USE OF VISUALIZATION IN DIGITAL FINANCIAL REPORTING: THE EFFECT OF SPARKLINE

Information visualization (InfoViz) is an essential component of decision support systems (DSS). Sparklines is a visualization tool. This study examines if Sparklines in digital financial reports aids novice investors and if so under what circumstances? Does it enhances decision-making performance and facilitates effective decision-making experience? Additionally, does it lowers decision making effort; reduces dilution effect from non-relevant data in financial reports and mitigates recency bias in using digital financial reports? The hypothesis is guided by the theory of Proximity Compatibility Principle and the Theory of Cognitive Fit. The research methodology for this study is a repeated measure, controlled laboratory based experiment. A pilot test was conducted in with a sample of forty undergraduate students from Gatton College of Business and Economics. The sample size for this study was 275 subjects. The result revealed that there was significant effect of sparklines on decision making performance and it provides an incremental value over a tabular format. Sparklines makes an important contribution towards mitigating recency bias. The results also suggested that the irrelevant information cue in the shareholder’s report were not able to weaken the impact of relevant information in the audited financial data reported using sparklines. Sparklines increased the attention of the readers to the tables. Subjects performed the integrative tasks and spatial better when using Sparklines. For tasks such as symbolic tasks, Sparkline does not necessarily improve decision performance. It was also found out that decision makers experience greater satisfaction when using sparklines. The overall cognitive load experienced by subjects was lower using sparklines when task demands are high (such as in a bankruptcy prediction task). Interestingly, the results indicate that there is no significant effect of sparkline on decision confidence and time. In conclusion, recall of facts and pattern among subjects was found superior with use of sparkline. This study provides an empirical and justifiable basis for policy makers to make explicit recommendations about use of novel graphics such as sparkline in digital financial reports. Limitations of this study are noted

A Big Data analytics approach for construction firms failure prediction models

Using 693,000 datacells from 33,000 sample construction firms that operated or failed between 2008 and 2017, failure prediction models were developed using artificial neural network (ANN), support vector machine (SVM), multiple discriminant analysis (MDA) and logistic regression (LR). The accuracy of the models on test data surprisingly showed ANN to have only a slightly better accuracy than LR and MDA. The ANN’s number of units in the hidden layer and weight decay hyperparameters were consequently tuned using the grid search. Tuning process led to tedious machine computation that was aborted after many hours without completion. The state of art Big Data Analytics (BDA) technology was, for the first time in failure prediction, consequently employed and the tuning was completed in some seconds. Mean accuracy from cross-validation was used for selection of the model with best parameter values which were used to develop a new ANN model which outperformed all previously developed models on test data. Subsequent use of selected variables to develop new models led to reduced tuning computational cost but not improved performance. Since the real-life effect of a misclassification cost is greater than the tedious computation cost, it was concluded that BDA is the best compromise

Deep Learning Model Implementation Using Convolutional Neural Network Algorithm for Default P2P Lending Prediction

Peer-to-peer (P2P) lending is one of the innovations in the field of fintech that offers microloan services through online channels without intermediaries. P2P  lending facilitates the lending and borrowing process between borrowers and lenders, but on the other hand, there is a threat that can harm lenders, namely default.  Defaults on  P2P  lending platforms result in significant losses for lenders and pose a threat to the overall efficiency of the peer-to-peer lending system. So it is essential to have an understanding of such risk management methods. However, designing feature extractors with very complicated information about borrowers and loan products takes a lot of work. In this study, we present a deep convolutional neural network (CNN) architecture for predicting default in P2P lending, with the goal of extracting features automatically and improving performance. CNN is a deep learning technique for classifying complex information that automatically extracts discriminative features from input data using convolutional operations. The dataset used is the Lending Club dataset from P2P lending platforms in America containing 9,578 data. The results of the model performance evaluation got an accuracy of 85.43%. This study shows reasonably decent results in predicting p2p lending based on CNN. This research is expected to contribute to the development of new methods of deep learning that are more complex and effective in predicting risks on P2P lending platforms

Will it fail and why? A large case study of company default prediction with highly interpretable machine learning models

Finding a model to predict the default of a firm is a well-known topic over the financial and data science community. Default prediction problem has been studied for over fifty years, but remain a very hard task even today. Since it maintains a remarkable practical relevance, we try to put in practice our efforts in order to obtain the maximum rediction results, also in comparison with the reference literature. In our work we use in combination three large and important datasets in order to investigate both bankruptcy and bank default: a state of difficulty for companies that often anticipates actual bankruptcy. We combine one dataset from the Italian Central Credit Register of the Bank of Italy, one from balance sheet information related to Italian firms, and information from AnaCredit dataset, a novel source of credit data by European Central Bank. We try to go beyond the academic study and to show how our model, based on some promising machine learning algorithms, outperforms the current default predictions made by credit institutions. At the same time, we try to provide insights on the reasons that lead to a particular outcome. In fact, many modern approaches try to find well-performing models to forecast the default of a company; those models often act like a black-box and don’t give to financial institutions the fundamental explanations they need for their choices. This project aims to find a robust predictive model using a tree-based machine learning algorithm which flanked by a game-theoretic approach can provide sound explanations of the output of the model. Finally, we dedicated a special effort to the analysis of predictions in highly unbalanced contexts. Imbalanced classes are a common problem in machine learning classification that typically is addressed by removing the imbalance in the training set. We conjecture that it is not always the best choice and propose the use of a slightly unbalanced training set, showing that this approach contributes to maximize the performance

