learning (ML) tools can be used to support systematic literature reviews. We apply a ML approach for topic detection to analyze emerging topics in the literature—our context is accounting and finance research in the Asia–Pacific region. To evaluate the robustness of the approach, we compare findings from the automated ML approach with the results from a manual analysis of the literature. The automated approach uses a keyword algorithm detection mechanism whereby the manual analysis uses common techniques for qualitative data analysis, that is, triangulation between researchers (expert judgement). From our paper, we conclude that both methods have strengths and weaknesses. The automated analysis works well for large corpora of text and provides a very standardized and non-biased way of analyzing the literature. However, the human researcher is potentially better equipped to evaluate current issues and future trends in the literature. Overall, the best results might be achieved when a variety of tools are used together.     

Means versus ends: a review of the literature on Japanese management accounting

As an introduction to the topic of Japanese management accounting, we provide here a review of the literature and an attempt to categorize what has been written previously on the subject. One obvious characteristic that emerged during this review is that Japanese management accounting is designed to operate in a 'Japanese setting', not a Western one. Much of the literature on Japanese methods recognizes some aspect of that setting when describing the techniques. During the review process, several related clusters of recurring practices surfaced. We have crystallized these clusters into five themes. They are: (1) examining work using the 'eyes' of the market; (2) focusing on the quality of work; (3) employing 'waste' as the measure of cost; (4) continually improving the way work is done, and (5) sharing knowledge through vertical and horizontal communication. It is our contention that the Japanese management accounting techniques described in the literature cannot be sensibly and effectively used in Western venues without an understanding of how the Japanese employ these particular management accounting techniques to pursue the multiple themes behind their use.     

Integrating intelligent systems into marketing to support market segmentation decisions

For the last 50 years market segmentation has been considered to be a key concept in marketing strategy. As a means of tackling market heterogeneity, the underlying logic and managerial rationale for market segmentation is well established in the marketing literature. However, there is evidence to suggest that attempts by organizations to classify customers into distinct segments for whom product or services can be specifically tailored are proving to be difficult to implement in practice. As the business environment in which many organizations operate becomes increasingly uncertain and highly competitive, greater importance is now being attached to marketing knowledge. The purpose of this paper is to highlight market segmentation problems as a relevant area for a greater level of engagement of intelligent systems academic researchers and practitioners with their counterparts within the marketing discipline, in order to explore how data mining approaches can assist marketers in gaining valuable insights into patterns of consumer behaviour, which can then be used to inform market segmentation decision‐making. Since the application of data mining within the marketing domain is only in its infancy, a research agenda is proposed to encourage greater interdisciplinary collaboration between information systems and marketing so that data mining can more noticeably enter the repertoire of analytical techniques being employed for segmentation. Copyright © 2007 John Wiley & Sons, Ltd.