Title: A new method for predicting stock market crashes using classification and artificial neural networks

Authors: Saeed Tabar; Sushil Sharma; David Volkman

Addresses: Miller College of Business, Ball State University, Muncie, IN, USA ' Miller College of Business, Ball State University, Muncie, IN, USA ' College of Business Administration, University of Nebraska, Omaha, USA

Abstract: The stock market prediction is an interesting topic, especially for traders and investors. One important aspect of predicting the stock market is identifying price patterns which may result in a market crash. With the advancement of computer technology, particularly in the area of artificial intelligence, a large number of new models have been proposed. The proposed method in this article is based on identifying the normal behaviour of a crowd in the stock market using exponential moving average and then classifying the price fluctuations into three categories BUY, SELL, and STOP. An artificial neural network (ANN) with five input neurons, ten hidden neurons, and three output neurons is then used to learn from the price fluctuations and predict one day ahead. The final results show that the algorithm is capable of identifying the market crashes in advance by issuing STOP labels.

Keywords: artificial neural networks; ANNs; classification; stock market; market prediction; market crash.

DOI: 10.1504/IJBDA.2020.108697

International Journal of Business and Data Analytics, 2020 Vol.1 No.3, pp.203 - 217

Received: 27 Mar 2019
Accepted: 22 May 2019
Published online: 27 Jul 2020 *

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