Generic Architecture for Predictive Computational Modelling with Application to Financial Data Analysis: Integration of Semantic Approach and Machine Learning

The PhD thesis introduces a Generic Architecture for Predictive Computational Modelling capable of automating analytical conclusions regarding quantitative data structured as a data frame. The model involves heterogeneous data mining based on a semantic approach, graph-based methods (ontology, knowledge graphs, graph databases) and advanced machine learning methods. The main focus of my research is data pre-processing aimed at a more efficient selection of input features to the computational model. Since the model I propose is generic, it can be applied for data mining of all quantitative datasets (containing two-dimensional, size-mutable, heterogeneous tabular data); however, it is best suitable for highly interconnected data. To adapt this generic model to a specific use case, an Ontology as the formal conceptual representation for the relevant domain knowledge is needed. I have determined to use financial/market data for my use cases. In the course of practical experiments, the effectiveness of the PCM model application for the UK companies’ financial risk analysis and the FTSE100 market index forecasting was evaluated. The tests confirmed that the PCM model has more accurate outcomes than stand-alone traditional machine learning methods. By critically evaluating this architecture, I proved its validity and suggested directions for future research

Corporate Credit Rating: A Survey

Corporate credit rating (CCR) plays a very important role in the process of contemporary economic and social development. How to use credit rating methods for enterprises has always been a problem worthy of discussion. Through reading and studying the relevant literature at home and abroad, this paper makes a systematic survey of CCR. This paper combs the context of the development of CCR methods from the three levels: statistical models, machine learning models and neural network models, summarizes the common databases of CCR, and deeply compares the advantages and disadvantages of the models. Finally, this paper summarizes the problems existing in the current research and prospects the future of CCR. Compared with the existing review of CCR, this paper expounds and analyzes the progress of neural network model in this field in recent years.Comment: 11 page

Application of an Artificial Neural Network as a Third-Party Database Auditing System

Data auditing is a fundamental challenge for organizations who deal with large databases. Databases are frequently targeted by attacks that grow in quantity and sophistication every day, and one-third of which are coming from users inside the organizations. Database auditing plays a vital role in protecting against these attacks. Native features in data base auditing systems monitor and capture activities and incidents that occur within a database and notify the database administrator. However, the cost of administration and performance overhead in the software must be considered. As opposed to using native auditing tools, the better solution for having a more secure database is to utilize third-party products. The primary goal of this thesis is to utilize an efficient and optimized deep learning approach to detect suspicious behaviors within a database by calculating the amount of risk that each user poses for the system. This will be accomplished by using an Artificial Neural Network as an enhanced feature of analyzer component of a database auditing system. This ANN will work as a third-party product for the database auditing system. The model has been validated in order to have a low bias and low variance. Moreover, parameter tuning technique has been utilized to find the best parameters that would result in the highest accuracy for the model

Advanced decision support systems for managers

Managers need to make timely decisions to solve the problems in the organization or to take advantage of the opportunities. But accessing the related information and knowledge is a necessity for the purpose of decision-making. Current transactional information systems in organizations easily manage the affairs of the organization, but they do not provide the necessary ability and intelligence to take a good decision and business performance management. Here, the importance of using such systems to support management decisions and business intelligence tools are highlighted. The purpose of this paper is to analyze the decision-making process, the structure, components, and features. Also a decision support system (DSS) is described. Then, the types of decision support systems will be defined and their abilities and functions will be expressed. The following performance management and business intelligence are explained. Finally, Intelligent Decision Support System will be examined and their functions and abilities to develop a decision support tool for decision-making will be analyzed

Advanced decision support systems for managers

Managers need to make timely decisions to solve the problems in the organization or to take advantage of the opportunities. But accessing the related information and knowledge is a necessity for the purpose of decision-making. Current transactional information systems in organizations easily manage the affairs of the organization, but they do not provide the necessary ability and intelligence to take a good decision and business performance management. Here, the importance of using such systems to support management decisions and business intelligence tools are highlighted. The purpose of this paper is to analyze the decision-making process, the structure, components, and features. Also a decision support system (DSS) is described. Then, the types of decision support systems will be defined and their abilities and functions will be expressed. The following performance management and business intelligence are explained. Finally, Intelligent Decision Support System will be examined and their functions and abilities to develop a decision support tool for decision-making will be analyzed