Ram S. Sriram

A Comparison of Selected Artificial Neural Networks that Help Auditors Evaluate Client Financial Viability

This study compares the performance of three artificial neural network (ANN) approaches—backpropagalion, categorical learning, and probabilistic neural network—as classification tools to assist and support auditors judgment about a clients continued financial viability into the future (going concern status). ANN performance is compared on the basis of overall error rates and estimated relative costs of misclassificaticn (incorrectly classifying an insolvent firm as solvent versus classifying a solvent firm as insolvent). When only the overall error rate is considered, the probabilistic neural network is the most reliable in classification, followed by backpropagation and categorical learning network. When the estimated relative costs of misclassification are considered, the categorical learning network is the least costly, followed by backpropagation and probabilistic neural network.

A comparison of the relative costs of financial distress models: artificial neural networks, logit and multivariate discriminant analysis

This study uses two artificial neural networks (ANNs), categorical learning/instar ANNs and probabilistic (PNN) ANNs, suitable for classification and prediction type issues, and compares them to traditional multivariate discriminant analysis (MDA) and logit to examine financial distress one to three years prior to failure. The results indicate that traditional MDA and logit perform best with the lowest overall error rates. However, when the relative error costs are considered, the ANNs perform better than traditional logit or MDA. Also, as the time period moves farther away from the eventual failure date, ANNs perform more accurately and with lower relative error costs than logit or MDA. This supports the conclusion that for auditors and other evaluators interested in early warning techniques, categorical learning network and probabilistic ANNs would be useful.

Improving Decision Effectiveness of Artificial Neural Networks: A Modified Genetic Algorithm Approach

This study proposes the use of a modified genetic algorithm (MGA), a global search technique, as a training method to improve generalizability and to identify relevant inputs in a neural network (NN) model. Generalizability refers to the NN models ability to perform well on exemplars (observations) that were not used during training (out-of-sample); improved generalizability enhances NNs acceptability as a valid decision-support tool. The MGA improves generalizability by setting unnecessary weights (or connections) to zero and by eliminating these weights. Because the eliminated weights have no further impact on the training (in-sample or out-of-sample data), the relevant variables can be identified from the model. By eliminating unnecessary weights, the MGA is able to search and find a parsimonious model that generalizes well. Unlike the traditional NN, the MGA identifies the model variables that contribute to an outcome, helping decision makers to rationalize output and accept results with greater confidence. The study uses real-life data to demonstrate the use of MGA.

EDI Adoption and Implementation: An Examination of Perceived Operational and Strategic Benefits, and Controls

In recent years, Electronic Data Interchange (EDI) has revolutionized the way in which businesses conduct their trading activities. Even though the popularity and potential attached to EDI is growing rapidly, knowledge regarding the nature of EDI benefits and EDI control practices is very limited. This paper reports the results of a survey of EDI users that explores these key implementation issues. This study focuses on organizational factors that are associated with EDI adoption and implementation. Findings indicate that organizations experience both operational and strategic benefits from EDI. Customer‐initiated EDI users recognized slightly greater EDI strategic benefits than did other users. Also, long‐time users recognized both strategic and operational benefits in greater proportions than did more recent users, and smaller firms more often cited better customer service and convenience (as strategic and operational benefits, respectively) from implementing EDI. An examination of control practices rev...

An Examination of the Effect of IT Investments on Firm Value: The Case of Y2K-Compliance Costs

In this study, using the recent Y2‐compliance expenditures as an example, we examine whether disclosures relating to investments in information technology (IT) were relevant to investors in assessing the market value of equity. We use a sample of 190 firms that disclosed estimates of total Y2K‐compliance costs in their 1997 annual reports to examine the association between Y2K‐compliance costs and share prices. We test the joint hypothesis that Y2K‐compliance costs were relevant to equity valuation of firms that chose to become Y2K‐compliant and that these costs were sufficiently reliable to be reflected in share prices. We find that estimates of Y2K‐compliance costs were positively and significantly related to share prices after controlling for earnings, book value of equity, and other factors. We find that the stock market is not shortsighted, and consider investments in Y2K‐remediation efforts a significant and value‐increasing activity for the average firm.

