Application of Artificial Neural Systems as an Event Study Methodology: An Empirical Investigation (2000)

INTRODUCTION Event studies that test market efficiency have their roots in Fama, Fisher, Jensen and Roll (1969), which investigated stock splits to see whether the stock market was efficient with respect to the split. This study involves a comparison of traditional event study methodologies and the application of Artificial Neural System (ANS) technology as a novel approach to event study methodologies. It is hypothesized that ANS can, not only be useful as an event study methodology, and that they can be superior in some situations to these traditional models. The study is organized in the following order: next section discusses the literature review, followed by the various research methodologies and theoretical frameworks, the data bases employed, testable hypotheses, and the sample selection process. It is followed by statistical results and a discussion of the empirical findings and problems encountered in the study. Finally, the conclusions are presented. LITERATURE REVIEW Even though event studies in finance are common, no previous attempt has been made in the literature to use ANS as an event study methodology. A thorough literature search indicated that although ANS was beginning to appear in finance more frequently in the early nineties, but its application in the finance is still very limited. For instance, Salchenberger, Cinar, and Lash (1992) used ANS to predict the probability of savings and loans failure; Huang, Dorsey, and Boose (1992) used ANS to predict insolvency of life insurers. They compared ANS to discriminant analysis, k-nearest neighbor, and logit. The neural network was shown to produce superior estimates to the alternative methods. Bell, Ribar, and Verchio (1990) using ANS to predict the commercial bank failures, found that ANS performed better than logistic regression particularly in the grey area where the failing banks appeared to be less financially distressed. Coats and Franklin (1993) used ANS and the multiple discriminant analysis for predicting financial distress. The test results showed that the ANS approach is more effective than discriminant analysis for pattern recognition and early detection of financial distress (at 80% of the time over an effective lead time of up to four years). Singleton and Surkan (1991) also compared the performance of ANS models to that of multiple discriminant analysis and Probit, and found that ANS model could classify bonds more accurately than other techniques. In 1998, Luther compared the performance of ANS to a Logit model for predicting the outcome of bankruptcy based on the firm's financial ratios at the time of filing for chapter 11. She found that not only ANS can be used in the prediction of outcome of bankruptcy, but ANS model had a significantly higher prediction accuracy than the Logit model and was more robust than the Logit model. Kuo and Reitsch (1996) compared the accuracy of ANS with other traditional forecasting models and concluded that ANS is superior to some of the traditional models. They also concluded that since ANS does not require any assumptions about underlying population distributions, they are very useful where inputs are highly correlated, missing, or the systems are nonlinear. Green (1998) studied the use of ANS as a tool to monitor individual credit card usage patterns, and thus help reduce credit card fraud. He concluded that in order for ANS to be effective in reducing credit card fraud, there must be a constant transaction monitoring, which many firms are reluctant to implement due to its high cost. In 1997, Nam, Yi, and Prybutok compared ANS with regression and exponential smoothing to predict airline passenger volume. Using monthly data from January 1982 to December 1993, they found ANS to perform better in some instances. STATISTICAL METHODOLOGY AND RESEARCH DESIGN Investigation of security returns behavior contingent on specific events has been featured prominently in the literature for decades. …