Modelling small‐business credit scoring by using logistic regression,neural networks and decision trees

Previous research on credit scoring that used statistical and intelligent methods was mostly focused on commercial and consumer lending. The main purpose of this paper is to extract important features for credit scoring in small‐business lending on a dataset with specific transitional economic conditions using a relatively small dataset. To do this, we compare the accuracy of the best models extracted by different methodologies, such as logistic regression, neural networks (NNs), and CART decision trees. Four different NN algorithms are tested, including backpropagation, radial basis function network, probabilistic and learning vector quantization, by using the forward nonlinear variable selection strategy. Although the test of differences in proportion and McNemar's test do not show a statistically significant difference in the models tested, the probabilistic NN model produces the highest hit rate and the lowest type I error. According to the measures of association, the best NN model also shows the highest degree of association with the data, and it yields the lowest total relative cost of misclassification for all scenarios examined. The best model extracts a set of important features for small‐business credit scoring for the observed sample, emphasizing credit programme characteristics, as well as entrepreneur's personal and business characteristics as the most important ones. Copyright © 2005 John Wiley & Sons, Ltd.     

Risk assessment in internal auditing: a neural network approach

Risk assessment is a systematic process for integrating professional judgments about relevant risk factors, their relative significance and probable adverse conditions and/or events leading to identification of auditable activities (IIA, 1995, SIAS No. 9). Internal auditors utilize risk measures to allocate critical audit resources to compliance, operational, or financial activities within the organization (Colbert, 1995). In information rich environments, risk assessment involves recognizing patterns in the data, such as complex data anomalies and discrepancies, that perhaps conceal one or more error or hazard conditions (e.g. Coakley and Brown, 1996; Bedard and Biggs, 1991; Libby, 1985). This research investigates whether neural networks can help enhance auditors’ risk assessments. Neural networks, an emerging artificial intelligence technology, are a powerful non‐linear optimization and pattern recognition tool (Haykin, 1994; Bishop, 1995). Several successful, real‐world business neural network application decision aids have already been built (Burger and Traver, 1996). Neural network modeling may prove invaluable in directing internal auditor attention to those aspects of financial, operating, and compliance data most informative of high‐risk audit areas, thus enhancing audit efficiency and effectiveness. This paper defines risk in an internal auditing context, describes contemporary approaches to performing risk assessments, provides an overview of the backpropagation neural network architecture, outlines the methodology adopted for conducting this research project including a Delphi study and comparison with statistical approaches, and presents preliminary results, which indicate that internal auditors could benefit from using neural network technology for assessing risk. Copyright © 1999 John Wiley & Sons, Ltd.     

Deep networks for predicting direction of change in foreign exchange rates

Trillions of dollars are traded daily on the foreign exchange (forex) market, making it the largest financial market in the world. Accurate forecasting of forex rates is a necessary element in any effective hedging or speculation strategy in the forex market. Time series models and shallow neural networks provide acceptable point estimates for future rates but are poor at predicting the direction of change and, hence, are not very useful for supporting profitable trading strategies. Machine learning classifiers trained on input features crafted based on domain knowledge produce marginally better results. The recent success of deep networks is partially attributable to their ability to learn abstract features from raw data. This motivates us to investigate the ability of deep convolution neural networks to predict the direction of change in forex rates. Exchange rates for the currency pairs EUR/USD, GBP/USD and JPY/USD are used in experiments. Results demonstrate that trained deep networks achieve satisfactory out‐of‐sample prediction accuracy.     

Stock Price Prediction Using Prior Knowledge and Neural Networks

In this paper we investigate ways to use prior knowledge and neural networks to improve multivariate prediction ability. Daily stock prices are predicted as a complicated real-world problem, taking non-numerical factors such as political and international events are into account. We have studied types of prior knowledge which are difficult to insert into initial network structures or to represent in the form of error measurements. We make use of prior knowledge of stock price predictions and newspaper information on domestic and foreign events. Event-knowledge is extracted from newspaper headlines according to prior knowledge. We choose several economic indicators, also according to prior knowledge, and input them together with event-knowledge into neural networks. The use of event-knowledge and neural networks is shown to be effective experimentally: the prediction error of our approach is smaller than that of multiple regression analysis on the 5% level of significance. © 1997 by John Wiley & Sons, Ltd.     

Predicting mispricing of initial public offerings

This article investigates the ability of neural network models to predict mispricing of initial public offerings (IPOs). The aim is to improve the modest explanatory power of existing models that are based on the theory of asymmetrically informed economic agents surrounding post‐issue market value of IPOs. This study develops and compares linear regression and neural network models. The results show that modelling variable interactions and non‐linearity allows a potentially fruitful approach for stagging in IPOs. Neural networks have been criticized for being a black box; however, this paper shows that, by using sensitivity analysis, neural networks can provide a reasonable explanation of their predictive behaviour and direction of association between variables. Copyright © 2005 John Wiley & Sons, Ltd.     

