Artificial Neural Network Models for Pricing Initial Public Offerings

Jain, Bharat A.; Nag, Barin N.

doi:10.1111/j.1540-5915.1995.tb01430.x

Cited by 70 publications

(25 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Among the latter, we should mention the work of Jain and Nag [2]. These authors try to predict the post-issue market price using artificial neural networks.…”

Section: Introductionmentioning

confidence: 99%

Random Forest Prediction of IPO Underpricing

2017

View full text Add to dashboard Cite

Abstract:The prediction of initial returns on initial public offerings (IPOs) is a complex matter. The independent variables identified in the literature mix strong and weak predictors, their explanatory power is limited, and samples include a sizable number of outliers. In this context, we suggest that random forests are a potentially powerful tool. In this paper, we benchmark this algorithm against a set of eight classic machine learning algorithms. The results of this comparison show that random forests outperform the alternatives in terms of mean and median predictive accuracy. The technique also provided the second smallest error variance among the stochastic algorithms. The experimental work also supports the potential of random forests for two practical applications: IPO pricing and IPO trading.

show abstract

“…Among the latter, we should mention the work of Jain and Nag [2]. These authors try to predict the post-issue market price using artificial neural networks.…”

Section: Introductionmentioning

confidence: 99%

Random Forest Prediction of IPO Underpricing

2017

View full text Add to dashboard Cite

show abstract

“…Other than the seminal work of Jain and Nag [17] trying to predict first-day returns using Artificial Neural Networks, very few efforts have been done using artificial intelligence. We suggest new approach based on genetic algorithms that will be compared to the traditional instrument in terms of predictive performance.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary rule-based system for IPO underpricing prediction

Quintana

Luque

Isasi

2005

Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation

View full text Add to dashboard Cite

Academic literature has documented for a long time the existence of important price gains in the first trading day of initial public offerings (IPOs).Most of the empirical analysis that has been carried out to date to explain underpricing through the offering structure is based on multiple linear regression. The alternative that we suggest is a rule-based system defined by a genetic algorithm using a Michigan approach. The system offers significant advantages in two areas, 1) a higher predictive performance, and 2) robustness to outlier patterns. The importance of the latter should be emphasized since the non-trivial task of selecting the patterns to be excluded from the training sample severely affects the results.We compare the predictions provided by the algorithm to those obtained from linear models frequently used in the IPO literature. The predictions are based on seven classic variables. The results suggest that there is a clear correlation between the selected variables and the initial return, therefore making possible to predict, to a certain extent, the closing price.

show abstract

“…A review on existing literature reveals financial studies on a wide variety of subjects such as bankruptcy prediction (Tsukuda and Baba, 1994;Tam and Kiang, 1992), prediction of savings and loan association failures (Salchenberger et al, 1992), credit evaluation (Jensen, 1992;Collins et al, 1988), analysis of financial statements (Kryzanowski and Galler, 1995), auditing (Etheridge et al, 2000;Koh and Tan, 1999;Lenard et al, 1995), corporate distress diagnosis (Altman et al, 1994), bond rating (Dutta and Shekhar, 1988), initial stock pricing (Jain and Nag, 1996), currency exchange rate forecasting (Refenes, 1993), stock market analysis (Wong and Long, 1995) and forecasting in futures markets (Kaastra and Boyd, 1995;Trippi and DeSieno, 1992). Nevertheless, ANNs have not been applied to the estimation or prediction of any kinds of correlation, including that for foreign exchange.…”

Section: Neural Network In Financial Forecasting and Analysismentioning

confidence: 99%

Performance evaluation of neural network architectures: the case of predicting foreign exchange correlations

Chen

Leung

2005

J. Forecast.

View full text Add to dashboard Cite

In the last decade, neural networks have emerged from an esoteric instrument in academic research to a rather common tool assisting auditors, investors, portfolio managers and investment advisors in making critical financial decisions. It is apparent that a better understanding of the network's performance and limitations would help both researchers and practitioners in analysing real-world problems. Unlike many existing studies which focus on a single type of network architecture, this study evaluates and compares the performance of models based on two competing neural network architectures, the multi-layered feedforward neural network (MLFN) and general regression neural network (GRNN). Our empirical evaluation measures the network models' strength on the prediction of currency exchange correlation with respect to a variety of statistical tests including RMSE, MAE, U statistic, Theil's decomposition test, Henriksson-Merton market timing test and Fair-Shiller informational content test. Results of experiments suggest that the selection of proper architectural design may contribute directly to the success in neural network forecasting. In addition, market timing tests indicate that both MLFN and GRNN models have economically significant values in predicting the exchange rate correlation. On the other hand, informational content tests discover that the neural network models based on different architectures capture useful information not found in each other and the information sets captured by the two network designs are independent of one another. An auxiliary experiment is developed and confirms the possible synergetic effect from combining forecasts made by the two different network architectures and from incorporating information from an implied correlation model into the neural network forecasts. Implied correlation and random walk models are also included in our empirical experiment for benchmark comparison. Copyright © 2005 John Wiley & Sons, Ltd.

show abstract

Artificial Neural Network Models for Pricing Initial Public Offerings

Cited by 70 publications

References 26 publications

Random Forest Prediction of IPO Underpricing

Random Forest Prediction of IPO Underpricing

Evolutionary rule-based system for IPO underpricing prediction

Performance evaluation of neural network architectures: the case of predicting foreign exchange correlations

Contact Info

Product

Resources

About