Financial statement users’ institutional logic

In this paper, we investigate what we call “financial statement users’ institutional logic,” defined as users’ expressed fundamental views and beliefs about accounting information. We analyze users’ comment letters to standard setters in response to the proposed standards on lease accounting to identify the dimensions of the institutional logic that underlie their views on accounting information. Our qualitative analysis identified and validated ten principal dimensions, namely economics and substance, due process issues, measurement, readiness and relevance for use, conceptual foundations, clarity, presentation and disclosure, cost-benefit issues, comparability and consistency, and financial statement manipulation. Quantitative analyses revealed that four of these dimensions, i.e. due process issues, readiness and relevance for use, comparability and consistency, and cost-benefit issues, occupy a medium or large amount of space in users’ comments and are referred to in strong terms, while economics and substance and measurement, although also widely discussed, are addressed in weaker terms. Overall, our study begins to fill a gap in the literature by providing insights into users’ views on accounting information. These insights challenge the “homo economicus user” currently constructed in standard-setting debates.     

上市公司财务报告舞弊的识别——基于三角形理论的实证研究

为构建财务报告舞弊识别模型,本文选取2000—2009年发生财务报告舞弊的A股上市公司及其配对非舞弊公司为研究对象,利用配对样本t检验、Wilcoxon符号秩检验、Logistic回归,对描述三角形理论的25个指标研究发现,两类公司之间营业利润—经营现金流量、外部董事比例等指标描述的压力和机会因素存在显著差异;各指标与舞弊可能性的相关关系表明,压力越大、机会越多,舞弊可能性越大。由此建立的识别模型正确识别率达到93.7%,有助于人们识别舞弊,帮助上市公司发现舞弊根源。     

Predicting accounting fraud using imbalanced ensemble learning classifiers – evidence from China

The current research aims to launch effective accounting fraud detection models using imbalanced ensemble learning algorithms for China A-Share listed firms. Based on a sample of 33,544 Chinese firm-year instances from 1998 to 2017, this research respectively established one logistic regression and four ensemble learning classifiers (AdaBoost, XGBoost, CUSBoost, and RUSBoost) by 12 financial ratios and 28 raw financial data. Additionally, we divided the sample into the train and test observations to evaluate the classifiers' out-of-sample performance. In detail, we applied two metrics, namely, Area under the ROC (receiver operating characteristic) curve (AUC) and Area under the Precision-Recall curve (AUPR), to evaluate classifiers' discriminability. In the supplement test, this study put forward an algebraic fused model on the basis of the four ensemble learning classifiers and introduced the sliding window technique. The empirical results showed that the ensemble learning classifiers can detect accounting fraud for the imbalanced China A-listed firms far more effectively than the logistic regression model. Moreover, imbalanced ensemble learning classifiers (CUSBoost and RUSBoost) effectively performed better than the common ensemble learning models (AdaBoost and XGBoost) in average. The algebraic fused model in the supplement test also obtained the highest average AUC and AUPR among all the employed algorithms. Our results offer firm support for the potential role of Machine Learning (ML)-based Artificial Intelligence (AI) approaches in reliably predicting accounting fraud with high accuracy. Similarly, for the Chinese settings, our ML-based AI offers utmost advantage in forecasting accounting fraud. Finally, this paper fills the research gap on the applications of imbalanced ensemble learning in accounting fraud detection for Chinese listed firms.     

Economic policy uncertainty and financial statement comparability

We examine the implications of economic policy uncertainty (EPU) for financial statement comparability. We posit that the increased difficulty of estimating future cash flows and the increased opportunity for earnings management with increased EPU reduce the quality of earnings and its comparability. Consistent with this reasoning, we find a negative relation between earnings comparability and lagged EPU. Further, the association between EPU and comparability is more negative for firms that have poorer accruals quality and higher earnings volatility. We do not find that accounting policy choice is systematically related to the association between EPU and comparability. These results suggest that cross-sectional differences in accounting estimates rather than accounting policies influence the relation between EPU and comparability.     

Creating financial statements using FRx within Microsoft Dynamics GP: Learning vs. teaching

This project requires you to create financial statements using FRx within Microsoft Dynamics GP, an enterprise system. The project emphasizes the learner-centered paradigm rather than a teacher-centered educational paradigm. Researchers have found great importance in the learner-centered approach in educating students, especially in information systems (Landry, Saulnier, Wagner, & Longenecker, 2008; Saulnier, Landry, & Wagner, 2008). Major differences exist between the two approaches; for example, the professor gives information and evaluates, and emphasizes the right answer in a teacher-centered approach. The professor coaches and facilitates, and emphasizes that students generate good questions and learn from mistakes in a learner-centered approach. This project utilizes the learner-centered approach, an effective approach for you to use in today’s environment.     

Generative Bayesian neural network model for risk-neutral pricing of American index options

Financial models with stochastic volatility or jumps play a critical role as alternative option pricing models for the classical Black–Scholes model, which have the ability to fit different market volatility structures. Recently, machine learning models have elicited considerable attention from researchers because of their improved prediction accuracy in pricing financial derivatives. We propose a generative Bayesian learning model that incorporates a prior reflecting a risk-neutral pricing structure to provide fair prices for the deep ITM and the deep OTM options that are rarely traded. We conduct a comprehensive empirical study to compare classical financial option models with machine learning models in terms of model estimation and prediction using S&P 100 American put options from 2003 to 2012. Results indicate that machine learning models demonstrate better prediction performance than the classical financial option models. Especially, we observe that the generative Bayesian neural network model demonstrates the best overall prediction performance.     

Universal features of price formation in financial markets: perspectives from deep learning

Using a large-scale Deep Learning approach applied to a high-frequency database containing billions of market quotes and transactions for US equities, we uncover nonparametric evidence for the existence of a universal and stationary relation between order flow history and the direction of price moves. The universal price formation model exhibits a remarkably stable out-of-sample accuracy across a wide range of stocks and time periods. Interestingly, these results also hold for stocks which are not part of the training sample, showing that the relations captured by the model are universal and not asset-specific.

The universal model—trained on data from all stocks—outperforms asset-specific models trained on time series of any given stock. This weighs in favor of pooling together financial data from various stocks, rather than designing asset- or sector-specific models, as is currently commonly done. Standard data normalizations based on volatility, price level or average spread, or partitioning the training data into sectors or categories such as large/small tick stocks, do not improve training results. On the other hand, inclusion of price and order flow history over many past observations improves forecast accuracy, indicating that there is path-dependence in price dynamics.     

Meeting user information needs: The impact of major changes in FASB and GASB standards on financial reporting by colleges and universities

This longitudinal study reports the impact of changes in generally accepted accounting principles on financial statement disclosures for 100 public and private institutions of higher education. Disclosures from the period when all colleges and universities followed the same accounting standards are compared with disclosures in periods after major changes in accounting and reporting standards were made by the Financial Accounting Standards Board (FASB) for private institutions and by the Governmental Accounting Standards Board (GASB) for public institutions. We find that an importance-weighted disclosure index shows that user needs are better met using the new reporting standards for public but not private institutions. An expanded unweighted index, however, shows improvement for both public and private colleges and universities. Using this disclosure index, the improvement for universities reporting under GASB standards exceeded the improvement for those reporting under FASB standards.