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Procedia Computer Science 00 (2019) 000–000 Procedia Computer Science (2019) 000–000 Procedia Computer Science 14700 (2019) 400–406

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2018 International Conference on Identification, Information and Knowledge 2018 International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2018 in the Internet of Things, IIKI 2018

Stock Market Prediction Based on Generative Adversarial Network Stock Market Prediction Based on Generative Adversarial Network a a a Kang Zhangaa , Guoqiang Zhonga,∗ a,∗, Junyu Donga , Shengke Wanga , Yong Wanga Kang Zhang , Guoqiang Zhong , Junyu Dong , Shengke Wang , Yong Wang a Department of Computer Science and Technology, Ocean University of China, Qingdao, 266100, China of Computer Science and Technology, Ocean University of China, Qingdao, 266100, China

a Department

Abstract Abstract Deep learning has recently achieved great success in many areas due to its strong capacity in data process. For instance, it has been Deep recently achieved great success in prediction, many areasportfolio due to itsoptimization, strong capacity in datainformation process. Forprocessing instance, itand has trade been widelylearning used inhas financial areas such as stock market financial widely used in financial areas such as stock market prediction, portfolio optimization, financial information processing and trade execution strategies. Stock market prediction is one of the most popular and valuable area in finance. In this paper, we propose a execution strategies. market predictionNetwork is one of(GAN) the most popular and valuable area in finance. In the thisdiscriminator paper, we propose novel architecture of Stock Generative Adversarial with the Multi-Layer Perceptron (MLP) as and thea novel architecture Memory of Generative Adversarial Networkfor (GAN) with thethe Multi-Layer Perceptron (MLP) as the discriminator the Long Short-Term (LSTM) as the generator forecasting closing price of stocks. The generator is built by and LSTM Long Short-Term Memory (LSTM) as the generator for in forecasting the closing price data of stocks. generator is built by LSTM to mine the data distributions of stocks from given data stock market and generate in theThe same distributions, whereas the to mine the data distributions ofaims stocks given data andgenerated generate data. data in same the distributions, whereas the discriminator designed by MLP to from discriminate the in realstock stockmarket data and Wethe choose daily data on S&P 500 discriminator designed aims to discriminate the real andthe generated data. We choose the daily data on show S&P that 500 Index and several stocksbyinMLP a wide range of trading days and stock try to data predict daily closing price. Experimental results Index and GAN severalcan stocks a wide range of tradingindays and try to predict the daily Experimental show that our novel get ainpromising performance the closing price prediction on closing the realprice. data compared withresults other models in our novel GAN can get a promising performance in the closing price prediction on the real data compared with other models in machine learning and deep learning. machine learning and deep learning. c 2019  Authors. Published Published by by Elsevier Elsevier B.V. © 2019 The The Authors. B.V. c 2019  The Authors. Published by Elsevier B.V. This is an open access article under This is an open access article under the the CC CC BY-NC-ND BY-NC-ND license license (https://creativecommons.org/licenses/by-nc-nd/4.0/) (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility ofthe thescientific scientific committee the2018 2018 International Conference Identification, Information Peer-review under responsibility of committee ofofthe International Conference on on Identification, Information and Peer-review under responsibility the scientific committee of the 2018 International Conference on Identification, Information and Knowledge inInternet the Internet of of Things. Knowledge in the of Things. and Knowledge in the Internet of Things. Keywords: Deep Learning; Stock Prediction; Generative Adversarial Networks; Data Mining. Keywords: Deep Learning; Stock Prediction; Generative Adversarial Networks; Data Mining.

1. Introduction 1. Introduction The prediction of stock market returns is one of the most important and challenging issues in this domain. Many The prediction of stockin market returns one of mostmarket important and challenging issues in thisindomain. Many analyses and assumptions financial areaisshow thatthestock is predictable. Technical analysis stock investanalyses and assumptions in financial area show that stock market is predictable. Technical analysis in stock investment theory is an analysis methodology for forecasting the direction of prices through the research on past market ment theory