Minimizing Cost of Continuous Audit: Counting and Time Dependent Strategies

Why do we need to audit databases? The answer to this question depends on several factors, including the users and the applications that have accessed the data, the timing and the type of data modifications such as permissions or schema and so on. This paper examines certain strategies that have been suggested in the database auditing literature (see, e.g. Orman, 2001). Orman studied the counting, periodic and hybrid auditing strategies with the objective of minimizing the number of errors introduced during database access. Unlike Orman whose focus is on assessing the number of errors infilterating the system, we focus on the long run operating cost of running database audit. We use results from regenerative stochastic processes to derive expressions for the long run average cost under the counting and periodic auditing strategies. Future directions for research are also proposed.

The Value Relevance of IT Investments on Firm Value in the Financial Services Sector

Understanding and assessing the payoff from investments in IT is an important exercise for managers. A number of researchers have examined the elusive notion of firm-level information system effectiveness and the results are mixed. This study contributes to this debate by examining the association between market value of equity and IT-related investments for a sample of firms in the financial services sector. It should be noted that companies in the financial services industry are intensive users of IT and often rely upon IT as a source of competitive advantage. We find a positive association between investments in IT and market value. Overall, our findings support the notion that investors perceive investments in IT as value-relevant.

Strategic cost measurement for flexible manufacturing systems

Abstract The rise in global competition has induced manufacturing firms to adopt flexible manufacturing systems (FMS). Flexible automation brings both economic and non-economic advantages to a firm. These changes in manufacturing environments have shifted the emphasis on management reports and are creating information needs that cannot be fulfilled by traditional cost measurements and reports. Information reporting is now expected to focus more on influencing and supporting the long-term goals and strategies of firms. This paper discusses the impact of FMS and its implications in terms of information reporting, strategic cost analyses and control. We compare the traditional cost measurements and reporting with the changes that are taking place in firms that have adopted FMS. Our study shows that in firms where FMS is already in place, several changes are needed in cost and financial evaluation procedures and organization structures in order for planners and managers to perform effectively.

Board Size, Corporate Information Environment and Cost of Capital

Prior academic research has found a discount for equity holders but a premium for bondholders of firms with large boards. We argue that these results could have been impacted by the relation between board size and corporate information environment, which is absent in prior empirical analyses. In this study, we examine the impact of board size on both the equity holders and bondholders by analyzing how board size affects the information environment of a firm. Using a sample of S&P 1500 firms, our study finds that board size is positively associated with variables that proxy for information transparency. Further tests indicate that firms with larger boards pay lower weighted average cost of capital and that the discount is greater for firms that are less transparent. We find that firms with greater transparency do not benefit from larger boards. These results hold even when we use alternative measures of cost of capital. Overall, the results suggest that investors perceive larger boards as providing a more transparent information environment, which leads to a lower cost of capital for the firm.

Belief Networks for Expert System Development in Auditing

This study examines the use of a belief network based expert system for an auditing task—financial distress evaluation for banks. A belief network uses probability measures to store important dependencies across variables of interest in a problem domain, and makes inferences based on observed evidence using probability calculus. This paper discusses how belief network structures can be constructed, and used to assist auditors in making appropriate recommendations regarding the financial health of a bank under audit. The ability of a belief network to make reliable predictions depends on how well the network structure reflects the underlying dependencies across variables in the problem domain (e.g. financial ratios and the financial health of a bank). The first part of this study illustrates how a computer program developed by the authors can be used to generate and evaluate different feasible belief network structures based on historical data. The program uses an information-theoretic measure to compare the alternative structures. The ability of the program to identify existing dependencies across variables is demonstrated by using it to reconstruct a known network structure from simulated data. Next, the program is used on a database of twelve important bank financial ratios over a three-year period. The predictive ratios identified by the program reflect important areas of a banks health, such as loan quality, efficiency, profitability and capital adequacy. Finally, a belief revision mechanism is encoded for the belief network structure identified earlier, and is used to illustrate how it can assist auditors in making recommendations about financial health based on a banks critical financial ratios. The probability estimates provided by the system are validated using data on banks not used in the network design stage, and are found to be reliable.