Supply of bank loans and business debts: A view from historical bankruptcy cases

As bank loans fell in the 2008 crisis, business bankruptcy increased. To study how bank loans affect business balance sheets and bankruptcy, we use new data on bankrupt businesses in Missouri between 1898 and 1942. We confirm that when banks curtail loans, courts see more bankruptcies among businesses with high exposure to bank debt. To reduce real volatility, policy‐makers can set tough bank liquidity requirements in the upswing of business cycle but allow weaker requirements in the downswing. We also find that between 1914 and 1933, businesses in St. Louis were more sensitive to changes in bank loans than businesses in Kansas City, probably due to the tight monetary policy conducted by the conservative St. Louis Fed. The Glass‐Steagall Act weakened the relationship between bank loans and business debt structure. The takeaway is that lender‐of‐last‐resort practices stabilize both the financial sector and the real economy.     

Effectiveness of Stochastic Neural Network for Prediction of Fall or Rise of TOPIX

Stochastic neural network is a hierarchical network of stochastic neurons which emit 0 or 1 with the probability determined by the values of inputs. We have developed an efficient training algorithm so as to maximize the likelihood of such a neural network. This algorithm enables us to apply the stochastic neural network to a practical problem like prediction of fall or rise of Tokyo Stock Price Index (TOPIX). We trained it with the data from 1994 to 1996 and predicted the fall or rise of 1 day ahead of TOPIX for the period from 1997 to 2000. The result is quite promising. The accuracy of the prediction of the stochastic network is the 60.28%, although those of non-stochastic neural network, autoregressive model and GARCH model are 50.02, 51.38 and 57.21%, respectively. However, the stochastic neural network is not so advantageous over other networks or models for prediction of the TOPIX used for training. This means that the stochastic neural network is less over fitting to the training data than others, and results in the best prediction. We will demonstrate how the stochastic neural network learns well non-linear structure behind of the data in comparison to other models or networks, including Generalized Linear model (GLM).JEL codes: D24, L60, 047     

我国车险业务经营绩效评价研究——以某财险公司湖南分公司为例

财产保险公司绝大部分业务来自于车险业务,因而车险业务的绩效关系到财险公司的经营业绩,甚至整个保险业的稳定和健康发展。从承保、理赔、续保及财务四个方面选取17个具体指标构建车险业务绩效评价指标体系进行有效性检验,计算某财险公司湖南分公司车险业务绩效水平指数,评价其车险业务经营状况。结果显示：该公司财务指标对车险业务影响的权重最大,综合车险指数呈上升趋势,表现出较好的经营状况。     

A two‐step system for direct bank telemarketing outcome classification

A two‐step system is presented to improve prediction of telemarketing outcomes and to help the marketing management team effectively manage customer relationships in the banking industry. In the first step, several neural networks are trained with different categories of information to make initial predictions. In the second step, all initial predictions are combined by a single neural network to make a final prediction. Particle swarm optimization is employed to optimize the initial weights of each neural network in the ensemble system. Empirical results indicate that the two‐step system presented performs better than all its individual components. In addition, the two‐step system outperforms a baseline one where all categories of marketing information are used to train a single neural network. As a neural networks ensemble model, the proposed two‐step system is robust to noisy and nonlinear data, easy to interpret, suitable for large and heterogeneous marketing databases, fast and easy to implement.     

为金融企业打造诺亚方舟的灾备系统建设

随着金融行业风险管理意识的不断提升,灾难备份也从最初的IT层面上升到企业的业务连续性管理。信息技术的日新月异,给金融行业的灾难备份带来了机遇和挑战。该文介绍了当前我国灾难备份的政策框架与金融企业灾备建设的现状,并展望了金融企业灾备技术革新和灾备建设的发展趋势。     

Learning multi-market microstructure from order book data

In this paper, we investigate market behaviors at high-frequency using neural networks trained with order book data. Experiments are done intensively with 110 asset pairs covering 97% of spot-futures pairs in the Korea Exchange. An efficient training scheme that improves the performance and training stability is suggested, and using the proposed scheme, the lead–lag relationship between spot and futures markets are measured by comparing the performance gains of each market data set for predicting the other. In addition, the gradients of the trained model are analyzed to understand some important market features that neural networks learn through training, revealing characteristics of the market microstructure. Our results show that highly complex neural network models can successfully learn market features such as order imbalance, spread-volatility correlation, and mean reversion.     

