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An Application Of An Artificial Neural Network Investment System To Predict Takeover Targets

Year: 2013

Type: article

Abstract: Artificial neural networks are a robust, effective complement to traditional statistical methods in financial applications. They can incorporate qualitative and quantitative information, and recognize underlying patterns and trends in large, complex data sets. This paper applies a neural network model to identify potential acquisition targets. The model incorporates various factors based on acquisition theories suggested in the literature. The resulting neural network model exhibits a highly successful prediction rate and a portfolio of predicted target stocks identified by the network substantially outperformed the market.

Source: Journal of Applied Business Research (JABR)

DOI: 10.19030/jabr.v15i4.8151

Authors: John J. Cheh, Randy S. Weinberg, Ken C. Yook

Institutions: University of Akron, Carnegie Mellon University, Johns Hopkins University

Language: English

Topic: Stock Market Forecasting Methods

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: bronze

OpenAlex ID: W2177596475

FWCI: 4.554

Cites:

Cited by: 35

Related to: 10