Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/80561
Title: An interpretable neural fuzzy inference system for predictions of underpricing in initial public offerings
Authors: Qian, Xiaolin
Quek, Chai
Miao, Chunyan
Wang, Di
Zhang, Xiaofeng
Ng, Geok See
Zhou, You
Tan, Ah-Hwee
Keywords: DRNTU::Engineering::Computer science and engineering
Neural Fuzzy Inference System
Interpretable Rules
Issue Date: 2018
Source: Wang, D., Qian, X., Quek, C., Tan, A. H., Miao, C., Zhang, X., ... Zhou, Y. (2018). An interpretable neural fuzzy inference system for predictions of underpricing in initial public offerings. Neurocomputing, 319102-117. doi:10.1016/j.neucom.2018.07.036
Series/Report no.: Neurocomputing
Abstract: Due to their aptitude in both accurate data processing and human comprehensible reasoning, neural fuzzy inference systems have been widely adopted in various application domains as decision support systems. Especially in real-world scenarios such as decision making in financial transactions, the human experts may be more interested in knowing the comprehensive reasons of certain advices provided by a decision support system in addition to how confident the system is on such advices. In this paper, we apply an integrated autonomous computational model termed genetic algorithm and rough set incorporated neural fuzzy inference system (GARSINFIS) to predict underpricing in initial public offerings (IPOs). The difference between a stock’s potentially high value and its actual IPO price is referred as money-left-on-the-table, which has been extensively studied in the literature of corporate finance on its theoretical foundations, but surprisingly under-investigated in the field of computational decision support systems. Specifically, we use GARSINFIS to derive interpretable rules in determining whether there is money-left-on-the-table in IPOs to assist the investors in their decision making. For performance evaluations, we first demonstrate how to balance between accuracy and interpretability in GARSINFIS by simply altering the values of several coefficient parameters using well-known datasets. We then use GARSINFIS to investigate the IPO underpricing problem. The encouraging experimental results show that we may yield higher initial returns of IPOs by following the advices provided by GARSINFIS than any other benchmarking model. Therefore, our autonomous computational model is shown to be capable of offering the investors highly interpretable and reliable decision supports to grab the money-left-on-the-table in IPOs.
URI: https://hdl.handle.net/10356/80561
http://hdl.handle.net/10220/46696
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2018.07.036
Rights: © 2018 Elsevier. This is the author created version of a work that has been peer reviewed and accepted for publication by Neurocomputing, Elsevier. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [https://doi.org/10.1016/j.neucom.2018.07.036].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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