A Neural Network Approach for Analyzing Small Business Lending Decisions

In this paper, we apply the neural network method to small business lending decisions. We use the neural network to classify the loan applications into the groups of acceptance or rejection, and compare the model results with the actual decisions made by loan officers. Data were collected from a leading bank in Central New York. The sample contains important financial statement and business information of borrowers and the loan officers' decisions. We conduct the network training on the data sample and find that the neural network has a stronger discriminating power for classifying the acceptance and rejection groups than traditional parametric and nonparametric classifiers. The results show that the neural network model has a high predictive ability. Our findings suggest that neural networks can be a very useful tool for enhancing small-business lending decisions and reducing loan processing time and costs.     

Off-site monitoring systems for predicting bank underperformance: a comparison of neural networks,discriminant analysis,and professional human judgment

This study compares the ability of discriminant analysis, neural networks, and professional human judgment methodologies in predicting commercial bank underperformance. Experience from the banking crisis of the 1980s and early 1990s suggest that improved prediction models are needed for helping prevent bank failures and promoting economic stability. Our research seeks to address this issue by exploring new prediction model techniques and comparing them to existing approaches. When comparing the predictive ability of all three models, the neural network model shows slightly better predictive ability than that of the regulators. Both the neural network model and regulators significantly outperform the benchmark discriminant analysis model's accuracy. These findings suggest that neural networks show promise as an off-site surveillance methodology. Factoring in the relative costs of the different types of misclassifications from each model also indicates that neural network models are better predictors, particularly when weighting Type I errors more heavily. Further research with neural networks in this field should yield workable models that greatly enhance the ability of regulators and bankers to identify and address weaknesses in banks before they approach failure. Copyright © 2001 John Wiley & Sons, Ltd.     

The Adequacy of Pre‐purchase Due Diligence in Independent Small Business and Franchising

This research reports an in‐depth study of the due diligence activities that prospective independent small business operators and franchisees in Australia undertake prior to purchasing or starting up their businesses. Although academic literature and industry publications promote undertaking ‘proper due diligence’, there is a lack of empirical research into the nature of due diligence and its effect on business outcomes. Using a qualitative approach, 60 currently and formerly operating independents and franchisees were personally interviewed, exploring the diversity of approaches to undertaking due diligence prior to entering business. The research revealed that differences occur in both the type and amount of due diligence undertaken by independents and franchisees and highlighted further differences between current and former operators. In general, the due diligence undertaken by participants was relatively unsophisticated with few exceptions of rigour and planning. Where prospective independents and franchisees were entering business for the first time, their appreciation of business was naïve. A steep learning curve followed during which they often recognised flaws in their initial research. As a result of this qualitative in‐depth research, we present a set of propositions regarding due diligence and a model for future testing on a large sample.     

Bankruptcy prediction of financially stressed firms: an examination of the predictive accuracy of artificial neural networks

This is an extension of prior studies that have used artificial neural networks to predict bankruptcy. The incremental contribution of this study is threefold. First, we use only financially stressed firms in our control sample. This enables the models to more closely approximate the actual decision processes of auditors and other interested parties. Second, we develop a more parsimonious model using qualitative ‘bad news’ variables that prior research indicates measure financial distress. Past research has focused on the ‘usefulness’ of accounting numbers and therefore often ignored non‐accounting variables that may contribute to the classification accuracy of the distress prediction models. In addition, rather than use multiple financial ratios, we include a single variable of financial distress using the Zmijewski distress score that incorporates ratios measuring profitability, liquidity, and solvency. Finally, we develop and test a genetic algorithm neural network model. We examine its predictive ability to that of a backpropagation neural network and a model using multiple discriminant analysis. The results indicate that the misclassification cost of the genetic algorithm‐based neural network was the lowest among the models. Copyright © 2001 John Wiley & Sons, Ltd.     

BUILDING AND EVOLVING DATA WAREHOUSING AND BUSINESS INTELLIGENCE ARTEFACTS: THE CASE OF SYSCO

This paper investigates use of data warehouse and business intelligence capabilities to integrate with customers in the supply chain and improve insights into customer sales. By making internal data warehouse sales information available to customers, additional value to those customers is created, eliminating asymmetries of information in the supply chain. In addition, the evolution of data warehousing into business intelligence is investigated, expanding sales information to include marketing associate performance analysis generated for internal use. Further, a methodology that was used for building a business intelligence system is also examined. Finally, what appears to be a business‐intelligence‐driven focus on enterprise resource planning systems is analyzed. These issues are illustrated using real‐world data warehousing and business intelligence artefacts developed at SYSCO. Copyright © 2012 John Wiley & Sons, Ltd.     

A neural network approach to the prediction of going concern status

The assessment of a firm's going concern status is not an easy task. To assist auditors, going concern prediction models based on statistical methods such as multiple discriminant analysis and logit/probit analysis have been explored with some success. This study attempts to look at a different and more recent approach—neural networks. In particular, a neural network model of the feedforward, backpropagation type was constructed to predict a firm's going concern status from six financial ratios, using a data set containing 165 non-going concerns and 165 matched going concerns. On an evenly distributed hold-out sample, the trained network model correctly predicted all 30 test cases. The results suggest that neural networks can be a promising avenue of research and application in the going concern area.     

CAP Forum on E‐Business: E‐Commerce and Tax Planning: Canadian Experiences*

This paper explores the deployment of e‐commerce by Canadian firms in the global marketplace, with an emphasis on the implications of e‐commerce for tax planning. The business press and various government task forces have discussed challenges raised by e‐commerce for traditional “source‐based” tax systems; however, these discussions have presented little evidence of firms' reliance on e‐commerce for tax‐planning purposes. Similarly, academic research has seldom examined whether firms' decisions to implement e‐commerce are by tax‐planning considerations. It is thus largely unknown whether firms actively consider taxation issues when evaluating e‐commerce, how the factors that have been identified as influencing decisions to implement e‐commerce systems are balanced against tax‐planning considerations, and what barriers might exist in practice to using e‐commerce for tax planning. We choose a qualitative interview‐based approach to explore these issues. Our findings suggest that tax planning is not considered by most of our respondent companies in their decisions to deploy e‐commerce. The companies we interviewed tended to implement e‐commerce over several years, starting with back‐office technologies like enterprise resource planning (ERP) systems. Accordingly, the ability to perform online sales transactions, which is a key component of using e‐commerce for tax planning, often was not yet in place. One implication of these results is that if concerns over tax revenue losses are realistic, tax policymakers may have some time to refine tax legislation to address the challenges raised by e‐commerce.     

Sustainability as Adaptability

Most corporations now view sustainability as a key requirement for competitive advantage, but few claim to have achieved it. One of the key obstacles separating intention from execution is that the sustainability frameworks employed by companies tend to be insufficiently clear, precise, or comprehensive to guide decision making. One of the most pressing challenges for corporate leaders today is, of course, to sustain the economic viability of the core businesses. But given the implicit “beyond business” focus of most sustainability efforts, corporate executives would be better served by a more integrated, holistic framework—one that enables them to make tradeoffs among the economic, social, and ecological aspects of business. This article introduces such a framework—one that redefines sustainability as the ability of companies to adapt to change in three different spheres of operation—ecological, social, and economic—with a near‐term as well as a longer‐term planning horizon. Without such adaptation, business models become obsolete for reasons that can range from economic failure, to competitive inferiority, to social or ecological limits. This ability to adapt can be measured and valued by using the BCG Adaptive Advantage Index, a composite measure of corporate performance during market downturns. The BCG analysis also shows that although the most adaptive companies tend to report lower profits and have lower values during periods of relative stability, such companies perform consistently better over full cycles. Creating social and ecological value alone doesn't automatically confer economic rewards, but—with the right business model and capabilities—it can. The authors explore some of the business model archetypes that successfully achieve this “co‐optimization.”     

Predicting Corporate Failure Using a Neural Network Approach

This paper investigates the performance of Artificial Neural Networks for the classification and subsequent prediction of business entities into failed and non-failed classes. Two techniques, back-propagation and Optimal Estimation Theory (OET), are used to train the neural networks to predict bankruptcy filings. The data are drawn from Compustat data tapes representing a cross-section of industries. The results obtained with the neural networks are compared with other well-known bankruptcy prediction techniques such as discriminant analysis, probit and logit, as well as against benchmarks provided by directly applying the bankruptcy prediction models developed by Altman (1968) and Ohlson (1980) to our data set. We control the degree of ‘disproportionate sampling’ by creating ‘training’ and ‘testing’ populations with proportions of bankrupt firms ranging from 1% to 50%. For each population, we apply each technique 50 times to determine stable accuracy rates in terms of Type I, Type II and Total Error. We show that the performance of various classification techniques, in terms of their classification errors, depends on the proportions of bankrupt firms in the training and testing data sets, the variables used in the models, and assumptions about the relative costs of Type I and Type II errors. The neural network solutions do not achieve the ‘magical’ results that literature in this field often promises, although there are notable 'pockets' of superior performance by the neural networks, depending on particular combinations of proportions of bankrupt firms in training and testing data sets and assumptions about the relative costs of Type I and Type II errors. However, since we tested only one architecture for the neural network, it will be necessary to investigate potential improvements in neural network performance through systematic changes in neural network architecture.